commit b44d0668f7f9ea6698ac1db9d1aac21c864bc39e Author: Le Ma <24167796@qq.com> Date: Tue Dec 3 11:34:51 2024 +1300 Initial commit diff --git a/01split.py b/01split.py new file mode 100644 index 0000000..fed278c --- /dev/null +++ b/01split.py @@ -0,0 +1,133 @@ +import cv2 +import numpy as np +import os + +# 读取输入图像 +image = cv2.imread('input.jpg') + +# 检查图像是否读取成功 +if image is None: + print("无法读取 'input.jpg'，请确保文件存在且路径正确。") + exit() + +# 获取图像的尺寸 +height, width = image.shape[:2] + +# 创建九个掩码，初始化为全黑 +masks = [np.zeros((height, width), dtype=np.uint8) for _ in range(9)] + +# 定义曲线参数，增加弯曲程度 +amplitude_h = height / 15 # 横向曲线的最大幅度 +amplitude_v = width / 15 # 纵向曲线的最大幅度 + +# 定义 x 和 y 坐标 +x = np.arange(width) +y = np.arange(height) + +# 定义横向曲线（沿着 x 轴变化），在每个区域的中间进行一次弯曲 +y_h1 = np.zeros_like(x, dtype=float) +y_h2 = np.zeros_like(x, dtype=float) + +# 定义横向曲线的三个段 +segment_width = width / 3 + +for i in range(3): + start_x = i * segment_width + end_x = (i + 1) * segment_width + center_x = (start_x + end_x) / 2 + + mask = (x >= start_x) & (x < end_x) + x_relative = x[mask] - center_x + amplitude_modifier = np.cos(np.pi * x_relative / (end_x - start_x)) ** 2 + + y_h1[mask] = (height / 3) + amplitude_h * amplitude_modifier * np.sin(np.pi * x_relative / (end_x - start_x)) + y_h2[mask] = (2 * height / 3) + amplitude_h * amplitude_modifier * np.sin(np.pi * x_relative / (end_x - start_x)) + +# 确保 y 值在图像范围内 +y_h1 = np.clip(y_h1, 0, height - 1).astype(int) +y_h2 = np.clip(y_h2, 0, height - 1).astype(int) + +# 定义纵向曲线的三个段 +segment_height = height / 3 + +x_v1 = np.zeros_like(y, dtype=float) +x_v2 = np.zeros_like(y, dtype=float) + +for i in range(3): + start_y = i * segment_height + end_y = (i + 1) * segment_height + center_y = (start_y + end_y) / 2 + + mask = (y >= start_y) & (y < end_y) + y_relative = y[mask] - center_y + amplitude_modifier = np.cos(np.pi * y_relative / (end_y - start_y)) ** 2 + + x_v1[mask] = (width / 3) + amplitude_v * amplitude_modifier * np.sin(np.pi * y_relative / (end_y - start_y)) + x_v2[mask] = (2 * width / 3) + amplitude_v * amplitude_modifier * np.sin(np.pi * y_relative / (end_y - start_y)) + +# 确保 x 值在图像范围内 +x_v1 = np.clip(x_v1, 0, width - 1).astype(int) +x_v2 = np.clip(x_v2, 0, width - 1).astype(int) + +# 创建网格 +Y, X = np.meshgrid(np.arange(height), np.arange(width), indexing='ij') + +# 获取对应的曲线值 +y_curve1 = y_h1[X] +y_curve2 = y_h2[X] +x_curve1 = x_v1[Y] +x_curve2 = x_v2[Y] + +# 计算区域索引 +region = np.zeros((height, width), dtype=int) + +# 定义区域编号如下： +# 0 | 1 | 2 +# 3 | 4 | 5 +# 6 | 7 | 8 + +# 上区域 +upper = Y <= y_curve1 +middle = (Y > y_curve1) & (Y <= y_curve2) +lower = Y > y_curve2 + +# 左、中、右区域 +left = X <= x_curve1 +center = (X > x_curve1) & (X <= x_curve2) +right = X > x_curve2 + +# 分配区域 +region[upper & left] = 0 +region[upper & center] = 1 +region[upper & right] = 2 +region[middle & left] = 3 +region[middle & center] = 4 +region[middle & right] = 5 +region[lower & left] = 6 +region[lower & center] = 7 +region[lower & right] = 8 + +# 创建掩码 +for idx in range(9): + masks[idx][region == idx] = 255 + +# 确保输出目录存在 +output_dir = 'output' +if not os.path.exists(output_dir): + os.makedirs(output_dir) + +# 应用掩码并保存带透明度的图片 +for idx, mask in enumerate(masks): + # 提取 RGB 部分 + img_part = cv2.bitwise_and(image, image, mask=mask) + + # 转换为 BGRA + img_part = cv2.cvtColor(img_part, cv2.COLOR_BGR2BGRA) + + # 设置 alpha 通道 + img_part[:, :, 3] = mask + + # 保存为 PNG 格式以保留透明度 + cv2.imwrite(os.path.join(output_dir, f'output{idx + 1}.png'), img_part) + +print("图片已成功切割成9张，并保存到 'output' 目录下，带有透明背景。") diff --git a/02rotate.py b/02rotate.py new file mode 100644 index 0000000..2aaa00d --- /dev/null +++ b/02rotate.py @@ -0,0 +1,27 @@ +import os +import random +from PIL import Image + + +def rotate_images_random_angle(input_folder, output_folder): + if not os.path.exists(output_folder): + os.makedirs(output_folder) + + for filename in os.listdir(input_folder): + if filename.lower().endswith('.png'): + input_path = os.path.join(input_folder, filename) + output_path = os.path.join(output_folder, filename) + angle = random.uniform(0, 360) + try: + with Image.open(input_path) as img: + rotated_img = img.rotate(angle, expand=True) + rotated_img.save(output_path) + print(f"Rotated {filename} by {angle:.2f} degrees.") + except Exception as e: + print(f"Error processing {filename}: {e}") + + +if __name__ == "__main__": + input_folder = 'output' + output_folder = 'output2' + rotate_images_random_angle(input_folder, output_folder) diff --git a/03combine.py b/03combine.py new file mode 100644 index 0000000..ec1d4cd --- /dev/null +++ b/03combine.py @@ -0,0 +1,50 @@ +import os +from math import ceil, sqrt +from PIL import Image +import random + +# 设置图片文件夹路径 +folder_path = 'output2' +output_folder = 'output3' +output_path = 'output3/merged_image.png' + +if not os.path.exists(output_folder): + os.makedirs(output_folder) + +# 获取所有 PNG 文件 +images = [os.path.join(folder_path, f) for f in os.listdir(folder_path) if f.lower().endswith('.png')] +if not images: + print("没有找到 PNG 图片。") + exit() + +random.shuffle(images) + +num_images = len(images) +# 计算行数和列数，使得接近正方形 +cols = ceil(sqrt(num_images)) +rows = ceil(num_images / cols) + +# 打开所有图片并获取尺寸 +opened_images = [Image.open(img) for img in images] +widths, heights = zip(*(img.size for img in opened_images)) + +# 假设所有图片尺寸相同，如果不同，可以选择调整大小 +max_width = max(widths) +max_height = max(heights) + +# 创建一个新的空白图片 +merged_width = cols * max_width +merged_height = rows * max_height +merged_image = Image.new('RGBA', (merged_width, merged_height), (255, 255, 255, 0)) + +# 将每张图片粘贴到合适的位置 +for index, img in enumerate(opened_images): + row = index // cols + col = index % cols + x = col * max_width + y = row * max_height + merged_image.paste(img, (x, y)) + +# 保存合并后的图片 +merged_image.save(output_path) +print(f"图片已成功合并并保存为 {output_path}") diff --git a/03rectangle_dir.py b/03rectangle_dir.py new file mode 100644 index 0000000..483d286 --- /dev/null +++ b/03rectangle_dir.py @@ -0,0 +1,84 @@ +import os +import cv2 +import numpy as np + + +def process_png(file_path, output_folder): + image = cv2.imread(file_path, cv2.IMREAD_UNCHANGED) + if image is None: + print(f"无法读取图像：{file_path}") + return + + if image.shape[2] < 4: + print(f"图像没有 alpha 通道：{file_path}") + return + + alpha = image[:, :, 3] + _, thresh = cv2.threshold(alpha, 0, 255, cv2.THRESH_BINARY) + contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + if not contours: + print(f"图像中没有非透明部分：{file_path}") + return + + cnt = max(contours, key=cv2.contourArea) + rect = cv2.minAreaRect(cnt) + box = cv2.boxPoints(rect) + box = np.int32(box) + + # 提取最小矩形内的图像并旋转 + width = int(rect[1][0]) + height = int(rect[1][1]) + + if width == 0 or height == 0: + print(f"最小矩形的宽度或高度为零：{file_path}") + return + + src_pts = box.astype("float32") + # 定义目标点为水平矩形 + dst_pts = np.array([ + [0, height - 1], + [0, 0], + [width - 1, 0], + [width - 1, height - 1] + ], dtype="float32") + + # 计算透视变换矩阵 + M = cv2.getPerspectiveTransform(src_pts, dst_pts) + warped = cv2.warpPerspective(image, M, (width, height)) + + base, ext = os.path.splitext(os.path.basename(file_path)) + extracted_filename = f"{base}_extracted{ext}" + extracted_path = os.path.join(output_folder, extracted_filename) + + # 保存带 alpha 通道的图像 + if warped.shape[2] == 4: + cv2.imwrite(extracted_path, warped) + else: + # 如果没有 alpha 通道，转换为 RGB + cv2.imwrite(extracted_path, cv2.cvtColor(warped, cv2.COLOR_BGR2RGB)) + + print(f"提取并旋转后的图像已保存为：{extracted_path}") + + +def main(): + input_folder = 'output' + output_folder = 'output2' + + if not os.path.exists(output_folder): + os.makedirs(output_folder) + + png_files = [f for f in os.listdir(input_folder) if f.lower().endswith('.png')] + + if not png_files: + print("在指定的文件夹中未找到 PNG 文件。") + return + + for filename in png_files: + file_path = os.path.join(input_folder, filename) + print(f"正在处理文件：{file_path}") + process_png(file_path, output_folder) + + +if __name__ == "__main__": + main() diff --git a/03rectangle_file.py b/03rectangle_file.py new file mode 100644 index 0000000..54ea0fd --- /dev/null +++ b/03rectangle_file.py @@ -0,0 +1,79 @@ +import os +import cv2 +import numpy as np + + +def process_merged_image(file_path, output_folder): + image = cv2.imread(file_path, cv2.IMREAD_UNCHANGED) + if image is None: + print(f"无法读取图像：{file_path}") + return + + if image.shape[2] < 4: + print(f"图像没有 alpha 通道：{file_path}") + return + + alpha = image[:, :, 3] + _, thresh = cv2.threshold(alpha, 0, 255, cv2.THRESH_BINARY) + contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + if not contours: + print("图像中没有非透明部分。") + return + + for idx, cnt in enumerate(contours, start=1): + rect = cv2.minAreaRect(cnt) + box = cv2.boxPoints(rect) + box = np.int32(box) + + width = int(rect[1][0]) + height = int(rect[1][1]) + + if width == 0 or height == 0: + print(f"第 {idx} 个最小矩形的宽度或高度为零，跳过。") + continue + + src_pts = box.astype("float32") + # 定义目标点为水平矩形 + dst_pts = np.array([ + [0, height - 1], + [0, 0], + [width - 1, 0], + [width - 1, height - 1] + ], dtype="float32") + + # 计算透视变换矩阵 + M = cv2.getPerspectiveTransform(src_pts, dst_pts) + warped = cv2.warpPerspective(image, M, (width, height)) + + base, ext = os.path.splitext(os.path.basename(file_path)) + extracted_filename = f"{base}_extracted_{idx}{ext}" + extracted_path = os.path.join(output_folder, extracted_filename) + + # 保存带 alpha 通道的图像 + if warped.shape[2] == 4: + cv2.imwrite(extracted_path, warped) + else: + # 如果没有 alpha 通道，转换为 RGB + cv2.imwrite(extracted_path, cv2.cvtColor(warped, cv2.COLOR_BGR2RGB)) + + print(f"提取并旋转后的图像已保存为：{extracted_path}") + + +def main(): + input_image = os.path.join('output3', 'merged_image.png') + output_folder = 'output4' + + if not os.path.exists(output_folder): + os.makedirs(output_folder) + + if not os.path.isfile(input_image): + print(f"输入图像不存在：{input_image}") + return + + print(f"正在处理图像：{input_image}") + process_merged_image(input_image, output_folder) + + +if __name__ == "__main__": + main() diff --git a/04extract_borders.py b/04extract_borders.py new file mode 100644 index 0000000..fa6dccc --- /dev/null +++ b/04extract_borders.py @@ -0,0 +1,196 @@ +import os +from PIL import Image +import numpy as np + + +def get_edge_opaque_pixels_with_limit(image_path): + """ + 提取图片中每一行和每一列最左、最右、最上、最下的完全不透明像素（alpha == 255）， + 并根据位置限制过滤。 + + Args: + image_path (str): 图片文件路径。 + + Returns: + dict: 包含四个边的像素信息列表。 + """ + with Image.open(image_path).convert("RGBA") as img: + width, height = img.size + pixels = img.load() + limit_left = width / 3 + limit_right = 2 * width / 3 + limit_top = height / 3 + limit_bottom = 2 * height / 3 + + left_pixels = [] + right_pixels = [] + top_pixels = [] + bottom_pixels = [] + + # 左边 + for y in range(height): + for x in range(width): + r, g, b, a = pixels[x, y] + if a == 255 and x <= limit_left: + left_pixels.append({ + "position": (x, y), + "color": (r, g, b, a) + }) + break + + # 右边 + for y in range(height): + for x in range(width - 1, -1, -1): + r, g, b, a = pixels[x, y] + if a == 255 and x >= limit_right: + right_pixels.append({ + "position": (x, y), + "color": (r, g, b, a) + }) + break + + # 上边 + for x in range(width): + for y in range(height): + r, g, b, a = pixels[x, y] + if a == 255 and y <= limit_top: + top_pixels.append({ + "position": (x, y), + "color": (r, g, b, a) + }) + break + + # 下边 + for x in range(width): + for y in range(height - 1, -1, -1): + r, g, b, a = pixels[x, y] + if a == 255 and y >= limit_bottom: + bottom_pixels.append({ + "position": (x, y), + "color": (r, g, b, a) + }) + break + + return { + "left": left_pixels, + "right": right_pixels, + "top": top_pixels, + "bottom": bottom_pixels + } + + +def get_edge_opaque_pixels_with_limit_numpy(image_path): + """ + 使用NumPy提取图片中每一行和每一列最左、最右、最上、最下的完全不透明像素（alpha == 255）， + 并根据位置限制过滤。 + + Args: + image_path (str): 图片文件路径。 + + Returns: + dict: 包含四个边的像素信息列表。 + """ + with Image.open(image_path).convert("RGBA") as img: + data = np.array(img) + height, width, _ = data.shape + limit_left = width / 3 + limit_right = 2 * width / 3 + limit_top = height / 3 + limit_bottom = 2 * height / 3 + + left_pixels = [] + right_pixels = [] + top_pixels = [] + bottom_pixels = [] + + # 左边 + for y in range(height): + row = data[y, :, :] + opaque_indices = np.where(row[:, 3] == 255)[0] + valid_indices = opaque_indices[opaque_indices <= limit_left] + if valid_indices.size > 0: + x = valid_indices[0] + r, g, b, a = row[x] + left_pixels.append({ + "position": (int(x), y), + "color": (int(r), int(g), int(b), int(a)) + }) + + # 右边 + for y in range(height): + row = data[y, :, :] + opaque_indices = np.where(row[:, 3] == 255)[0] + valid_indices = opaque_indices[opaque_indices >= limit_right] + if valid_indices.size > 0: + x = valid_indices[-1] # 最右边的第一个满足条件的像素 + r, g, b, a = row[x] + right_pixels.append({ + "position": (int(x), y), + "color": (int(r), int(g), int(b), int(a)) + }) + + # 上边 + for x in range(width): + column = data[:, x, :] + opaque_indices = np.where(column[:, 3] == 255)[0] + valid_indices = opaque_indices[opaque_indices <= limit_top] + if valid_indices.size > 0: + y = valid_indices[0] + r, g, b, a = column[y] + top_pixels.append({ + "position": (x, int(y)), + "color": (int(r), int(g), int(b), int(a)) + }) + + # 下边 + for x in range(width): + column = data[:, x, :] + opaque_indices = np.where(column[:, 3] == 255)[0] + valid_indices = opaque_indices[opaque_indices >= limit_bottom] + if valid_indices.size > 0: + y = valid_indices[-1] # 最下边的第一个满足条件的像素 + r, g, b, a = column[y] + bottom_pixels.append({ + "position": (x, int(y)), + "color": (int(r), int(g), int(b), int(a)) + }) + + return { + "left": left_pixels, + "right": right_pixels, + "top": top_pixels, + "bottom": bottom_pixels + } + + +def process_directory(directory_path, use_numpy=False): + """ + 遍历指定文件夹下所有图片文件，提取每张图片的边缘不透明像素信息。 + + Args: + directory_path (str): 文件夹路径。 + use_numpy (bool): 是否使用NumPy加速处理。 + + Returns: + dict: 每个文件对应的四个方向的像素信息。 + """ + supported_extensions = ('.png', '.jpg', '.jpeg', '.bmp', '.gif', '.tiff') + result_map = {} + + for filename in os.listdir(directory_path): + if filename.lower().endswith(supported_extensions): + file_path = os.path.join(directory_path, filename) + if use_numpy: + edge_pixels = get_edge_opaque_pixels_with_limit_numpy(file_path) + else: + edge_pixels = get_edge_opaque_pixels_with_limit(file_path) + result_map[filename] = edge_pixels + + return result_map + + +if __name__ == "__main__": + directory_path = "output4" # 替换为你的文件夹路径 + use_numpy = True # 设置为True使用NumPy优化，False使用PIL + result = process_directory(directory_path, use_numpy) + diff --git a/04extract_borders_slim.py b/04extract_borders_slim.py new file mode 100644 index 0000000..ea21b3b --- /dev/null +++ b/04extract_borders_slim.py @@ -0,0 +1,196 @@ +import os +from PIL import Image +import numpy as np + + +def get_edge_opaque_pixels_with_limit_filtered_colors(image_path): + """ + 提取图片中每一行和每一列最左、最右、最上、最下的完全不透明像素（alpha == 255）， + 并根据位置限制过滤，去掉位置和alpha通道。 + + Args: + image_path (str): 图片文件路径。 + + Returns: + dict: 包含四个边的颜色信息列表。 + """ + with Image.open(image_path).convert("RGBA") as img: + width, height = img.size + pixels = img.load() + limit_left = width / 3 + limit_right = 2 * width / 3 + limit_top = height / 3 + limit_bottom = 2 * height / 3 + + left_colors = [] + right_colors = [] + top_colors = [] + bottom_colors = [] + + # 左边 + for y in range(height): + for x in range(width): + r, g, b, a = pixels[x, y] + if a == 255 and x <= limit_left: + left_colors.append((r, g, b)) + break + + # 右边 + for y in range(height): + for x in range(width - 1, -1, -1): + r, g, b, a = pixels[x, y] + if a == 255 and x >= limit_right: + right_colors.append((r, g, b)) + break + + # 上边 + for x in range(width): + for y in range(height): + r, g, b, a = pixels[x, y] + if a == 255 and y <= limit_top: + top_colors.append((r, g, b)) + break + + # 下边 + for x in range(width): + for y in range(height - 1, -1, -1): + r, g, b, a = pixels[x, y] + if a == 255 and y >= limit_bottom: + bottom_colors.append((r, g, b)) + break + + return { + "left": left_colors, + "right": right_colors, + "top": top_colors, + "bottom": bottom_colors + } + + +def get_edge_opaque_pixels_with_limit_filtered_colors_numpy(image_path): + """ + 使用NumPy提取图片中每一行和每一列最左、最右、最上、最下的完全不透明像素（alpha == 255）， + 并根据位置限制过滤，去掉位置和alpha通道。 + + Args: + image_path (str): 图片文件路径。 + + Returns: + dict: 包含四个边的颜色信息列表。 + """ + with Image.open(image_path).convert("RGBA") as img: + data = np.array(img) + height, width, _ = data.shape + limit_left = width / 3 + limit_right = 2 * width / 3 + limit_top = height / 3 + limit_bottom = 2 * height / 3 + + left_colors = [] + right_colors = [] + top_colors = [] + bottom_colors = [] + + # 左边 + for y in range(height): + row = data[y, :, :] + opaque_indices = np.where(row[:, 3] == 255)[0] + valid_indices = opaque_indices[opaque_indices <= limit_left] + if valid_indices.size > 0: + x = valid_indices[0] + r, g, b, _ = row[x] + left_colors.append((int(r), int(g), int(b))) + + # 右边 + for y in range(height): + row = data[y, :, :] + opaque_indices = np.where(row[:, 3] == 255)[0] + valid_indices = opaque_indices[opaque_indices >= limit_right] + if valid_indices.size > 0: + x = valid_indices[-1] + r, g, b, _ = row[x] + right_colors.append((int(r), int(g), int(b))) + + # 上边 + for x in range(width): + column = data[:, x, :] + opaque_indices = np.where(column[:, 3] == 255)[0] + valid_indices = opaque_indices[opaque_indices <= limit_top] + if valid_indices.size > 0: + y = valid_indices[0] + r, g, b, _ = column[y] + top_colors.append((int(r), int(g), int(b))) + + # 下边 + for x in range(width): + column = data[:, x, :] + opaque_indices = np.where(column[:, 3] == 255)[0] + valid_indices = opaque_indices[opaque_indices >= limit_bottom] + if valid_indices.size > 0: + y = valid_indices[-1] + r, g, b, _ = column[y] + bottom_colors.append((int(r), int(g), int(b))) + + return { + "left": left_colors, + "right": right_colors, + "top": top_colors, + "bottom": bottom_colors + } + + +def process_directory_filtered(directory_path, use_numpy=False): + """ + 遍历指定文件夹下所有图片文件，提取每张图片的边缘不透明像素颜色信息。 + + Args: + directory_path (str): 文件夹路径。 + use_numpy (bool): 是否使用NumPy加速处理。 + + Returns: + dict: 每个文件对应的四个方向的颜色信息列表。 + """ + supported_extensions = ('.png', '.jpg', '.jpeg', '.bmp', '.gif', '.tiff') + result_map = {} + + for filename in os.listdir(directory_path): + if filename.lower().endswith(supported_extensions): + file_path = os.path.join(directory_path, filename) + if use_numpy: + edge_colors = get_edge_opaque_pixels_with_limit_filtered_colors_numpy(file_path) + else: + edge_colors = get_edge_opaque_pixels_with_limit_filtered_colors(file_path) + result_map[filename] = edge_colors + + return result_map + + +if __name__ == "__main__": + directory_path = "output4" # 替换为你的文件夹路径 + use_numpy = True # 设置为True使用NumPy优化，False使用PIL + result = process_directory_filtered(directory_path, use_numpy) + + for filename, edges in result.items(): + print(f"文件: {filename}") + + # 左边 + print(" 左边的最左不透明像素颜色:") + for color in edges["left"]: + print(f" 颜色: {color}") + + # 右边 + print(" 右边的最右不透明像素颜色:") + for color in edges["right"]: + print(f" 颜色: {color}") + + # 上边 + print(" 上边的最上不透明像素颜色:") + for color in edges["top"]: + print(f" 颜色: {color}") + + # 下边 + print(" 下边的最下不透明像素颜色:") + for color in edges["bottom"]: + print(f" 颜色: {color}") + + print("\n") diff --git a/angle.py b/angle.py new file mode 100644 index 0000000..6405c04 --- /dev/null +++ b/angle.py @@ -0,0 +1,162 @@ +import cv2 +import numpy as np +import itertools +import os +import time # 引入 time 模块 + +# 设置输入输出文件夹路径 +input_folder = "output10" # 输入文件夹 +output_folder = "output12" # 输出文件夹 + +# 创建输出文件夹（如果不存在） +os.makedirs(output_folder, exist_ok=True) +start_time = time.time() + +def order_points(points): + """ + 将四边形的四个点按顺时针顺序排列。 + :param points: 四边形的四个点 [(x1, y1), (x2, y2), (x3, y3), (x4, y4)] + :return: 排序后的点 + """ + # 计算质心 + center = np.mean(points, axis=0) + + # 计算每个点相对于质心的角度 + angles = [np.arctan2(point[1] - center[1], point[0] - center[0]) for point in points] + + # 按角度排序 + sorted_points = [point for _, point in sorted(zip(angles, points))] + + return sorted_points + + +# 定义处理每张图片的函数 +def process_image(image_path, save_path): + # 加载图片 + image = cv2.imread(image_path, cv2.IMREAD_UNCHANGED) + if image is None: + print(f"无法加载图片: {image_path}") + return + + img_height, img_width = image.shape[:2] + + alpha_channel = image[:, :, 3] + + non_transparent_area = np.count_nonzero(alpha_channel > 0) + if non_transparent_area == 0: # 如果图片完全透明，跳过 + print(f"图片完全透明: {image_path}") + return + + # 双边滤波平滑处理 + #smooth_mask = cv2.bilateralFilter(alpha_channel, d=9, sigmaColor=75, sigmaSpace=75) + + # 高斯模糊平滑处理 + #blurred_mask = cv2.GaussianBlur(smooth_mask, (3, 3), sigmaX=0, sigmaY=0) + + # 二值化处理：转换为边框 mask + _, border_mask = cv2.threshold(alpha_channel, 1, 255, cv2.THRESH_BINARY) + + # 找到边框上的轮廓 + contours, _ = cv2.findContours(border_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + # 创建一个空白的边框图片 + border_image = np.zeros_like(alpha_channel) + cv2.drawContours(border_image, contours, -1, color=255, thickness=1) + + # 角点检测 (最多 16 个角点) + max_corners = 14 + corners = cv2.goodFeaturesToTrack(border_image, maxCorners=max_corners, qualityLevel=0.01, minDistance=15, blockSize=5) + if corners is not None: + corners = np.intp(corners) + corner_points = [tuple(corner.ravel()) for corner in corners] + + quadrilaterals = list(itertools.combinations(corner_points, 4)) + + def calculate_weight(points): + + contour = np.array(points, dtype=np.int32) + area = cv2.contourArea(contour) + + if area/non_transparent_area < 0.8: + return -1 + + if not cv2.isContourConvex(contour): + return -1 + + # print(area/non_transparent_area) + + edges = [np.linalg.norm(np.array(points[i]) - np.array(points[(i + 1) % 4])) for i in range(4)] + max_edge, min_edge = max(edges), min(edges) + if max_edge - min_edge > max_edge / 7: + return -1 + + edge_similarity = 1 - (max_edge - min_edge) / max_edge + angles = [] + for i in range(4): + v1 = np.array(points[(i + 1) % 4]) - np.array(points[i]) + v2 = np.array(points[(i + 3) % 4]) - np.array(points[i]) + cosine_angle = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2)) + angle = np.degrees(np.arccos(np.clip(cosine_angle, -1.0, 1.0))) + angles.append(angle) + if any(angle < 80 or angle > 100 for angle in angles): + return -1 + + angle_similarity = 1 - sum(abs(angle - 90) for angle in angles) / 360 + square_similarity = edge_similarity * angle_similarity + return square_similarity * 0.4 + (area / non_transparent_area) * 0.6 # 使用非透明面积归一化 + + best_quad = None + max_weight = -1 + for quad in quadrilaterals: + # 对最优四边形的点进行排序 + quad = order_points(quad) + + weight = calculate_weight(quad) + if weight > max_weight: + max_weight = weight + best_quad = quad + + + + if best_quad is None: + print(f"未找到有效四边形: {image_path}") + + output_image = cv2.cvtColor(image[:, :, :3], cv2.COLOR_BGR2RGB) + for point in corner_points: + cv2.circle(output_image, point, radius=3, color=(255, 0, 0), thickness=-1) + + # 用蓝色标记边框 + cv2.drawContours(output_image, contours, -1, color=(0, 0, 255), thickness=1) # 蓝色 (BGR: 255, 0, 0) + + cv2.imwrite(save_path, cv2.cvtColor(output_image, cv2.COLOR_RGB2BGR)) + + + return + + + area = cv2.contourArea(np.array(best_quad, dtype=np.int32)) + print(image_path+"\t\t"+str(area / non_transparent_area)) + + output_image = cv2.cvtColor(image[:, :, :3], cv2.COLOR_BGR2RGB) + for point in corner_points: + cv2.circle(output_image, point, radius=3, color=(255, 0, 0), thickness=-1) + + cv2.polylines(output_image, [np.array(best_quad, dtype=np.int32)], isClosed=True, color=(0, 255, 0), thickness=2) + + # 用蓝色标记边框 + cv2.drawContours(output_image, contours, -1, color=(0, 0, 255), thickness=1) # 蓝色 (BGR: 255, 0, 0) + + cv2.imwrite(save_path, cv2.cvtColor(output_image, cv2.COLOR_RGB2BGR)) + else: + print(f"未检测到角点: {image_path}") + + +# 遍历输入文件夹中的图片 +for filename in os.listdir(input_folder): + if filename.lower().endswith((".png", ".jpg", ".jpeg")): # 支持常见图片格式 + input_path = os.path.join(input_folder, filename) + output_path = os.path.join(output_folder, filename) + process_image(input_path, output_path) +end_time = time.time() +print(f"处理图片的时间: {end_time - start_time:.4f} 秒") +print(f"所有图片已处理完成，结果保存在: {output_folder}") diff --git a/angle2.py b/angle2.py new file mode 100644 index 0000000..d52bcf5 --- /dev/null +++ b/angle2.py @@ -0,0 +1,182 @@ +import cv2 +import numpy as np +import itertools +import os +import time + +# 设置输入输出文件夹路径 +input_folder = "output10" # 输入文件夹 +output_folder = "output12" # 输出文件夹 + +# 创建输出文件夹（如果不存在） +os.makedirs(output_folder, exist_ok=True) +start_time = time.time() + +def order_points(points): + """ + 将四边形的四个点按顺时针顺序排列。 + :param points: 四边形的四个点 [(x1, y1), (x2, y2), (x3, y3), (x4, y4)] + :return: 排序后的点 + """ + # 计算质心 + center = np.mean(points, axis=0) + + # 计算每个点相对于质心的角度 + angles = [np.arctan2(point[1] - center[1], point[0] - center[0]) for point in points] + + # 按角度排序 + sorted_points = [point for _, point in sorted(zip(angles, points))] + return sorted_points + +def order_counters(contours): + counterclockwise_contours = [] + for contour in contours: + # 计算轮廓的签名面积 + area = cv2.contourArea(contour, oriented=True) + + # 如果面积是负值，说明轮廓是逆时针 + if area > 0: + # 将轮廓反转为逆时针 + contour = contour[::-1] + + # 添加到新的列表中 + counterclockwise_contours.append(contour) + return counterclockwise_contours + +def calculate_weight(points, non_transparent_area): + """ + 计算四边形的权重，用于选择最优四边形。 + :param points: 四边形的四个点 [(x1, y1), (x2, y2), (x3, y3), (x4, y4)] + :param non_transparent_area: 非透明区域的面积 + :return: 四边形的权重（-1 表示无效四边形） + """ + contour = np.array(points, dtype=np.int32) + area = cv2.contourArea(contour) + + # 筛选条件 1: 四边形的面积比例 + if area / non_transparent_area < 0.8: + return -1 + + # 计算边长 + edges = [np.linalg.norm(np.array(points[i]) - np.array(points[(i + 1) % 4])) for i in range(4)] + max_edge, min_edge = max(edges), min(edges) + + # 筛选条件 2: 边长相似性 + if max_edge - min_edge > max_edge / 7: + return -1 + + edge_similarity = 1 - (max_edge - min_edge) / max_edge + + # 计算角度 + angles = [] + for i in range(4): + v1 = np.array(points[(i + 1) % 4]) - np.array(points[i]) + v2 = np.array(points[(i + 3) % 4]) - np.array(points[i]) + cosine_angle = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2)) + angle = np.degrees(np.arccos(np.clip(cosine_angle, -1.0, 1.0))) + angles.append(angle) + + # 筛选条件 3: 角度相似性 + if any(angle < 80 or angle > 100 for angle in angles): + return -1 + + angle_similarity = 1 - sum(abs(angle - 90) for angle in angles) / 360 + + # 计算最终权重 + square_similarity = edge_similarity * angle_similarity + return square_similarity * 0.5 + (area / non_transparent_area) * 0.5 # 使用非透明面积归一化 + +def find_best_quadrilateral(corner_points, non_transparent_area): + """ + 从角点中筛选最优四边形。 + :param corner_points: 检测到的角点 [(x1, y1), (x2, y2), ...] + :param non_transparent_area: 非透明区域的面积 + :return: 最优四边形的点集和最大权重 + """ + quadrilaterals = list(itertools.combinations(corner_points, 4)) + best_quad = None + max_weight = -1 + + for quad in quadrilaterals: + # 对四边形的点进行顺时针排序 + quad = order_points(quad) + + weight = calculate_weight(quad, non_transparent_area) + if weight > max_weight: + max_weight = weight + best_quad = quad + + return best_quad, max_weight + +def process_image(image_path, save_path): + # 加载图片 + image = cv2.imread(image_path, cv2.IMREAD_UNCHANGED) + if image is None: + print(f"无法加载图片: {image_path}") + return + + img_height, img_width = image.shape[:2] + alpha_channel = image[:, :, 3] + + non_transparent_area = np.count_nonzero(alpha_channel > 0) + if non_transparent_area == 0: # 如果图片完全透明，跳过 + print(f"图片完全透明: {image_path}") + return + + # 找到边框上的轮廓 + contours, _ = cv2.findContours(alpha_channel, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + # 创建一个空白的边框图片 + border_image = np.zeros_like(alpha_channel) + cv2.drawContours(border_image, contours, -1, color=255, thickness=1) + + # 角点检测 (最多 16 个角点) + max_corners = 14 + corners = cv2.goodFeaturesToTrack(border_image, maxCorners=max_corners, qualityLevel=0.01, minDistance=15, blockSize=5) + if corners is not None: + corners = np.intp(corners) + corner_points = [tuple(corner.ravel()) for corner in corners] + + # 使用独立函数筛选最优四边形 + best_quad, max_weight = find_best_quadrilateral(corner_points, non_transparent_area) + + if best_quad is None: + print(f"未找到有效四边形: {image_path}") + + output_image = cv2.cvtColor(image[:, :, :3], cv2.COLOR_BGR2RGB) + for point in corner_points: + cv2.circle(output_image, point, radius=3, color=(255, 0, 0), thickness=-1) + + # 用蓝色标记边框 + cv2.drawContours(output_image, contours, -1, color=(0, 0, 255), thickness=1) # 蓝色 (BGR: 255, 0, 0) + + cv2.imwrite(save_path, cv2.cvtColor(output_image, cv2.COLOR_RGB2BGR)) + return + + area = cv2.contourArea(np.array(best_quad, dtype=np.int32)) + print(image_path+"\t\t"+str(area / non_transparent_area)) + + # 绘制最优四边形和角点 + output_image = cv2.cvtColor(image[:, :, :3], cv2.COLOR_BGR2RGB) + for point in corner_points: + cv2.circle(output_image, point, radius=3, color=(255, 0, 0), thickness=-1) + + cv2.polylines(output_image, [np.array(best_quad, dtype=np.int32)], isClosed=True, color=(0, 255, 0), thickness=2) + + # 用蓝色标记边框 + cv2.drawContours(output_image, contours, -1, color=(0, 0, 255), thickness=1) # 蓝色 (BGR: 255, 0, 0) + + cv2.imwrite(save_path, cv2.cvtColor(output_image, cv2.COLOR_RGB2BGR)) + else: + print(f"未检测到角点: {image_path}") + +# 遍历输入文件夹中的图片 +for filename in os.listdir(input_folder): + if filename.lower().endswith((".png", ".jpg", ".jpeg")): # 支持常见图片格式 + input_path = os.path.join(input_folder, filename) + output_path = os.path.join(output_folder, filename) + process_image(input_path, output_path) + +end_time = time.time() +print(f"处理图片的时间: {end_time - start_time:.4f} 秒") +print(f"所有图片已处理完成，结果保存在: {output_folder}") diff --git a/angle3.py b/angle3.py new file mode 100644 index 0000000..7237b69 --- /dev/null +++ b/angle3.py @@ -0,0 +1,179 @@ +import cv2 +import numpy as np +import itertools +import os +import time +import math + +# 设置输入输出文件夹路径 +input_folder = "output10" # 输入文件夹 +output_folder = "output12" # 输出文件夹 + +# 创建输出文件夹（如果不存在） +os.makedirs(output_folder, exist_ok=True) +start_time = time.time() + +import numpy as np + +def order_points(points, clockwise=True): + """ + 将四边形的四个点按顺时针或逆时针顺序排列。 + :param points: 四边形的四个点 [(x1, y1), (x2, y2), (x3, y3), (x4, y4)] + :param clockwise: 是否按顺时针排列，默认 True（顺时针），False（逆时针） + :return: 排序后的点 + """ + # 计算质心 + center = np.mean(points, axis=0) + + # 计算每个点相对于质心的角度 + angles = [np.arctan2(point[1] - center[1], point[0] - center[0]) for point in points] + + # 按角度排序，顺时针（从大到小）或逆时针（从小到大） + sorted_points = [point for _, point in sorted(zip(angles, points), reverse=clockwise)] + return sorted_points + + +def counter_order(contours): + for contour in contours: + # 计算轮廓的签名面积 + area = cv2.contourArea(contour, oriented=True) + # 如果面积是负值，说明轮廓是逆时针 + return area > 0 + +def calculate_weight(points, non_transparent_area): + contour = np.array(points, dtype=np.int32) + area = cv2.contourArea(contour) + + if area / non_transparent_area < 0.8: + return -1 + + edges = [np.linalg.norm(np.array(points[i]) - np.array(points[(i + 1) % 4])) for i in range(4)] + max_edge, min_edge = max(edges), min(edges) + + if max_edge - min_edge > max_edge / 7: + return -1 + + edge_similarity = 1 - (max_edge - min_edge) / max_edge + + angles = [] + for i in range(4): + v1 = np.array(points[(i + 1) % 4]) - np.array(points[i]) + v2 = np.array(points[(i + 3) % 4]) - np.array(points[i]) + cosine_angle = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2)) + angle = np.degrees(np.arccos(np.clip(cosine_angle, -1.0, 1.0))) + angles.append(angle) + + if any(angle < 80 or angle > 100 for angle in angles): + return -1 + + angle_similarity = 1 - sum(abs(angle - 90) for angle in angles) / 360 + square_similarity = edge_similarity * angle_similarity + return square_similarity * 0.4 + (area / non_transparent_area) * 0.6 + +def find_best_quadrilateral(corner_points, non_transparent_area): + quadrilaterals = list(itertools.combinations(corner_points, 4)) + best_quad = None + max_weight = -1 + + for quad in quadrilaterals: + quad = order_points(quad) + weight = calculate_weight(quad, non_transparent_area) + if weight > max_weight: + max_weight = weight + best_quad = quad + + return best_quad, max_weight + + +def nearest_distance(x1, y1, x2, y2): + distance = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2) + return distance < 2 + +def split_contours_into_segments(contour, quad): + + segments = [[],[],[],[]] + + segment_index = 0 + + flag = [True,True,True,True] + + offset = [] + + for point in contour: + point = point[0] + + if flag[0] & nearest_distance(point[0], point[1], quad[0][0], quad[0][1]): + segment_index += 1 + flag[0] = False + if flag[1] & nearest_distance(point[0], point[1], quad[1][0], quad[1][1]): + segment_index += 1 + flag[1] = False + if flag[2] & nearest_distance(point[0], point[1], quad[2][0], quad[2][1]): + segment_index += 1 + flag[2] = False + if flag[3] & nearest_distance(point[0], point[1], quad[3][0], quad[3][1]): + segment_index += 1 + flag[3] = False + + if segment_index >= 4: + offset.append(point) + else: + segments[segment_index].append(point) + + segments[0] = offset + segments[0] + return segments + + +def process_image(image_path, save_path): + image = cv2.imread(image_path, cv2.IMREAD_UNCHANGED) + if image is None: + print(f"无法加载图片: {image_path}") + return + + alpha_channel = image[:, :, 3] + + non_transparent_area = np.count_nonzero(alpha_channel > 0) + if non_transparent_area == 0: + print(f"图片完全透明: {image_path}") + return + + max_corners = 14 + corners = cv2.goodFeaturesToTrack(alpha_channel, maxCorners=max_corners, qualityLevel=0.01, minDistance=15, blockSize=5) + if corners is not None: + corners = np.intp(corners) + corner_points = [tuple(corner.ravel()) for corner in corners] + + best_quad, max_weight = find_best_quadrilateral(corner_points, non_transparent_area) + + if best_quad is None: + print(f"未找到有效四边形: {image_path}") + return + + output_image = cv2.cvtColor(image[:, :, :3], cv2.COLOR_BGR2RGB) + for point in corner_points: + cv2.circle(output_image, point, radius=3, color=(255, 0, 0), thickness=-1) + + #cv2.polylines(output_image, [np.array(best_quad, dtype=np.int32)], isClosed=True, color=(0, 255, 0), thickness=2) + contours, _ = cv2.findContours(alpha_channel, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) + for contour in contours: + clockwise = counter_order(contours) + best_quad = order_points(best_quad, clockwise) + segments = split_contours_into_segments(contour, best_quad) + colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0)] + for i, segment in enumerate(segments): + if segment: + cv2.polylines(output_image, [np.array(segment)], isClosed=False, color=colors[i], thickness=2) + + cv2.imwrite(save_path, cv2.cvtColor(output_image, cv2.COLOR_RGB2BGR)) + else: + print(f"未检测到角点: {image_path}") + +for filename in os.listdir(input_folder): + if filename.lower().endswith((".png", ".jpg", ".jpeg")): + input_path = os.path.join(input_folder, filename) + output_path = os.path.join(output_folder, filename) + process_image(input_path, output_path) + +end_time = time.time() +print(f"处理图片的时间: {end_time - start_time:.4f} 秒") +print(f"所有图片已处理完成，结果保存在: {output_folder}") diff --git a/angle4.py b/angle4.py new file mode 100644 index 0000000..98f1064 --- /dev/null +++ b/angle4.py @@ -0,0 +1,304 @@ +import cv2 +import numpy as np +import itertools +import os +import time +import math + +from PIL import Image + +# 设置输入输出文件夹路径 +input_folder = "output10" # 输入文件夹 +output_folder = "output12" # 输出文件夹 + +# 创建输出文件夹（如果不存在） +os.makedirs(output_folder, exist_ok=True) +start_time = time.time() + +import numpy as np + +def order_points(points, clockwise=True): + """ + 将四边形的四个点按顺时针或逆时针顺序排列。 + :param points: 四边形的四个点 [(x1, y1), (x2, y2), (x3, y3), (x4, y4)] + :param clockwise: 是否按顺时针排列，默认 True（顺时针），False（逆时针） + :return: 排序后的点 + """ + # 计算质心 + center = np.mean(points, axis=0) + + # 计算每个点相对于质心的角度 + angles = [np.arctan2(point[1] - center[1], point[0] - center[0]) for point in points] + + # 按角度排序，顺时针（从大到小）或逆时针（从小到大） + sorted_points = [point for _, point in sorted(zip(angles, points), reverse=clockwise)] + return sorted_points + + +def counter_order(contours): + for contour in contours: + # 计算轮廓的签名面积 + area = cv2.contourArea(contour, oriented=True) + # 如果面积是负值，说明轮廓是逆时针 + return area > 0 + +def calculate_weight(points, non_transparent_area): + contour = np.array(points, dtype=np.int32) + area = cv2.contourArea(contour) + + if area / non_transparent_area < 0.8: + return -1 + + edges = [np.linalg.norm(np.array(points[i]) - np.array(points[(i + 1) % 4])) for i in range(4)] + max_edge, min_edge = max(edges), min(edges) + + if max_edge - min_edge > max_edge / 7: + return -1 + + edge_similarity = 1 - (max_edge - min_edge) / max_edge + + angles = [] + for i in range(4): + v1 = np.array(points[(i + 1) % 4]) - np.array(points[i]) + v2 = np.array(points[(i + 3) % 4]) - np.array(points[i]) + cosine_angle = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2)) + angle = np.degrees(np.arccos(np.clip(cosine_angle, -1.0, 1.0))) + angles.append(angle) + + if any(angle < 80 or angle > 100 for angle in angles): + return -1 + + angle_similarity = 1 - sum(abs(angle - 90) for angle in angles) / 360 + square_similarity = edge_similarity * angle_similarity + return square_similarity * 0.4 + (area / non_transparent_area) * 0.6 + +def find_best_quadrilateral(corner_points, non_transparent_area): + quadrilaterals = list(itertools.combinations(corner_points, 4)) + best_quad = None + max_weight = -1 + + for quad in quadrilaterals: + quad = order_points(quad) + weight = calculate_weight(quad, non_transparent_area) + if weight > max_weight: + max_weight = weight + best_quad = quad + + return best_quad, max_weight + +def extract_and_save_pixels(image , coordinates, out_image_file): + # 创建一个空白图像，大小为原图大小 + transparent_img = np.zeros_like(image) + + # 设置指定坐标的像素值 + for x, y in coordinates: + if 0 <= x < image.shape[1] and 0 <= y < image.shape[0]: # 检查是否在范围内 + transparent_img[y, x] = image[y, x] + + + + + + # 获取开始坐标和结束坐标 + start_point = coordinates[0] + end_point = coordinates[-1] + + # 计算连线的中间点 + middle_point = ( + (start_point[0] + end_point[0]) // 2, + (start_point[1] + end_point[1]) // 2, + ) + + # 检查中间点是否在图像范围内 + if not (0 <= middle_point[0] < image.shape[1] and 0 <= middle_point[1] < image.shape[0]): + raise ValueError(f"中间点 {middle_point} 超出了图像边界。") + + # 提取中间点的 alpha 通道值 + alpha_value = image[middle_point[1], middle_point[0], 3] # alpha 通道在第 4 位 + + # 判断透明性 + is_transparent = alpha_value == 0 + print(f"中间点 {middle_point} 的透明状态: {'透明' if is_transparent else '非透明'}") + + + + + + + + # 转换坐标为 NumPy 数组 + coordinates_array = np.array(coordinates) + + # 计算最小外接矩形 + rect = cv2.minAreaRect(coordinates_array) + size = tuple(map(int, rect[1])) + + # 如果宽度或高度小于5像素，终止方法 + if size[0] < 5 or size[1] < 5: + return + + # 扩展矩形尺寸 + expanded_width = size[0] + 4 # 每侧扩展 2 个像素 + expanded_height = size[1] + 4 + expanded_size = (expanded_width, expanded_height) + + + # 获取旋转矩阵并旋转图像 + angle = rect[-1] + if angle < -45: + angle += 90 + center = tuple(map(int, rect[0])) + size = tuple(map(int, rect[1])) + rotation_matrix = cv2.getRotationMatrix2D(center, angle, 1.0) + # 对图像进行仿射变换 + rotated_img = cv2.warpAffine(transparent_img, rotation_matrix, (image.shape[1], image.shape[0]), + flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0, 0)) + + # 确保尺寸为正数 + size = (max(int(size[0]), 1), max(int(size[1]), 1)) + + # 检查裁剪范围是否超出边界 + x_start = max(int(center[0] - expanded_size[0] / 2), 0) + x_end = min(int(center[0] + expanded_size[0] / 2), rotated_img.shape[1]) + y_start = max(int(center[1] - expanded_size[1] / 2), 0) + y_end = min(int(center[1] + expanded_size[1] / 2), rotated_img.shape[0]) + + if x_start >= x_end or y_start >= y_end: + raise ValueError("裁剪范围无效，可能是输入坐标错误或图片过小。") + + # 裁剪最小矩形 + cropped_img = rotated_img[y_start:y_end, x_start:x_end] + + # 如果宽度小于高度，旋转 90° + if cropped_img.shape[1] < cropped_img.shape[0]: + cropped_img = cv2.rotate(cropped_img, cv2.ROTATE_90_CLOCKWISE) + + # 判断是否需要旋转 180° + h, w, _ = cropped_img.shape + half_h = h // 2 + + upper_half = cropped_img[:half_h, :, 3] # 提取上半部分的 alpha 通道 + lower_half = cropped_img[half_h:, :, 3] # 提取下半部分的 alpha 通道 + + upper_non_transparent = np.sum(upper_half > 0) + lower_non_transparent = np.sum(lower_half > 0) + + # 如果上半部分非透明像素多于下半部分，旋转 180° + if upper_non_transparent > lower_non_transparent: + cropped_img = cv2.rotate(cropped_img, cv2.ROTATE_180) + + + # 检查裁剪结果是否为空 + if cropped_img.size == 0: + raise ValueError("裁剪结果为空，请检查坐标范围。") + + # 保存结果 + cv2.imwrite("output13/"+str(is_transparent)+"_"+out_image_file, cropped_img) + print(f"结果图片已保存到 {output_path}") + + +def nearest_distance(x1, y1, x2, y2): + distance = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2) + return distance < 2 + +def split_contours_into_segments(contour, quad): + + segments = [[],[],[],[]] + + segment_index = 0 + + flag = [True,True,True,True] + + offset = [] + + for point in contour: + point = point[0] + + if flag[0] & nearest_distance(point[0], point[1], quad[0][0], quad[0][1]): + segment_index += 1 + flag[0] = False + if flag[1] & nearest_distance(point[0], point[1], quad[1][0], quad[1][1]): + segment_index += 1 + flag[1] = False + if flag[2] & nearest_distance(point[0], point[1], quad[2][0], quad[2][1]): + segment_index += 1 + flag[2] = False + if flag[3] & nearest_distance(point[0], point[1], quad[3][0], quad[3][1]): + segment_index += 1 + flag[3] = False + + if segment_index >= 4: + offset.append(point) + else: + segments[segment_index].append(point) + + segments[0] = offset + segments[0] + return segments + + +def add_suffix(file_path, suffix): + # 分割文件名和扩展名 + dir_name, file_name_with_ext = os.path.split(file_path) + file_name, file_ext = os.path.splitext(file_name_with_ext) + + # 添加后缀 + new_file_name = f"{file_name}{suffix}{file_ext}" + + return new_file_name + +def process_image(image_path, save_path): + image = cv2.imread(image_path, cv2.IMREAD_UNCHANGED) + if image is None: + print(f"无法加载图片: {image_path}") + return + + alpha_channel = image[:, :, 3] + + non_transparent_area = np.count_nonzero(alpha_channel > 0) + if non_transparent_area == 0: + print(f"图片完全透明: {image_path}") + return + + max_corners = 14 + corners = cv2.goodFeaturesToTrack(alpha_channel, maxCorners=max_corners, qualityLevel=0.01, minDistance=15, blockSize=5) + if corners is not None: + corners = np.intp(corners) + corner_points = [tuple(corner.ravel()) for corner in corners] + + best_quad, max_weight = find_best_quadrilateral(corner_points, non_transparent_area) + + if best_quad is None: + print(f"未找到有效四边形: {image_path}") + return + + output_image = cv2.cvtColor(image[:, :, :3], cv2.COLOR_BGR2RGB) + for point in corner_points: + cv2.circle(output_image, point, radius=3, color=(255, 0, 0), thickness=-1) + + #cv2.polylines(output_image, [np.array(best_quad, dtype=np.int32)], isClosed=True, color=(0, 255, 0), thickness=2) + contours, _ = cv2.findContours(alpha_channel, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) + for contour in contours: + clockwise = counter_order(contours) + best_quad = order_points(best_quad, clockwise) + segments = split_contours_into_segments(contour, best_quad) + colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0)] + for i, segment in enumerate(segments): + if segment: + cv2.polylines(output_image, [np.array(segment)], isClosed=False, color=colors[i], thickness=2) + + extract_and_save_pixels(image, segment, add_suffix(save_path,"_" + str(i))) + + cv2.imwrite(save_path, cv2.cvtColor(output_image, cv2.COLOR_RGB2BGR)) + + else: + print(f"未检测到角点: {image_path}") + +for filename in os.listdir(input_folder): + if filename.lower().endswith((".png", ".jpg", ".jpeg")): + input_path = os.path.join(input_folder, filename) + output_path = os.path.join(output_folder, filename) + process_image(input_path, output_path) + +end_time = time.time() +print(f"处理图片的时间: {end_time - start_time:.4f} 秒") +print(f"所有图片已处理完成，结果保存在: {output_folder}") diff --git a/birds.png b/birds.png new file mode 100644 index 0000000..aa12d1f Binary files /dev/null and b/birds.png differ diff --git a/combined_image.png b/combined_image.png new file mode 100644 index 0000000..89c76ab Binary files /dev/null and b/combined_image.png differ diff --git a/compare.py b/compare.py new file mode 100644 index 0000000..5880fff --- /dev/null +++ b/compare.py @@ -0,0 +1,96 @@ +import os +import shutil + +import cv2 +import numpy as np +from skimage.metrics import structural_similarity as ssim + +def load_images(folder): + """Load all PNG images from a folder, categorized by prefix.""" + true_images = {} + false_images = {} + for filename in os.listdir(folder): + if filename.endswith('.png'): + filepath = os.path.join(folder, filename) + image = cv2.imread(filepath, cv2.IMREAD_UNCHANGED) # Load image with alpha channel + if filename.startswith("True_"): + true_images[filename] = image + elif filename.startswith("False_"): + false_images[filename] = image + return true_images, false_images + +def resize_image(image, size=(125, 50)): + """Resize an image to a standard size.""" + return cv2.resize(image, size, interpolation=cv2.INTER_AREA) + +def calculate_ssim(image1, image2): + """Calculate SSIM between two images.""" + if image1.shape[2] == 4: # Handle alpha channel + image1 = cv2.cvtColor(image1, cv2.COLOR_BGRA2BGR) + if image2.shape[2] == 4: # Handle alpha channel + image2 = cv2.cvtColor(image2, cv2.COLOR_BGRA2BGR) + gray1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY) + gray2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY) + score, _ = ssim(gray1, gray2, full=True) + return score + +def calculate_color_similarity(image1, image2): + """Compare color histograms of two images.""" + if image1.shape[2] == 4: # Handle alpha channel + image1 = cv2.cvtColor(image1, cv2.COLOR_BGRA2BGR) + if image2.shape[2] == 4: # Handle alpha channel + image2 = cv2.cvtColor(image2, cv2.COLOR_BGRA2BGR) + hist1 = cv2.calcHist([image1], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256]) + hist2 = cv2.calcHist([image2], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256]) + hist1 = cv2.normalize(hist1, hist1).flatten() + hist2 = cv2.normalize(hist2, hist2).flatten() + return cv2.compareHist(hist1, hist2, cv2.HISTCMP_CORREL) + +def calculate_combined_similarity(image1, image2): + """Combine SSIM and color histogram similarity.""" + # ssim_score = calculate_ssim(image1, image2) + color_score = calculate_color_similarity(image1, image2) + # Weight SSIM and color similarity equally (adjust weights as needed) + return 1 * color_score + +def find_most_similar_images(true_images, false_images): + """Find the most similar image for each 'True_' image among 'False_' images.""" + results = {} + for true_name, true_image in true_images.items(): + max_similarity = -1 + most_similar_image = None + for false_name, false_image in false_images.items(): + # resized_true = resize_image(true_image) + # resized_false = resize_image(false_image) + + if true_name.split("_")[2] == false_name.split("_")[2]: + continue + + similarity = calculate_combined_similarity(true_image, false_image) + if similarity > max_similarity: + max_similarity = similarity + most_similar_image = false_name + results[true_name] = (most_similar_image, max_similarity) + os.makedirs("output14\\"+true_name+"_"+most_similar_image,exist_ok=True) + shutil.copyfile('output13\\'+true_name, 'output14\\'+true_name+"_"+most_similar_image+"\\"+true_name) + shutil.copyfile('output13\\' + most_similar_image, 'output14\\' +true_name+"_"+most_similar_image+"\\" + most_similar_image) + + # shutil.copyfile('output12\\'+ "segment_"+true_name.split("_")[2]+".png", 'output14\\'+true_name+"_"+most_similar_image+"\\segment_"+true_name.split("_")[2]+".png") + # shutil.copyfile('output12\\' +"segment_"+most_similar_image.split("_")[2]+".png", 'output14\\'+true_name+"_"+most_similar_image+"\\segment_"+most_similar_image.split("_")[2]+".png") + + shutil.copyfile('output10\\'+ "segment_"+true_name.split("_")[2]+".png", 'output14\\'+true_name+"_"+most_similar_image+"\\segment_"+true_name.split("_")[2]+".png") + shutil.copyfile('output10\\' +"segment_"+most_similar_image.split("_")[2]+".png", 'output14\\'+true_name+"_"+most_similar_image+"\\segment_"+most_similar_image.split("_")[2]+".png") + + + return results + +def main(): + folder = 'output13' # Replace with your folder path + true_images, false_images = load_images(folder) + results = find_most_similar_images(true_images, false_images) + for true_image, (most_similar, similarity) in results.items(): + print(f"True Image: {true_image}, Most Similar False Image: {most_similar}, Similarity: {similarity:.4f}") + + +if __name__ == '__main__': + main() diff --git a/extract_mask.py b/extract_mask.py new file mode 100644 index 0000000..1c80140 --- /dev/null +++ b/extract_mask.py @@ -0,0 +1,92 @@ +import cv2 +import numpy as np +from PIL import Image +import matplotlib.pyplot as plt + +# 读取图像 +image_path = 'jigsaw_mask.png' # 替换为您的图像路径 +image = cv2.imread(image_path) + +if image is None: + raise ValueError("无法读取图像，请检查路径是否正确。") + +# 转换为RGB +image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + +# 转换为灰度图 +gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + +# 应用固定阈值进行二值化 +_, mask = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV) + +# 进行形态学操作以连接断裂的线条 +# 定义较大的核 +kernel = np.ones((1,1), np.uint8) + +# 增加膨胀次数以连接断裂 +mask = cv2.dilate(mask, kernel, iterations=1) + +# 闭运算进一步连接 +mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=5) + +# 再次确保掩膜是二值的 +_, mask = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY) + +# 创建 Alpha 通道 +alpha = cv2.bitwise_not(mask) + +# 确保 Alpha 通道只有0和255 +alpha = cv2.threshold(alpha, 127, 255, cv2.THRESH_BINARY)[1] + +# 创建纯白色背景 +white_bg = 255 * np.ones_like(image_rgb, dtype=np.uint8) + +# 合并 RGB 图像和 Alpha 通道 +final_rgb = cv2.bitwise_and(white_bg, white_bg, mask=cv2.bitwise_not(mask)) +image_rgba = cv2.cvtColor(final_rgb, cv2.COLOR_RGB2RGBA) +image_rgba[:, :, 3] = alpha + +# 将 OpenCV 的 RGBA 图像转换为 Pillow Image 对象 +image_pil = Image.fromarray(image_rgba) + +# 转换为 NumPy 数组以进一步处理 +data = np.array(image_pil) + +# 分离颜色通道 +red, green, blue, alpha = data.T + +# 定义黑色的阈值范围 +black_threshold = 50 # 根据需要调整 + +# 创建掩膜：黑色区域为True，其余为False +black_areas = (red < black_threshold) & (green < black_threshold) & (blue < black_threshold) + +# 设置黑色区域的 Alpha 为0，非黑色区域的 Alpha 为255 +data[..., 3][black_areas.T] = 0 +data[..., 3][~black_areas.T] = 255 + +# 将非黑色区域设置为纯白色 +data[..., 0:3][~black_areas.T] = [255, 255, 255] + +# 创建新的 Image 对象 +final_image = Image.fromarray(data) + +# 保存结果为 PNG（支持透明度） +output_path = 'mask.png' +final_image.save(output_path) + +print(f"已保存带纯白背景和透明部分的图像到 {output_path}") + +# 可选：显示图像 +plt.figure(figsize=(12,6)) +plt.subplot(1,2,1) +plt.title('原图') +plt.imshow(image_rgb) +plt.axis('off') + +plt.subplot(1,2,2) +plt.title('带纯白背景和透明部分的图像') +plt.imshow(final_image) +plt.axis('off') + +plt.show() diff --git a/input.jpg b/input.jpg new file mode 100644 index 0000000..3202edc Binary files /dev/null and b/input.jpg differ diff --git a/jigsaw_mask.png b/jigsaw_mask.png new file mode 100644 index 0000000..e56c290 Binary files /dev/null and b/jigsaw_mask.png differ diff --git a/mask.png b/mask.png new file mode 100644 index 0000000..7809eef Binary files /dev/null and b/mask.png differ diff --git a/mask_split.py b/mask_split.py new file mode 100644 index 0000000..c2b7ab3 --- /dev/null +++ b/mask_split.py @@ -0,0 +1,14 @@ +from PIL import Image + +from PIL import Image + +# 打开目标图片和线条蒙版 +image = Image.open("birds.png").convert("RGBA") +mask = Image.open("mask.png").convert("L") # 灰度图 + +# 将蒙版应用到图片上 +# mask 是透明区域，255 表示不透明，0 表示完全透明 +image.putalpha(mask) + +# 保存结果 +image.save("result.png") diff --git a/mask_split_files.py b/mask_split_files.py new file mode 100644 index 0000000..9a15833 --- /dev/null +++ b/mask_split_files.py @@ -0,0 +1,115 @@ +import os +import numpy as np +from PIL import Image +import scipy.ndimage as ndimage +from scipy.ndimage import label, find_objects +import random + + +def create_segment_mask(alpha_channel): + """ + 创建分割掩码，找出被透明线条分隔的区域 + """ + binary_mask = alpha_channel < 255 # 透明区域 + labeled_array, num_features = label(~binary_mask) + + segment_masks = [] + for i in range(1, num_features + 1): + segment_mask = labeled_array == i + segment_masks.append(segment_mask) + + return segment_masks + + +def extract_segment_with_transparent_background(image, segment_mask): + """ + 提取分割区域，区域外设置为透明 + """ + slices = find_objects(segment_mask.astype(int))[0] + + y_slice = slice(max(0, slices[0].start - 5), min(image.shape[0], slices[0].stop + 5)) + x_slice = slice(max(0, slices[1].start - 5), min(image.shape[1], slices[1].stop + 5)) + + segment_image = np.zeros((image.shape[0], image.shape[1], 4), dtype=np.uint8) + segment_image[y_slice, x_slice][segment_mask[y_slice, x_slice]] = image[y_slice, x_slice][ + segment_mask[y_slice, x_slice]] + + return segment_image + + +def smart_crop_and_rotate(image, border_size=15): + """ + 智能裁剪和随机旋转，保留指定像素的透明边框 + """ + # 将numpy数组转换为PIL图像 + pil_image = Image.fromarray(image) + + # 找到非透明区域的边界框 + bbox = pil_image.getbbox() + + if bbox: + # 扩大裁剪区域，保留指定像素的透明边框 + left = max(0, bbox[0] - border_size) + upper = max(0, bbox[1] - border_size) + right = min(image.shape[1], bbox[2] + border_size) + lower = min(image.shape[0], bbox[3] + border_size) + + # 裁剪 + cropped_image = pil_image.crop((left, upper, right, lower)) + + # # 随机旋转角度（-45到45度） + rotation_angle = random.uniform(-180, 180) + rotated_image = cropped_image.rotate(rotation_angle, + expand=True, + fillcolor=(0, 0, 0, 0)) + + return np.array(rotated_image) + + return image + + +def main(input_path, output_folder): + """ + 主处理函数 + """ + # 创建输出文件夹 + os.makedirs(output_folder, exist_ok=True) + + # 随机数种子（保证每次运行结果可复现） + random.seed() + + # 打开图像 + image = Image.open(input_path) + + # 转换为RGBA模式 + image = image.convert("RGBA") + + # 转换为numpy数组 + img_array = np.array(image) + + # 获取透明通道 + alpha_channel = img_array[:, :, 3] + + # 创建分割掩码 + segment_masks = create_segment_mask(alpha_channel) + + # 保存每个分割区域 + for i, segment_mask in enumerate(segment_masks): + # 提取分割区域，区域外设置为透明 + segment_image_array = extract_segment_with_transparent_background(img_array, segment_mask) + + # 裁剪并随机旋转，保留20像素透明边框 + processed_segment = smart_crop_and_rotate(segment_image_array, border_size=20) + + # 从numpy数组创建图像 + segment_image = Image.fromarray(processed_segment) + + # 保存图像 + output_path = os.path.join(output_folder, f'segment_{i + 1}.png') + segment_image.save(output_path) + + print(f"分割、裁剪和旋转完成，共生成 {len(segment_masks)} 个图像片段") + + +# 执行分割 +main('result.png', 'output10') \ No newline at end of file diff --git a/minRectangle.py b/minRectangle.py new file mode 100644 index 0000000..49fc76e --- /dev/null +++ b/minRectangle.py @@ -0,0 +1,56 @@ +import os +import cv2 +import numpy as np + +# 输入和输出文件夹 +input_folder = 'output10' +output_folder = 'output11' + +# 确保输出文件夹存在 +os.makedirs(output_folder, exist_ok=True) + +# 遍历输入文件夹中的所有图片 +for filename in os.listdir(input_folder): + filepath = os.path.join(input_folder, filename) + + # 检查是否是图片文件 + if not filename.lower().endswith(('.png', '.jpg', '.jpeg')): + continue + + # 读取图片 + image = cv2.imread(filepath, cv2.IMREAD_UNCHANGED) + + if image is None: + print(f"无法读取文件: {filepath}") + continue + + # 检查是否有 alpha 通道 + if image.shape[2] == 4: + alpha_channel = image[:, :, 3] + _, binary_mask = cv2.threshold(alpha_channel, 1, 255, cv2.THRESH_BINARY) + else: + # 如果没有 alpha 通道，直接将非黑色区域作为非透明区域 + gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + _, binary_mask = cv2.threshold(gray, 1, 255, cv2.THRESH_BINARY) + + # 找到非透明区域的轮廓 + contours, _ = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + if contours: + # 获取最大轮廓的最小旋转矩形 + largest_contour = max(contours, key=cv2.contourArea) + rect = cv2.minAreaRect(largest_contour) + box = cv2.boxPoints(rect) + box = np.intp(box) # 修正数据类型 + + # 在原图上绘制矩形 + result_image = image.copy() + if image.shape[2] == 4: # 如果有 alpha 通道 + result_image = cv2.cvtColor(result_image, cv2.COLOR_BGRA2BGR) + cv2.drawContours(result_image, [box], 0, (0, 255, 0), 2) + + # 保存结果图片 + output_path = os.path.join(output_folder, filename) + cv2.imwrite(output_path, result_image) + +print("处理完成，结果已保存到 output11 文件夹中。") diff --git a/minRectangle2.py b/minRectangle2.py new file mode 100644 index 0000000..8bbae9c --- /dev/null +++ b/minRectangle2.py @@ -0,0 +1,49 @@ +import os +from PIL import Image, ImageDraw + +def draw_centered_red_box(image, percentage=0.8, color=(255, 0, 0)): + """ + 在图片上绘制一个红色框，框住指定比例的面积。 + image: PIL Image 对象 + percentage: 正方形框的面积占图片面积的比例（0-1之间） + color: 框的颜色 (R, G, B) + """ + w, h = image.size + # 计算正方形的边长 + box_size = int((w * h * percentage) ** 0.5) + half_box = box_size // 2 + + # 确定框的中心点 + center_x, center_y = w // 2, h // 2 + + # 计算正方形的边界 + left = max(center_x - half_box, 0) + top = max(center_y - half_box, 0) + right = min(center_x + half_box, w) + bottom = min(center_y + half_box, h) + + # 在图片上绘制红色框 + draw = ImageDraw.Draw(image) + draw.rectangle([left, top, right, bottom], outline=color, width=3) # 边框宽度为 3 像素 + + return image + +# 文件夹路径 +input_folder = "output10" +output_folder = "output10_with_red_box" +os.makedirs(output_folder, exist_ok=True) + +# 遍历文件夹中的图片 +for filename in os.listdir(input_folder): + if filename.lower().endswith(('.png', '.jpg', '.jpeg')): + file_path = os.path.join(input_folder, filename) + img = Image.open(file_path) + + # 添加红色框 + img_with_box = draw_centered_red_box(img, percentage=0.8) + + # 保存结果 + output_path = os.path.join(output_folder, filename) + img_with_box.save(output_path) + +print("处理完成，已保存带框的图片到 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-0,0 +1,91 @@ +import os +import sys + +rename_map = { + "segment_1.png": "segment_001.png", + "segment_2.png": "segment_002.png", + "segment_3.png": "segment_003.png", + "segment_4.png": "segment_004.png", + "segment_5.png": "segment_005.png", + "segment_6.png": "segment_006.png", + "segment_7.png": "segment_007.png", + "segment_8.png": "segment_008.png", + "segment_9.png": "segment_009.png", + "segment_10.png": "segment_011.png", + + "segment_11.png": "segment_013.png", + "segment_12.png": "segment_015.png", + "segment_13.png": "segment_017.png", + "segment_14.png": "segment_010.png", + "segment_15.png": "segment_012.png", + "segment_16.png": "segment_014.png", + "segment_17.png": "segment_016.png", + "segment_18.png": "segment_018.png", + "segment_19.png": "segment_019.png", + "segment_20.png": "segment_021.png", + + "segment_21.png": "segment_023.png", + "segment_22.png": "segment_025.png", + "segment_23.png": "segment_027.png", + "segment_24.png": "segment_020.png", + "segment_25.png": "segment_022.png", + "segment_26.png": "segment_024.png", + "segment_27.png": "segment_026.png", + "segment_28.png": "segment_029.png", + "segment_29.png": "segment_031.png", + "segment_30.png": "segment_033.png", + + "segment_31.png": "segment_035.png", + "segment_32.png": "segment_028.png", + "segment_33.png": "segment_030.png", + "segment_34.png": "segment_032.png", + "segment_35.png": "segment_034.png", + "segment_36.png": "segment_036.png", + "segment_37.png": "segment_037.png", + "segment_38.png": "segment_039.png", + "segment_39.png": "segment_041.png", + "segment_40.png": "segment_043.png", + + "segment_41.png": "segment_045.png", + "segment_42.png": "segment_038.png", + "segment_43.png": "segment_040.png", + "segment_44.png": "segment_042.png", + "segment_45.png": "segment_044.png", + "segment_46.png": "segment_047.png", + "segment_47.png": "segment_049.png", + "segment_48.png": "segment_051.png", + "segment_49.png": "segment_053.png", + "segment_50.png": "segment_046.png", + + "segment_51.png": "segment_048.png", + "segment_52.png": "segment_050.png", + "segment_53.png": "segment_052.png", + "segment_54.png": "segment_054.png", + + # 添加更多的文件映射关系 +} + +def rename_files(folder_path): + if not os.path.isdir(folder_path): + print(f"错误：{folder_path} 不是一个有效的文件夹路径。") + return + + for old_name, new_name in rename_map.items(): + old_path = os.path.join(folder_path, old_name) + new_path = os.path.join(folder_path, new_name) + + if not os.path.exists(old_path): + print(f"警告：文件 {old_name} 不存在，跳过。") + continue + + + + try: + os.replace(old_path, new_path) + print(f"已将 {old_name} 重命名为 {new_name}") + except Exception as e: + print(f"错误：无法将 {old_name} 重命名为 {new_name}。原因：{e}") + +if __name__ == "__main__": + + rename_files("output10") diff --git a/result - Copy.png b/result - Copy.png new file mode 100644 index 0000000..3e4cc78 Binary files /dev/null and b/result - Copy.png differ diff --git a/result.png b/result.png new file mode 100644 index 0000000..3e4cc78 Binary files /dev/null and b/result.png differ diff --git a/result22.png b/result22.png new file mode 100644 index 0000000..01d843c Binary files /dev/null and b/result22.png differ diff --git a/split01.py b/split01.py new file mode 100644 index 0000000..5d223c7 --- /dev/null +++ b/split01.py @@ -0,0 +1,46 @@ +from PIL import Image +import os + + +def split_image(image_path, output_dir, rows=10, cols=10): + """ + 将图片切成指定行列的小块。 + + 参数： + image_path (str): 输入图片的路径。 + output_dir (str): 保存小块图片的目录。 + rows (int): 切分的行数。 + cols (int): 切分的列数。 + """ + # 打开图片 + img = Image.open(image_path) + img_width, img_height = img.size + + # 计算每块的宽和高 + piece_width = img_width // cols + piece_height = img_height // rows + + # 创建输出目录 + os.makedirs(output_dir, exist_ok=True) + + # 遍历切割 + for row in range(rows): + for col in range(cols): + # 定义每块的区域 (left, upper, right, lower) + left = col * piece_width + upper = row * piece_height + right = (col + 1) * piece_width + lower = (row + 1) * piece_height + + # 裁剪出小块并保存 + piece = img.crop((left, upper, right, lower)) + piece_path = os.path.join(output_dir, f"piece_{row}_{col}.png") + piece.save(piece_path) + + print(f"图片已分割成 {rows * cols} 小块，保存到：{output_dir}") + + +# 使用示例 +image_path = "input.jpg" # 输入图片路径 +output_dir = "output_pieces" # 输出目录 +split_image(image_path, output_dir) diff --git a/split02.py b/split02.py new file mode 100644 index 0000000..af0ffb2 --- /dev/null +++ b/split02.py @@ -0,0 +1,75 @@ +from PIL import Image, ImageDraw +import numpy as np +import os +import random + + +def generate_jigsaw_shape(piece_width, piece_height, variation=10): + """ + 生成拼图块的曲线边界。 + :param piece_width: 每块拼图的宽度。 + :param piece_height: 每块拼图的高度。 + :param variation: 曲线的随机偏移量。 + :return: 拼图块的路径点列表。 + """ + points = [] + for x in range(0, piece_width + 1): + y = piece_height // 2 + random.randint(-variation, variation) + points.append((x, y)) + for y in range(piece_height, -1, -1): + x = piece_width // 2 + random.randint(-variation, variation) + points.append((x, y)) + return points + + +def create_jigsaw_pieces(image_path, output_dir, rows=5, cols=5, variation=10): + """ + 使用曲线形状将图片裁剪成拼图块。 + :param image_path: 输入图片路径。 + :param output_dir: 输出拼图块的保存目录。 + :param rows: 拼图的行数。 + :param cols: 拼图的列数。 + :param variation: 拼图曲线的随机偏移量。 + """ + # 打开图片 + img = Image.open(image_path) + img_width, img_height = img.size + + # 每块的宽和高 + piece_width = img_width // cols + piece_height = img_height // rows + + # 创建输出目录 + os.makedirs(output_dir, exist_ok=True) + + for row in range(rows): + for col in range(cols): + # 定义裁剪区域 + left = col * piece_width + upper = row * piece_height + right = (col + 1) * piece_width + lower = (row + 1) * piece_height + + # 生成随机曲线形状 + points = generate_jigsaw_shape(piece_width, piece_height, variation) + + # 创建掩码 + mask = Image.new("L", (piece_width, piece_height), 0) + draw = ImageDraw.Draw(mask) + draw.polygon(points, fill=255) + + # 裁剪拼图块 + piece = img.crop((left, upper, right, lower)) + piece.putalpha(mask) + + # 保存拼图块 + piece_path = os.path.join(output_dir, f"piece_{row}_{col}.png") + piece.save(piece_path, "PNG") + + print(f"拼图已生成，保存在：{output_dir}") + + +# 示例调用 +image_path = "input.jpg" # 输入图片路径 +output_dir = "output_jigsaw" # 输出目录 +create_jigsaw_pieces(image_path, output_dir, rows=5, cols=5, variation=15) diff --git a/split03.py b/split03.py new file mode 100644 index 0000000..a937c6a --- /dev/null +++ b/split03.py @@ -0,0 +1,48 @@ +import cv2 +import numpy as np +from PIL import Image +import os + +def split_image_with_template(image_path, template_path, output_dir): + """ + 根据拼图模板，将图片裁剪成拼图块。 + :param image_path: 输入图片路径 + :param template_path: 拼图模板路径 + :param output_dir: 输出拼图块保存目录 + """ + # 加载图片和模板 + img = cv2.imread(image_path) + template = cv2.imread(template_path, cv2.IMREAD_GRAYSCALE) + + # 确保模板是二值化图像 + _, mask = cv2.threshold(template, 127, 255, cv2.THRESH_BINARY) + + # 查找轮廓 + contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + # 创建输出目录 + os.makedirs(output_dir, exist_ok=True) + + # 裁剪每块拼图 + for i, contour in enumerate(contours): + # 创建与原始图片相同大小的掩码 + piece_mask = np.zeros_like(mask) + cv2.drawContours(piece_mask, [contour], -1, 255, thickness=-1) + + # 裁剪区域 + x, y, w, h = cv2.boundingRect(contour) + piece = cv2.bitwise_and(img, img, mask=piece_mask) + piece_cropped = piece[y:y + h, x:x + w] + + # 保存拼图块 + piece_path = os.path.join(output_dir, f"piece_{i}.png") + cv2.imwrite(piece_path, piece_cropped) + + print(f"拼图已生成，共 {len(contours)} 块，保存到：{output_dir}") + + +# 示例调用 +image_path = "input.jpg" # 原始图片路径 +template_path = "mask.png" # 拼图模板路径 +output_dir = "output_jigsaw" # 输出路径 +split_image_with_template(image_path, template_path, output_dir) diff --git a/split04.py b/split04.py new file mode 100644 index 0000000..e1d0dfb --- /dev/null +++ b/split04.py @@ -0,0 +1,88 @@ +import numpy as np +from PIL import Image, ImageDraw +import os +import random + + +def generate_curve(start, end, height, num_points=10): + """ + 生成一条随机曲线，用于拼图边界。 + :param start: 曲线起点坐标 (x, y) + :param end: 曲线终点坐标 (x, y) + :param height: 曲线的最大上下偏移高度 + :param num_points: 曲线的弯点数量 + :return: 曲线坐标列表 + """ + points = [start] + for i in range(1, num_points): + x = start[0] + (end[0] - start[0]) * i / num_points + y = start[1] + (end[1] - start[1]) * i / num_points + random.randint(-height, height) + points.append((x, y)) + points.append(end) + return points + + +def create_jigsaw_with_curves(image_path, output_dir, rows=5, cols=5, curve_height=20, num_points=10): + """ + 用曲线切割图片成拼图块。 + :param image_path: 输入图片路径 + :param output_dir: 输出拼图块保存目录 + :param rows: 拼图的行数 + :param cols: 拼图的列数 + :param curve_height: 曲线的最大偏移高度 + :param num_points: 每条曲线的弯点数量 + """ + # 加载图片 + img = Image.open(image_path) + img_width, img_height = img.size + + # 每块的宽和高 + piece_width = img_width // cols + piece_height = img_height // rows + + # 创建输出目录 + os.makedirs(output_dir, exist_ok=True) + + # 生成拼图 + for row in range(rows): + for col in range(cols): + # 创建掩码 + mask = Image.new("L", (img_width, img_height), 0) + draw = ImageDraw.Draw(mask) + + # 定义拼图块的四个边界 + left = col * piece_width + top = row * piece_height + right = (col + 1) * piece_width + bottom = (row + 1) * piece_height + + # 上边界 + top_curve = generate_curve((left, top), (right, top), curve_height, num_points) + # 下边界 + bottom_curve = generate_curve((right, bottom), (left, bottom), curve_height, num_points) + # 左边界 + left_curve = generate_curve((left, bottom), (left, top), curve_height, num_points) + # 右边界 + right_curve = generate_curve((right, top), (right, bottom), curve_height, num_points) + + # 构造拼图块路径 + path = top_curve + bottom_curve + left_curve + right_curve + + # 绘制掩码 + draw.polygon(path, fill=255) + + # 裁剪拼图块 + piece = Image.composite(img, Image.new("RGBA", img.size, (255, 255, 255, 0)), mask) + piece_cropped = piece.crop((left, top, right, bottom)) + + # 保存拼图块 + piece_path = os.path.join(output_dir, f"piece_{row}_{col}.png") + piece_cropped.save(piece_path) + + print(f"拼图已生成，共 {rows * cols} 块，保存到：{output_dir}") + + +# 示例调用 +image_path = "input.jpg" # 输入图片路径 +output_dir = "output_jigsaw" # 输出目录 +create_jigsaw_with_curves(image_path, output_dir, rows=5, cols=5, curve_height=30, num_points=10) diff --git a/split05.py b/split05.py new file mode 100644 index 0000000..4eea1e2 --- /dev/null +++ b/split05.py @@ -0,0 +1,64 @@ +import cv2 +import numpy as np + +def generate_curve_points(width, height, num_points=10): + """生成曲线的控制点，并绘制平滑曲线""" + # 随机生成控制点 + points = np.array([(x, np.random.randint(0, height)) for x in np.linspace(0, width, num_points)]) + # 使用样条曲线生成平滑曲线 + curve = cv2.approxPolyDP(points, 1, closed=False) + return curve.reshape(-1, 2) + +def split_image_with_curve(image, curve, grid_size=10): + """使用曲线将图像分割为小块""" + h, w = image.shape[:2] + grid_h, grid_w = h // grid_size, w // grid_size + pieces = [] + + # 遍历每个网格块 + for i in range(grid_size): + for j in range(grid_size): + # 计算网格块的范围 + x_start, y_start = j * grid_w, i * grid_h + x_end, y_end = x_start + grid_w, y_start + grid_h + + # 创建一个空白掩膜并绘制曲线 + mask = np.zeros((grid_h, grid_w), dtype=np.uint8) + curve_segment = curve[np.logical_and(curve[:, 0] >= x_start, curve[:, 0] <= x_end)] + for k in range(len(curve_segment) - 1): + cv2.line(mask, tuple(curve_segment[k]), tuple(curve_segment[k + 1]), 255, 2) + + # 将网格块切成上下两部分 + part1 = cv2.bitwise_and(image[y_start:y_end, x_start:x_end], image[y_start:y_end, x_start:x_end], mask=mask) + part2 = cv2.bitwise_and(image[y_start:y_end, x_start:x_end], image[y_start:y_end, x_start:x_end], mask=cv2.bitwise_not(mask)) + + pieces.append((part1, part2)) + + return pieces + +# 主程序 +if __name__ == "__main__": + # 加载正方形图片 + image_path = "input.jpg" # 替换为你的图片路径 + image = cv2.imread(image_path) + + if image is None: + print("无法加载图片，请检查路径！") + exit() + + # 确保图片是正方形 + h, w = image.shape[:2] + if h != w: + print("图片不是正方形，请提供正方形图片！") + exit() + + # 生成曲线 + curve = generate_curve_points(w, h) + + # 切割图片 + pieces = split_image_with_curve(image, curve) + + # 保存分割块 + for idx, (part1, part2) in enumerate(pieces): + cv2.imwrite(f"part1_{idx}.png", part1) + cv2.imwrite(f"part2_{idx}.png", part2) diff --git a/split06.py b/split06.py new file mode 100644 index 0000000..768df93 --- /dev/null +++ b/split06.py @@ -0,0 +1,43 @@ +import cv2 +import numpy as np +import os + +# 读取输入图像 +image = cv2.imread('input.jpg') + +# 获取图像的尺寸 +height, width = image.shape[:2] + +# 创建一个全黑的掩码 +mask = np.zeros((height, width), dtype=np.uint8) + +# 定义曲线（例如，使用正弦函数） +x = np.arange(width) +# 调整曲线的频率和幅度 +frequency = 2 * np.pi / width +amplitude = height / 4 +y = (height / 2) + amplitude * np.sin(frequency * x) + +# 绘制曲线以上的区域为白色 +for i in range(width): + cv2.line(mask, (i, 0), (i, int(y[i])), 255) + +# 创建反掩码 +mask_inv = cv2.bitwise_not(mask) + +# 应用掩码得到第一张图片 +img1 = cv2.bitwise_and(image, image, mask=mask) + +# 应用反掩码得到第二张图片 +img2 = cv2.bitwise_and(image, image, mask=mask_inv) + +# 确保输出目录存在 +output_dir = 'output' +if not os.path.exists(output_dir): + os.makedirs(output_dir) + +# 保存结果图片 +cv2.imwrite(os.path.join(output_dir, 'output1.jpg'), img1) +cv2.imwrite(os.path.join(output_dir, 'output2.jpg'), img2) + +print("图片已成功切割并保存到 'output' 目录下。") diff --git a/split07.py b/split07.py new file mode 100644 index 0000000..cfffdbc --- /dev/null +++ b/split07.py @@ -0,0 +1,69 @@ +import cv2 +import numpy as np +import os + +# 读取输入图像 +image = cv2.imread('input.jpg') + +# 检查图像是否读取成功 +if image is None: + print("无法读取 input.jpg，请确保文件存在且路径正确。") + exit() + +# 获取图像的尺寸 +height, width = image.shape[:2] + +# 创建四个掩码，初始化为全黑 +mask1 = np.zeros((height, width), dtype=np.uint8) +mask2 = np.zeros((height, width), dtype=np.uint8) +mask3 = np.zeros((height, width), dtype=np.uint8) +mask4 = np.zeros((height, width), dtype=np.uint8) + +# 定义两条曲线，用于将图像分割成四部分 +x = np.arange(width) + +# 第一条曲线（例如，正弦函数） +frequency1 = 2 * np.pi / width +amplitude1 = height / 4 +y1 = (height / 2) + amplitude1 * np.sin(frequency1 * x) + +# 第二条曲线（例如，余弦函数） +frequency2 = 4 * np.pi / width +amplitude2 = height / 6 +y2 = (height / 2) + amplitude2 * np.cos(frequency2 * x) + +# 确保y值在图像高度范围内 +y1 = np.clip(y1, 0, height - 1) +y2 = np.clip(y2, 0, height - 1) + +# 绘制第一个区域的掩码（曲线 y1 以上的区域） +for i in range(width): + cv2.line(mask1, (i, 0), (i, int(y1[i])), 255) + +# 绘制第二个区域的掩码（曲线 y1 和 y2 之间的区域） +for i in range(width): + y_top = int(min(y1[i], y2[i])) + y_bottom = int(max(y1[i], y2[i])) + cv2.line(mask2, (i, y_top), (i, y_bottom), 255) + +# 绘制第三个区域的掩码（曲线 y2 以下的区域） +for i in range(width): + cv2.line(mask3, (i, int(y2[i])), (i, height), 255) + +# 绘制第四个区域的掩码（剩余区域） +mask4 = cv2.bitwise_not(cv2.bitwise_or(cv2.bitwise_or(mask1, mask2), mask3)) + +# 创建掩码列表 +masks = [mask1, mask2, mask3, mask4] + +# 确保输出目录存在 +output_dir = 'output' +if not os.path.exists(output_dir): + os.makedirs(output_dir) + +# 应用掩码并保存图片 +for idx, mask in enumerate(masks): + img_part = cv2.bitwise_and(image, image, mask=mask) + cv2.imwrite(os.path.join(output_dir, f'output{idx+1}.png'), img_part) + +print("图片已成功切割成4张，并保存到 'output' 目录下。") diff --git a/split08.py b/split08.py new file mode 100644 index 0000000..305caaf --- /dev/null +++ b/split08.py @@ -0,0 +1,87 @@ +import cv2 +import numpy as np +import os + +# 读取输入图像 +image = cv2.imread('input.jpg') + +# 检查图像是否读取成功 +if image is None: + print("无法读取 input.jpg，请确保文件存在且路径正确。") + exit() + +# 获取图像的尺寸 +height, width = image.shape[:2] + +# 创建四个掩码，初始化为全黑 +mask1 = np.zeros((height, width), dtype=np.uint8) +mask2 = np.zeros((height, width), dtype=np.uint8) +mask3 = np.zeros((height, width), dtype=np.uint8) +mask4 = np.zeros((height, width), dtype=np.uint8) + +# 定义三条曲线 +x = np.arange(width) + +# 第一条曲线（正弦函数） +frequency1 = 2 * np.pi / width +amplitude1 = height / 4 +y1 = (height / 4) + amplitude1 * np.sin(frequency1 * x) + +# 第二条曲线（余弦函数） +frequency2 = 4 * np.pi / width +amplitude2 = height / 4 +y2 = (height / 2) + amplitude2 * np.cos(frequency2 * x) + +# 第三条曲线（不同频率的正弦函数） +frequency3 = 6 * np.pi / width +amplitude3 = height / 4 +y3 = (3 * height / 4) + amplitude3 * np.sin(frequency3 * x) + +# 确保y值在图像高度范围内 +y1 = np.clip(y1, 0, height - 1) +y2 = np.clip(y2, 0, height - 1) +y3 = np.clip(y3, 0, height - 1) + +# 绘制第一个区域的掩码（从顶部到y1） +for i in range(width): + y_top = 0 + y_bottom = int(y1[i]) + cv2.line(mask1, (i, y_top), (i, y_bottom), 255) + +# 绘制第二个区域的掩码（从y1到y2） +for i in range(width): + y_top = int(y1[i]) + y_bottom = int(y2[i]) + if y_bottom >= y_top: + cv2.line(mask2, (i, y_top), (i, y_bottom), 255) + else: + cv2.line(mask2, (i, y_bottom), (i, y_top), 255) + +# 绘制第三个区域的掩码（从y2到y3） +for i in range(width): + y_top = int(y2[i]) + y_bottom = int(y3[i]) + if y_bottom >= y_top: + cv2.line(mask3, (i, y_top), (i, y_bottom), 255) + else: + cv2.line(mask3, (i, y_bottom), (i, y_top), 255) + +# 绘制第四个区域的掩码（从y3到底部） +for i in range(width): + y_top = int(y3[i]) + y_bottom = height - 1 + cv2.line(mask4, (i, y_top), (i, y_bottom), 255) + +# 确保输出目录存在 +output_dir = 'output' +if not os.path.exists(output_dir): + os.makedirs(output_dir) + +# 应用掩码并保存图片 +masks = [mask1, mask2, mask3, mask4] + +for idx, mask in enumerate(masks): + img_part = cv2.bitwise_and(image, image, mask=mask) + cv2.imwrite(os.path.join(output_dir, f'output{idx+1}.png'), img_part) + +print("图片已成功切割成4张，并保存到 'output' 目录下。") diff --git a/split09.py b/split09.py new file mode 100644 index 0000000..db6a1fc --- /dev/null +++ b/split09.py @@ -0,0 +1,63 @@ +import cv2 +import numpy as np +import os + +# 读取输入图像 +image = cv2.imread('input.jpg') + +# 检查图像是否读取成功 +if image is None: + print("无法读取 input.jpg，请确保文件存在且路径正确。") + exit() + +# 获取图像的尺寸 +height, width = image.shape[:2] + +# 创建四个掩码，初始化为全黑 +mask1 = np.zeros((height, width), dtype=np.uint8) # 左上角 +mask2 = np.zeros((height, width), dtype=np.uint8) # 右上角 +mask3 = np.zeros((height, width), dtype=np.uint8) # 左下角 +mask4 = np.zeros((height, width), dtype=np.uint8) # 右下角 + +# 定义横向曲线（沿着 x 轴变化） +x = np.arange(width) +frequency_h = 2 * np.pi / width +amplitude_h = height / 10 # 调整幅度为图像高度的 1/10 +y_h = (height / 2) + amplitude_h * np.sin(frequency_h * x) +y_h = np.clip(y_h, 0, height - 1).astype(int) # 确保 y 值在图像范围内 + +# 定义纵向曲线（沿着 y 轴变化） +y = np.arange(height) +frequency_v = 2 * np.pi / height +amplitude_v = width / 10 # 调整幅度为图像宽度的 1/10 +x_v = (width / 2) + amplitude_v * np.sin(frequency_v * y) +x_v = np.clip(x_v, 0, width - 1).astype(int) # 确保 x 值在图像范围内 + +# 创建掩码 +for i in range(height): + for j in range(width): + # 获取当前像素相对于曲线的位置 + y_curve = y_h[j] + x_curve = x_v[i] + + if i <= y_curve and j <= x_curve: + mask1[i, j] = 255 # 左上角 + elif i <= y_curve and j > x_curve: + mask2[i, j] = 255 # 右上角 + elif i > y_curve and j <= x_curve: + mask3[i, j] = 255 # 左下角 + else: + mask4[i, j] = 255 # 右下角 + +# 确保输出目录存在 +output_dir = 'output' +if not os.path.exists(output_dir): + os.makedirs(output_dir) + +# 应用掩码并保存图片 +masks = [mask1, mask2, mask3, mask4] +for idx, mask in enumerate(masks): + img_part = cv2.bitwise_and(image, image, mask=mask) + cv2.imwrite(os.path.join(output_dir, f'output{idx + 1}.png'), img_part) + +print("图片已成功切割成4张，并保存到 'output' 目录下。") diff --git a/split10.py b/split10.py new file mode 100644 index 0000000..ca01877 --- /dev/null +++ b/split10.py @@ -0,0 +1,64 @@ +import cv2 +import numpy as np +import os + +# 读取输入图像 +image = cv2.imread('input.jpg') + +# 检查图像是否读取成功 +if image is None: + print("无法读取 input.jpg，请确保文件存在且路径正确。") + exit() + +# 获取图像的尺寸 +height, width = image.shape[:2] + +# 创建四个掩码，初始化为全黑 +mask1 = np.zeros((height, width), dtype=np.uint8) # 左上角 +mask2 = np.zeros((height, width), dtype=np.uint8) # 右上角 +mask3 = np.zeros((height, width), dtype=np.uint8) # 左下角 +mask4 = np.zeros((height, width), dtype=np.uint8) # 右下角 + +# 定义横向曲线（沿着 x 轴变化） +x = np.arange(width) +num_bends = 4 # 4个弯曲 +frequency_h = num_bends * 2 * np.pi / width +amplitude_h = height / 20 # 调整幅度为图像高度的 1/20，幅度较小 +y_h = (height / 2) + amplitude_h * np.sin(frequency_h * x) +y_h = np.clip(y_h, 0, height - 1).astype(int) # 确保 y 值在图像范围内 + +# 定义纵向曲线（沿着 y 轴变化） +y = np.arange(height) +frequency_v = num_bends * 2 * np.pi / height +amplitude_v = width / 20 # 调整幅度为图像宽度的 1/20，幅度较小 +x_v = (width / 2) + amplitude_v * np.sin(frequency_v * y) +x_v = np.clip(x_v, 0, width - 1).astype(int) # 确保 x 值在图像范围内 + +# 创建掩码 +for i in range(height): + for j in range(width): + # 获取当前像素相对于曲线的位置 + y_curve = y_h[j] + x_curve = x_v[i] + + if i <= y_curve and j <= x_curve: + mask1[i, j] = 255 # 左上角 + elif i <= y_curve and j > x_curve: + mask2[i, j] = 255 # 右上角 + elif i > y_curve and j <= x_curve: + mask3[i, j] = 255 # 左下角 + else: + mask4[i, j] = 255 # 右下角 + +# 确保输出目录存在 +output_dir = 'output' +if not os.path.exists(output_dir): + os.makedirs(output_dir) + +# 应用掩码并保存图片 +masks = [mask1, mask2, mask3, mask4] +for idx, mask in enumerate(masks): + img_part = cv2.bitwise_and(image, image, mask=mask) + cv2.imwrite(os.path.join(output_dir, f'output{idx + 1}.png'), img_part) + +print("图片已成功切割成4张，并保存到 'output' 目录下。") diff --git a/split11.py b/split11.py new file mode 100644 index 0000000..19a8386 --- /dev/null +++ b/split11.py @@ -0,0 +1,86 @@ +import cv2 +import numpy as np +import os + +# 读取输入图像 +image = cv2.imread('input.jpg') + +# 检查图像是否读取成功 +if image is None: + print("无法读取 input.jpg，请确保文件存在且路径正确。") + exit() + +# 获取图像的尺寸 +height, width = image.shape[:2] + +# 创建九个掩码，初始化为全黑 +masks = [np.zeros((height, width), dtype=np.uint8) for _ in range(9)] + +# 定义曲线参数 +num_bends = 12 # 增加弯曲数 +amplitude_h = height / 20 # 横向曲线的幅度 +amplitude_v = width / 20 # 纵向曲线的幅度 + +# 定义两条横向曲线（沿着 x 轴变化） +x = np.arange(width) +frequency_h = num_bends * np.pi / width + +y_h1 = (height / 3) + amplitude_h * np.sin(frequency_h * x) +y_h2 = (2 * height / 3) + amplitude_h * np.sin(frequency_h * x) + +y_h1 = np.clip(y_h1, 0, height - 1).astype(int) +y_h2 = np.clip(y_h2, 0, height - 1).astype(int) + +# 定义两条纵向曲线（沿着 y 轴变化） +y = np.arange(height) +frequency_v = num_bends * np.pi / height + +x_v1 = (width / 3) + amplitude_v * np.sin(frequency_v * y) +x_v2 = (2 * width / 3) + amplitude_v * np.sin(frequency_v * y) + +x_v1 = np.clip(x_v1, 0, width - 1).astype(int) +x_v2 = np.clip(x_v2, 0, width - 1).astype(int) + +# 创建掩码 +for i in range(height): + for j in range(width): + # 获取当前像素相对于曲线的位置 + y_curve1 = y_h1[j] + y_curve2 = y_h2[j] + x_curve1 = x_v1[i] + x_curve2 = x_v2[i] + + # 判断所在区域 + if i <= y_curve1: + if j <= x_curve1: + masks[0][i, j] = 255 # 左上角 + elif j <= x_curve2: + masks[1][i, j] = 255 # 上中 + else: + masks[2][i, j] = 255 # 右上角 + elif i <= y_curve2: + if j <= x_curve1: + masks[3][i, j] = 255 # 中左 + elif j <= x_curve2: + masks[4][i, j] = 255 # 中间 + else: + masks[5][i, j] = 255 # 中右 + else: + if j <= x_curve1: + masks[6][i, j] = 255 # 左下角 + elif j <= x_curve2: + masks[7][i, j] = 255 # 下中 + else: + masks[8][i, j] = 255 # 右下角 + +# 确保输出目录存在 +output_dir = 'output' +if not os.path.exists(output_dir): + os.makedirs(output_dir) + +# 应用掩码并保存图片 +for idx, mask in enumerate(masks): + img_part = cv2.bitwise_and(image, image, mask=mask) + cv2.imwrite(os.path.join(output_dir, f'output{idx+1}.png'), img_part) + +print("图片已成功切割成9张，并保存到 'output' 目录下。") diff --git a/split12.py b/split12.py new file mode 100644 index 0000000..56a328b --- /dev/null +++ b/split12.py @@ -0,0 +1,99 @@ +import cv2 +import numpy as np +import os + +# 读取输入图像 +image = cv2.imread('input.jpg') + +# 检查图像是否读取成功 +if image is None: + print("无法读取 input.jpg，请确保文件存在且路径正确。") + exit() + +# 获取图像的尺寸 +height, width = image.shape[:2] + +# 创建九个掩码，初始化为全黑 +masks = [np.zeros((height, width), dtype=np.uint8) for _ in range(9)] + +# 定义曲线参数 +num_bends = 12 # 弯曲次数 +amplitude_h = height / 20 # 横向曲线的最大幅度 +amplitude_v = width / 20 # 纵向曲线的最大幅度 + +# 定义幅度调节函数，使曲线在交点处的幅度减小 +def amplitude_modifier(pos, center_pos, width): + return 1 - np.exp(-((pos - center_pos) ** 2) / (2 * (width ** 2))) + +# 定义两条横向曲线（沿着 x 轴变化） +x = np.arange(width) +frequency_h = num_bends * np.pi / width + +# 计算横向曲线的幅度调节 +amplitude_modifier_h1 = amplitude_modifier(x, width / 3, width / 10) +amplitude_modifier_h2 = amplitude_modifier(x, 2 * width / 3, width / 10) + +# 计算横向曲线的 y 值 +y_h1 = (height / 3) + (amplitude_h * amplitude_modifier_h1) * np.sin(frequency_h * x) +y_h2 = (2 * height / 3) + (amplitude_h * amplitude_modifier_h2) * np.sin(frequency_h * x) + +y_h1 = np.clip(y_h1, 0, height - 1).astype(int) +y_h2 = np.clip(y_h2, 0, height - 1).astype(int) + +# 定义两条纵向曲线（沿着 y 轴变化） +y = np.arange(height) +frequency_v = num_bends * np.pi / height + +# 计算纵向曲线的幅度调节 +amplitude_modifier_v1 = amplitude_modifier(y, height / 3, height / 10) +amplitude_modifier_v2 = amplitude_modifier(y, 2 * height / 3, height / 10) + +# 计算纵向曲线的 x 值 +x_v1 = (width / 3) + (amplitude_v * amplitude_modifier_v1) * np.sin(frequency_v * y) +x_v2 = (2 * width / 3) + (amplitude_v * amplitude_modifier_v2) * np.sin(frequency_v * y) + +x_v1 = np.clip(x_v1, 0, width - 1).astype(int) +x_v2 = np.clip(x_v2, 0, width - 1).astype(int) + +# 创建掩码 +for i in range(height): + for j in range(width): + y_curve1 = y_h1[j] + y_curve2 = y_h2[j] + x_curve1 = x_v1[i] + x_curve2 = x_v2[i] + + # 判断所在区域 + if i <= y_curve1: + if j <= x_curve1: + masks[0][i, j] = 255 # 左上角 + elif j <= x_curve2: + masks[1][i, j] = 255 # 上中 + else: + masks[2][i, j] = 255 # 右上角 + elif i <= y_curve2: + if j <= x_curve1: + masks[3][i, j] = 255 # 中左 + elif j <= x_curve2: + masks[4][i, j] = 255 # 中间 + else: + masks[5][i, j] = 255 # 中右 + else: + if j <= x_curve1: + masks[6][i, j] = 255 # 左下角 + elif j <= x_curve2: + masks[7][i, j] = 255 # 下中 + else: + masks[8][i, j] = 255 # 右下角 + +# 确保输出目录存在 +output_dir = 'output' +if not os.path.exists(output_dir): + os.makedirs(output_dir) + +# 应用掩码并保存图片 +for idx, mask in enumerate(masks): + img_part = cv2.bitwise_and(image, image, mask=mask) + cv2.imwrite(os.path.join(output_dir, f'output{idx+1}.png'), img_part) + +print("图片已成功切割成9张，并保存到 'output' 目录下。") diff --git a/split13.py b/split13.py new file mode 100644 index 0000000..5016a5a --- /dev/null +++ b/split13.py @@ -0,0 +1,99 @@ +import cv2 +import numpy as np +import os + +# 读取输入图像 +image = cv2.imread('input.jpg') + +# 检查图像是否读取成功 +if image is None: + print("无法读取 input.jpg，请确保文件存在且路径正确。") + exit() + +# 获取图像的尺寸 +height, width = image.shape[:2] + +# 创建九个掩码，初始化为全黑 +masks = [np.zeros((height, width), dtype=np.uint8) for _ in range(9)] + +# 定义曲线参数 +num_bends = 9 # 弯曲次数 +amplitude_h = height / 30 # 横向曲线的最大幅度 +amplitude_v = width / 30 # 纵向曲线的最大幅度 + +# 定义幅度调节函数，使曲线在交点处的幅度为零，并平滑过渡 +def amplitude_modifier(pos, center_pos, width): + return np.sin(np.pi * (pos - center_pos) / (2 * width)) ** 2 + +# 定义两条横向曲线（沿着 x 轴变化） +x = np.arange(width) +frequency_h = num_bends * np.pi / width + +# 计算横向曲线的幅度调节，使在交点处幅度为零 +amplitude_modifier_h1 = 1 - amplitude_modifier(x, width / 3, width / 6) +amplitude_modifier_h2 = 1 - amplitude_modifier(x, 2 * width / 3, width / 6) + +# 计算横向曲线的 y 值 +y_h1 = (height / 3) + (amplitude_h * amplitude_modifier_h1) * np.sin(frequency_h * x) +y_h2 = (2 * height / 3) + (amplitude_h * amplitude_modifier_h2) * np.sin(frequency_h * x) + +y_h1 = np.clip(y_h1, 0, height - 1).astype(int) +y_h2 = np.clip(y_h2, 0, height - 1).astype(int) + +# 定义两条纵向曲线（沿着 y 轴变化） +y = np.arange(height) +frequency_v = num_bends * np.pi / height + +# 计算纵向曲线的幅度调节，使在交点处幅度为零 +amplitude_modifier_v1 = 1 - amplitude_modifier(y, height / 3, height / 6) +amplitude_modifier_v2 = 1 - amplitude_modifier(y, 2 * height / 3, height / 6) + +# 计算纵向曲线的 x 值 +x_v1 = (width / 3) + (amplitude_v * amplitude_modifier_v1) * np.sin(frequency_v * y) +x_v2 = (2 * width / 3) + (amplitude_v * amplitude_modifier_v2) * np.sin(frequency_v * y) + +x_v1 = np.clip(x_v1, 0, width - 1).astype(int) +x_v2 = np.clip(x_v2, 0, width - 1).astype(int) + +# 创建掩码 +for i in range(height): + for j in range(width): + y_curve1 = y_h1[j] + y_curve2 = y_h2[j] + x_curve1 = x_v1[i] + x_curve2 = x_v2[i] + + # 判断所在区域 + if i <= y_curve1: + if j <= x_curve1: + masks[0][i, j] = 255 # 左上角 + elif j <= x_curve2: + masks[1][i, j] = 255 # 上中 + else: + masks[2][i, j] = 255 # 右上角 + elif i <= y_curve2: + if j <= x_curve1: + masks[3][i, j] = 255 # 中左 + elif j <= x_curve2: + masks[4][i, j] = 255 # 中间 + else: + masks[5][i, j] = 255 # 中右 + else: + if j <= x_curve1: + masks[6][i, j] = 255 # 左下角 + elif j <= x_curve2: + masks[7][i, j] = 255 # 下中 + else: + masks[8][i, j] = 255 # 右下角 + +# 确保输出目录存在 +output_dir = 'output' +if not os.path.exists(output_dir): + os.makedirs(output_dir) + +# 应用掩码并保存图片 +for idx, mask in enumerate(masks): + img_part = cv2.bitwise_and(image, image, mask=mask) + cv2.imwrite(os.path.join(output_dir, f'output{idx+1}.png'), img_part) + +print("图片已成功切割成9张，并保存到 'output' 目录下。") diff --git a/split14.py b/split14.py new file mode 100644 index 0000000..a7cfb15 --- /dev/null +++ b/split14.py @@ -0,0 +1,128 @@ +import cv2 +import numpy as np +import os + +# 读取输入图像 +image = cv2.imread('input.jpg') + +# 检查图像是否读取成功 +if image is None: + print("无法读取 input.jpg，请确保文件存在且路径正确。") + exit() + +# 获取图像的尺寸 +height, width = image.shape[:2] + +# 创建九个掩码，初始化为全黑 +masks = [np.zeros((height, width), dtype=np.uint8) for _ in range(9)] + +# 定义曲线参数，增加弯曲幅度 +amplitude_h = height / 15 # 横向曲线的最大幅度（修改为height / 15，增加弯曲） +amplitude_v = width / 15 # 纵向曲线的最大幅度（修改为width / 15，增加弯曲） + +# 定义 x 和 y 坐标 +x = np.arange(width) +y = np.arange(height) + +# 定义横向曲线（沿着 x 轴变化），在每个区域的中间进行一次弯曲 +y_h1 = np.zeros_like(x, dtype=float) +y_h2 = np.zeros_like(x, dtype=float) + +# 定义纵向曲线（沿着 y 轴变化），在每个区域的中间进行一次弯曲 +x_v1 = np.zeros_like(y, dtype=float) +x_v2 = np.zeros_like(y, dtype=float) + +# 定义横向曲线的三个段 +segment_width = width / 3 + +for i in range(3): + # 定义每个段的起始和结束位置 + start_x = i * segment_width + end_x = (i + 1) * segment_width + center_x = (start_x + end_x) / 2 + + # 创建掩码，确定在哪个段上 + mask = (x >= start_x) & (x < end_x) + + # 计算在该段上的相对位置 + x_relative = x[mask] - center_x + + # 计算幅度调节函数，使得在段的边界处为零，在中心为一 + amplitude_modifier = np.cos(np.pi * x_relative / (end_x - start_x)) ** 2 + + # 计算曲线 y 值 + y_h1[mask] = (height / 3) + amplitude_h * amplitude_modifier * np.sin(np.pi * x_relative / (end_x - start_x)) + y_h2[mask] = (2 * height / 3) + amplitude_h * amplitude_modifier * np.sin(np.pi * x_relative / (end_x - start_x)) + +# 确保 y 值在图像范围内 +y_h1 = np.clip(y_h1, 0, height - 1).astype(int) +y_h2 = np.clip(y_h2, 0, height - 1).astype(int) + +# 定义纵向曲线的三个段 +segment_height = height / 3 + +for i in range(3): + # 定义每个段的起始和结束位置 + start_y = i * segment_height + end_y = (i + 1) * segment_height + center_y = (start_y + end_y) / 2 + + # 创建掩码，确定在哪个段上 + mask = (y >= start_y) & (y < end_y) + + # 计算在该段上的相对位置 + y_relative = y[mask] - center_y + + # 计算幅度调节函数，使得在段的边界处为零，在中心为一 + amplitude_modifier = np.cos(np.pi * y_relative / (end_y - start_y)) ** 2 + + # 计算曲线 x 值 + x_v1[mask] = (width / 3) + amplitude_v * amplitude_modifier * np.sin(np.pi * y_relative / (end_y - start_y)) + x_v2[mask] = (2 * width / 3) + amplitude_v * amplitude_modifier * np.sin(np.pi * y_relative / (end_y - start_y)) + +# 确保 x 值在图像范围内 +x_v1 = np.clip(x_v1, 0, width - 1).astype(int) +x_v2 = np.clip(x_v2, 0, width - 1).astype(int) + +# 创建掩码 +for i in range(height): + for j in range(width): + y_curve1 = y_h1[j] + y_curve2 = y_h2[j] + x_curve1 = x_v1[i] + x_curve2 = x_v2[i] + + # 判断所在区域 + if i <= y_curve1: + if j <= x_curve1: + masks[0][i, j] = 255 # 左上角 + elif j <= x_curve2: + masks[1][i, j] = 255 # 上中 + else: + masks[2][i, j] = 255 # 右上角 + elif i <= y_curve2: + if j <= x_curve1: + masks[3][i, j] = 255 # 中左 + elif j <= x_curve2: + masks[4][i, j] = 255 # 中间 + else: + masks[5][i, j] = 255 # 中右 + else: + if j <= x_curve1: + masks[6][i, j] = 255 # 左下角 + elif j <= x_curve2: + masks[7][i, j] = 255 # 下中 + else: + masks[8][i, j] = 255 # 右下角 + +# 确保输出目录存在 +output_dir = 'output' +if not os.path.exists(output_dir): + os.makedirs(output_dir) + +# 应用掩码并保存图片 +for idx, mask in enumerate(masks): + img_part = cv2.bitwise_and(image, image, mask=mask) + cv2.imwrite(os.path.join(output_dir, f'output{idx+1}.png'), img_part) + +print("图片已成功切割成9张，并保存到 'output' 目录下。") diff --git a/split16.py b/split16.py new file mode 100644 index 0000000..e91123d --- /dev/null +++ b/split16.py @@ -0,0 +1,118 @@ +import cv2 +import numpy as np +import os + +# 读取输入图像 +image = cv2.imread('input.jpg') + +# 检查图像是否读取成功 +if image is None: + print("无法读取 'input.jpg'，请确保文件存在且路径正确。") + exit() + +# 获取图像的尺寸 +height, width = image.shape[:2] + +# 定义小图片的数量（例如，将图像分割成 3 x 3 个小正方形） +num_rows = 3 +num_cols = 3 + +# 计算每个小正方形的尺寸 +cell_height = height // num_rows +cell_width = width // num_cols + +# 定义曲线的参数 +curve_amplitude = min(cell_height, cell_width) / 6 # 曲线的幅度，可根据需要调整 +curve_length_ratio = 1 / 3 # 曲线占分割线长度的中间三分之一 + +# 创建输出目录 +output_dir = 'output' +if not os.path.exists(output_dir): + os.makedirs(output_dir) + +# 创建掩码列表，每个小图片对应一个掩码 +masks = [] + +# 初始化掩码 +for _ in range(num_rows * num_cols): + mask = np.zeros((height, width), dtype=np.uint8) + masks.append(mask) + +# 生成曲线偏移量的函数 +def generate_curve(length, amplitude): + x = np.linspace(0, length - 1, num=length) + y = amplitude * np.sin(np.pi * (x / (length - 1))) + return y.astype(int) + +# 设置每个小图片的区域为白色，并创建曲线分割线 +for row in range(num_rows): + for col in range(num_cols): + index = row * num_cols + col + mask = masks[index] + + # 定义当前小图片的边界 + y_start = row * cell_height + y_end = y_start + cell_height if row != num_rows - 1 else height + x_start = col * cell_width + x_end = x_start + cell_width if col != num_cols - 1 else width + + # 在掩码上设置小图片区域为白色 + mask[y_start:y_end, x_start:x_end] = 255 + +# 创建垂直曲线分割线（列之间） +for col in range(1, num_cols): + x_line = col * cell_width + + # 曲线应用于垂直分割线的中间三分之一 + curve_y_start = int(height * (1 - curve_length_ratio) / 2) + curve_y_end = int(height * (1 + curve_length_ratio) / 2) + curve_length = curve_y_end - curve_y_start + + # 生成曲线偏移量 + y_offsets = generate_curve(curve_length, curve_amplitude) + + for i, y_offset in enumerate(y_offsets): + y_pos = curve_y_start + i + + # 左侧小图片的掩码 + left_index = (y_pos // cell_height) * num_cols + col - 1 + if 0 <= left_index < len(masks): + masks[left_index][y_pos, x_line - y_offset:] = 0 # 将右侧区域设为黑色 + + # 右侧小图片的掩码 + right_index = (y_pos // cell_height) * num_cols + col + if 0 <= right_index < len(masks): + masks[right_index][y_pos, :x_line + y_offset] = 0 # 将左侧区域设为黑色 + +# 创建水平曲线分割线（行之间） +for row in range(1, num_rows): + y_line = row * cell_height + + # 曲线应用于水平分割线的中间三分之一 + curve_x_start = int(width * (1 - curve_length_ratio) / 2) + curve_x_end = int(width * (1 + curve_length_ratio) / 2) + curve_length = curve_x_end - curve_x_start + + # 生成曲线偏移量 + x_offsets = generate_curve(curve_length, curve_amplitude) + + for i, x_offset in enumerate(x_offsets): + x_pos = curve_x_start + i + + # 上方小图片的掩码 + top_index = (row - 1) * num_cols + (x_pos // cell_width) + if 0 <= top_index < len(masks): + masks[top_index][y_line - x_offset:, x_pos] = 0 # 将下方区域设为黑色 + + # 下方小图片的掩码 + bottom_index = row * num_cols + (x_pos // cell_width) + if 0 <= bottom_index < len(masks): + masks[bottom_index][:y_line + x_offset, x_pos] = 0 # 将上方区域设为黑色 + +# 应用掩码并保存小图片 +for index, mask in enumerate(masks): + img_part = cv2.bitwise_and(image, image, mask=mask) + output_path = os.path.join(output_dir, f'output{index + 1}.png') + cv2.imwrite(output_path, img_part) + +print(f"图片已成功切割成 {num_rows * num_cols} 张，并保存到 'output' 目录下。") diff --git a/split17.py b/split17.py new file mode 100644 index 0000000..688b8b5 --- /dev/null +++ b/split17.py @@ -0,0 +1,42 @@ +import matplotlib.pyplot as plt +import numpy as np + +# 定义 x 的范围 +x = np.linspace(0, 3, 500) + +# 初始化 y 为 0 +y = np.zeros_like(x) + +# 定义Ω形状的关键参数 +r = 0.6 # 半圆半径 +xc = 1.5 # 圆心 x 坐标 +yc = 0.6 # 圆心 y 坐标（上移半圆） + +# 定义中间部分的范围（圆弧范围） +circle_mask = (x >= xc - r) & (x <= xc + r) +x_circle = x[circle_mask] + +# 计算圆弧顶部的 y 值 +y[circle_mask] = yc + np.sqrt(r**2 - (x_circle - xc)**2) + +# 定义两侧曲线的范围 +left_curve_mask = (x >= 1) & (x < xc - r) +right_curve_mask = (x > xc + r) & (x <= 2) + +# 左侧曲线（底部向右偏移） +x_left = x[left_curve_mask] +x_shift = 0.2 # 底部向右偏移的幅度 +y[left_curve_mask] = (yc - r) * (1 - ((x_left - (1 + x_shift * (1 - (x_left - 1) / (xc - r - 1)))) / (xc - r - (1 + x_shift)))**2) + +# 右侧曲线（保持不变） +x_right = x[right_curve_mask] +y[right_curve_mask] = (yc - r) * (1 - ((x_right - 2) / (xc + r - 2))**2) + +# 绘制图形 +plt.plot(x, y, linewidth=2) +plt.axhline(0, color='gray', linewidth=0.5, linestyle='--') +plt.title('左侧底部向右偏移的Ω形状') +plt.xlabel('X 轴') +plt.ylabel('Y 轴') +plt.axis('equal') # 确保 x 和 y 轴的比例相同 +plt.show() diff --git a/split18.py b/split18.py new file mode 100644 index 0000000..a5ea1fd --- /dev/null +++ b/split18.py @@ -0,0 +1,42 @@ +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.patches import Arc + +# Set parameters for blue quarter-circle +blue_radius = 1 +theta_start = 90 +theta_end = 225 +theta = np.linspace(theta_start, theta_end, 300) +theta_rad = np.deg2rad(theta) +x = blue_radius * np.cos(theta_rad) +y = blue_radius * np.sin(theta_rad) +end_point_x = x[-1] +end_point_y = y[-1] + +# Set parameters for red semi-circle +red_diameter = blue_radius / 6 +red_radius = red_diameter / 2 +red_center_x = end_point_x +red_center_y = end_point_y - red_radius +red_rotation = 45 # New parameter: rotation angle in degrees + +# Create the plot +fig, ax = plt.subplots(figsize=(8, 8)) + +# Plot the blue quarter-circle +ax.plot(x, y, color='blue', linewidth=2, label='Blue Quarter-Circle') + +# Plot the rotated red semi-circle +red_arc = Arc((red_center_x, red_center_y), red_diameter, red_diameter, + angle=red_rotation, theta1=270, theta2=90, edgecolor='red', linewidth=2, label='Rotated Red Semi-Circle') +ax.add_patch(red_arc) + +# Set equal aspect ratio and remove axes +ax.set_aspect('equal', adjustable='box') +ax.axis('off') + +# Add legend +ax.legend() + +# Display the plot +plt.show() \ No newline at end of file diff --git a/tower.png b/tower.png new file mode 100644 index 0000000..1440b78 Binary files /dev/null and b/tower.png differ diff --git a/view.py b/view.py new file mode 100644 index 0000000..dc27291 --- /dev/null +++ b/view.py @@ -0,0 +1,18 @@ +import matplotlib.pyplot as plt +import os +from PIL import Image + +# 可视化函数 +def visualize_pieces(output_dir, rows=10, cols=10): + fig, axes = plt.subplots(rows, cols, figsize=(10, 10)) + for row in range(rows): + for col in range(cols): + piece_path = os.path.join(output_dir, f"piece_{row}_{col}.png") + piece = Image.open(piece_path) + axes[row, col].imshow(piece) + axes[row, col].axis('off') + plt.tight_layout() + plt.show() + +# 可视化拼图效果 +visualize_pieces("output_gigs")