93 lines
2.4 KiB
Python
93 lines
2.4 KiB
Python
import cv2
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import numpy as np
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from PIL import Image
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import matplotlib.pyplot as plt
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# 读取图像
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image_path = 'jigsaw_mask.png' # 替换为您的图像路径
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image = cv2.imread(image_path)
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if image is None:
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raise ValueError("无法读取图像，请检查路径是否正确。")
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# 转换为RGB
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image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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# 转换为灰度图
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# 应用固定阈值进行二值化
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_, mask = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)
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# 进行形态学操作以连接断裂的线条
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# 定义较大的核
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kernel = np.ones((1,1), np.uint8)
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# 增加膨胀次数以连接断裂
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mask = cv2.dilate(mask, kernel, iterations=1)
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# 闭运算进一步连接
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mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=5)
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# 再次确保掩膜是二值的
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_, mask = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)
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# 创建 Alpha 通道
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alpha = cv2.bitwise_not(mask)
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# 确保 Alpha 通道只有0和255
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alpha = cv2.threshold(alpha, 127, 255, cv2.THRESH_BINARY)[1]
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# 创建纯白色背景
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white_bg = 255 * np.ones_like(image_rgb, dtype=np.uint8)
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# 合并 RGB 图像和 Alpha 通道
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final_rgb = cv2.bitwise_and(white_bg, white_bg, mask=cv2.bitwise_not(mask))
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image_rgba = cv2.cvtColor(final_rgb, cv2.COLOR_RGB2RGBA)
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image_rgba[:, :, 3] = alpha
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# 将 OpenCV 的 RGBA 图像转换为 Pillow Image 对象
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image_pil = Image.fromarray(image_rgba)
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# 转换为 NumPy 数组以进一步处理
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data = np.array(image_pil)
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# 分离颜色通道
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red, green, blue, alpha = data.T
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# 定义黑色的阈值范围
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black_threshold = 50 # 根据需要调整
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# 创建掩膜：黑色区域为True，其余为False
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black_areas = (red < black_threshold) & (green < black_threshold) & (blue < black_threshold)
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# 设置黑色区域的 Alpha 为0，非黑色区域的 Alpha 为255
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data[..., 3][black_areas.T] = 0
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data[..., 3][~black_areas.T] = 255
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# 将非黑色区域设置为纯白色
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data[..., 0:3][~black_areas.T] = [255, 255, 255]
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# 创建新的 Image 对象
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final_image = Image.fromarray(data)
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# 保存结果为 PNG（支持透明度）
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output_path = 'mask.png'
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final_image.save(output_path)
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print(f"已保存带纯白背景和透明部分的图像到 {output_path}")
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# 可选：显示图像
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plt.figure(figsize=(12,6))
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plt.subplot(1,2,1)
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plt.title('原图')
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plt.imshow(image_rgb)
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plt.axis('off')
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plt.subplot(1,2,2)
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plt.title('带纯白背景和透明部分的图像')
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plt.imshow(final_image)
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plt.axis('off')
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plt.show()
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