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import cv2
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import numpy as np
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import concurrent.futures
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def process_image(image, new_background_color):
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# 如果原图尺寸太大，可以进行图像压缩
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scale_percent = 100 # 压缩比例（100%）
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# 计算压缩后的图像尺寸
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width = int(image.shape[1] * scale_percent / 100)
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height = int(image.shape[0] * scale_percent / 100)
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dim = (width, height)
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# 调整图像尺寸
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resized_image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
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# 进行图像处理
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result = grabcut_segmentation(resized_image, new_background_color)
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# 返回处理结果
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return result
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def grabcut_segmentation(image, new_background_color):
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# 创建掩膜
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mask = np.zeros(image.shape[:2], np.uint8)
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# 定义GrabCut算法所需的前景和背景模型
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bgd_model = np.zeros((1, 65), np.float64)
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fgd_model = np.zeros((1, 65), np.float64)
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# 定义矩形区域，包含前景对象（根据实际需要调整矩形位置和大小）
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height, width = image.shape[:2]
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rect = (10, 10, width - 10, height - 10)
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# 执行GrabCut算法, 通过调整num_iterations参数来控制迭代次数，以平衡速度和准确性
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num_iterations = 5
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cv2.grabCut(image, mask, rect, bgd_model, fgd_model, num_iterations, cv2.GC_INIT_WITH_RECT)
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# 创建前景和背景掩膜
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mask_foreground = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8')
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mask_background = 1 - mask_foreground
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# 创建纯色背景图像
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background = np.zeros_like(image, np.uint8)
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background[:] = new_background_color
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# 将前景放在白色背景上
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foreground = cv2.bitwise_and(image, image, mask=mask_foreground)
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result = cv2.add(foreground, cv2.bitwise_and(background, background, mask=mask_background))
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return result
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def grabcut(image_p, color):
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# 定义常用背景颜色，注意OpenCV 颜色通道顺序为 B G R
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blue_BGR = (240, 167, 2) # 蓝色
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white_BGR = (255, 255, 255) # 白色
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red_BGR = (27, 0, 217) # 红色
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blue_white_BGR = (196, 146, 52) # 蓝白渐变色
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light_gray_BGR = (210, 210, 210) # 浅灰色
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if(color == 'blue'):
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color = blue_BGR
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if(color == 'white'):
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color = white_BGR
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if(color == 'red'):
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color = red_BGR
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if(color == 'bw'):
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color = blue_white_BGR
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if(color == 'lg'):
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color = light_gray_BGR
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new_background_color = color # 新的背景色
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# 原图像和输出图像
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image_path = image_p
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output_path = 'output.jpg'
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# 读取原图像
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image = cv2.imread(image_path)
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# 使用多线程处理单张照片
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with concurrent.futures.ThreadPoolExecutor() as executor:
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result = executor.submit(process_image, image, new_background_color)
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# 获取处理结果
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result = result.result()
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# 显示和保存处理结果
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cv2.imshow('Result', result)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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# 保存结果图像
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cv2.imwrite(output_path, result) |
