import cv2 import numpy as np import os import argparse # from svm import predict_image_array, load_model #提取西红柿，使用S+L的图像 def extract_s_l(image_path): image = cv2.imread(image_path) hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) lab = cv2.cvtColor(image, cv2.COLOR_BGR2Lab) s_channel = hsv[:,:,1] l_channel = lab[:,:,0] result = cv2.add(s_channel, l_channel) return result def find_reflection(image_path, threshold=190): # 读取图像 image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE) # 应用阈值分割 _, reflection = cv2.threshold(image, threshold, 255, cv2.THRESH_BINARY) return reflection def otsu_threshold(image): # 将图像转换为灰度图像 # gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # 使用Otsu阈值分割 _, binary = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) return binary # 提取花萼，使用G-R的图像 def extract_g_r(image): # image = cv2.imread(image_path) g_channel = image[:,:,1] r_channel = image[:,:,2] result = cv2.subtract(cv2.multiply(g_channel, 1.5), r_channel) return result #提取西红柿，使用R-B的图像 def extract_r_b(image_path): image = cv2.imread(image_path) r_channel = image[:,:,2] b_channel = image[:,:,0] result = cv2.subtract(r_channel, b_channel) return result def extract_r_g(image_path): image = cv2.imread(image_path) r_channel = image[:,:,2] g_channel = image[:,:,1] result = cv2.subtract(r_channel, g_channel) return result def threshold_segmentation(image, threshold, color=255): _, result = cv2.threshold(image, threshold, color, cv2.THRESH_BINARY) return result def bitwise_operation(image1, image2, operation='and'): if operation == 'and': result = cv2.bitwise_and(image1, image2) elif operation == 'or': result = cv2.bitwise_or(image1, image2) else: raise ValueError("operation must be 'and' or 'or'") return result def largest_connected_component(bin_img): # 使用connectedComponentsWithStats函数找到连通区域 num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(bin_img, connectivity=8) # 找到最大的连通区域（除了背景） largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA]) # 创建一个新的二值图像，只显示最大的连通区域 new_bin_img = np.zeros_like(bin_img) new_bin_img[labels == largest_label] = 255 return new_bin_img def close_operation(bin_img, kernel_size=(5, 5)): kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size) closed_img = cv2.morphologyEx(bin_img, cv2.MORPH_CLOSE, kernel) return closed_img def open_operation(bin_img, kernel_size=(5, 5)): kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size) opened_img = cv2.morphologyEx(bin_img, cv2.MORPH_OPEN, kernel) return opened_img def draw_tomato_edge(original_img, bin_img): bin_img_processed = close_operation(bin_img, kernel_size=(15, 15)) # cv2.imshow('Close Operation', bin_img_processed) # bin_img_processed = open_operation(bin_img_processed, kernel_size=(19, 19)) # cv2.imshow('Open Operation', bin_img_processed) # 现在使用处理后的bin_img_processed查找轮廓 contours, _ = cv2.findContours(bin_img_processed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 如果没有找到轮廓，直接返回原图 if not contours: return original_img # 找到最大轮廓 max_contour = max(contours, key=cv2.contourArea) # 多边形近似的精度调整 epsilon = 0.0006 * cv2.arcLength(max_contour, True) # 可以调整这个值 approx = cv2.approxPolyDP(max_contour, epsilon, True) # 绘制轮廓 cv2.drawContours(original_img, [approx], -1, (0, 255, 0), 3) mask = np.zeros_like(bin_img) # 使用白色填充最大轮廓 cv2.drawContours(mask, [max_contour], -1, (255), thickness=cv2.FILLED) return original_img, mask def draw_tomato_edge_convex_hull(original_img, bin_img): bin_img_blurred = cv2.GaussianBlur(bin_img, (5, 5), 0) contours, _ = cv2.findContours(bin_img_blurred, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) if not contours: return original_img max_contour = max(contours, key=cv2.contourArea) hull = cv2.convexHull(max_contour) cv2.drawContours(original_img, [hull], -1, (0, 255, 0), 3) return original_img # 得到完整的西红柿二值图像，除了绿色花萼 def fill_holes(bin_img): # 复制 bin_img 到 img_filled img_filled = bin_img.copy() # 获取图像的高度和宽度 height, width = bin_img.shape # 创建一个掩码，比输入图像大两个像素点 mask = np.zeros((height + 2, width + 2), np.uint8) # 使用 floodFill 函数填充黑色区域 cv2.floodFill(img_filled, mask, (0, 0), 255) # 反转填充后的图像 img_filled_d = cv2.bitwise_not(img_filled) # 使用 bitwise_or 操作合并原图像和填充后的图像 img_filled = cv2.bitwise_or(bin_img, img_filled) # 裁剪 img_filled 和 img_filled_d 到与 bin_img 相同的大小 # img_filled = img_filled[:height, :width] img_filled_d = img_filled_d[:height, :width] return img_filled, img_filled_d def bitwise_and_rgb_with_binary(rgb_img, bin_img): # 将二值图像转换为三通道图像 bin_img_3channel = cv2.cvtColor(bin_img, cv2.COLOR_GRAY2BGR) # 使用 bitwise_and 操作合并 RGB 图像和二值图像 result = cv2.bitwise_and(rgb_img, bin_img_3channel) return result def extract_max_connected_area(image_path, lower_hsv, upper_hsv): # 读取图像 image = cv2.imread(image_path) # 将图像从BGR转换到HSV hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # 使用阈值获取指定区域的二值图像 mask = cv2.inRange(hsv, lower_hsv, upper_hsv) # 找到二值图像的连通区域 num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(mask, connectivity=8) # 找到最大的连通区域（除了背景） largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA]) # 创建一个新的二值图像，只显示最大的连通区域 new_bin_img = np.zeros_like(mask) new_bin_img[labels == largest_label] = 255 # 复制 new_bin_img 到 img_filled img_filled = new_bin_img.copy() # 获取图像的高度和宽度 height, width = new_bin_img.shape # 创建一个掩码，比输入图像大两个像素点 mask = np.zeros((height + 2, width + 2), np.uint8) # 使用 floodFill 函数填充黑色区域 cv2.floodFill(img_filled, mask, (0, 0), 255) # 反转填充后的图像 img_filled_inv = cv2.bitwise_not(img_filled) # 使用 bitwise_or 操作合并原图像和填充后的图像 img_filled = cv2.bitwise_or(new_bin_img, img_filled_inv) return img_filled def main(): parser = argparse.ArgumentParser(description='Process some integers.') parser.add_argument('--dir_path', type=str, default=r'D:\project\supermachine--tomato-passion_fruit\20240419RGBtest2\data', help='the directory path of images') parser.add_argument('--threshold_s_l', type=int, default=180, help='the threshold for s_l') parser.add_argument('--threshold_r_b', type=int, default=15, help='the threshold for r_b') args = parser.parse_args() for img_file in os.listdir(args.dir_path): if img_file.endswith('.bmp'): img_path = os.path.join(args.dir_path, img_file) s_l = extract_s_l(img_path) otsu_thresholded = otsu_threshold(s_l) img_fore = bitwise_and_rgb_with_binary(cv2.imread(img_path), otsu_thresholded) img_fore_defect = extract_g_r(img_fore) img_fore_defect = threshold_segmentation(img_fore_defect, args.threshold_r_b) # cv2.imshow('img_fore_defect', img_fore_defect) thresholded_s_l = threshold_segmentation(s_l, args.threshold_s_l) new_bin_img = largest_connected_component(thresholded_s_l) zhongggggg = cv2.bitwise_or(new_bin_img, cv2.imread('defect_mask.bmp', cv2.IMREAD_GRAYSCALE)) cv2.imshow('zhongggggg', zhongggggg) new_otsu_bin_img = largest_connected_component(otsu_thresholded) filled_img, defect = fill_holes(new_bin_img) defect = bitwise_and_rgb_with_binary(cv2.imread(img_path), defect) cv2.imshow('defect', defect) edge, mask = draw_tomato_edge(cv2.imread(img_path), new_bin_img) org_defect = bitwise_and_rgb_with_binary(edge, new_bin_img) fore = bitwise_and_rgb_with_binary(cv2.imread(img_path), mask) fore_g_r_t = threshold_segmentation(extract_g_r(fore), 20) fore_g_r_t_ture = bitwise_and_rgb_with_binary(cv2.imread(img_path), fore_g_r_t) cv2.imwrite('defect_big.bmp', fore_g_r_t_ture) res = cv2.bitwise_or(new_bin_img, fore_g_r_t) white = find_reflection(img_path) # SVM预测 # 加载模型 # model, scaler = load_model('/Users/xs/PycharmProjects/super-tomato/svm_green.joblib') # 对图像进行预测 # predicted_mask = predict_image_array(image, model, scaler) cv2.imshow('white', white) cv2.imshow('fore', fore) cv2.imshow('fore_g_r_t', fore_g_r_t) cv2.imshow('mask', mask) print('mask', mask.shape) print('filled', filled_img.shape) print('largest', new_bin_img.shape) print('rp', org_defect.shape) cv2.imshow('res', res) # lower_hsv = np.array([19, 108, 15]) # upper_hsv = np.array([118, 198, 134]) # max_connected_area = extract_max_connected_area(img_path, lower_hsv, upper_hsv) # cv2.imshow('Max Connected Area', max_connected_area) # 显示原始图像 original_img = cv2.imread(img_path) cv2.imshow('Original', original_img) cv2.imshow('thresholded_s_l', thresholded_s_l) cv2.imshow('Largest Connected Component', new_bin_img) cv2.imshow('Filled', filled_img) cv2.imshow('Defect', defect) cv2.imshow('Org_defect', org_defect) cv2.imshow('otsu_thresholded', new_otsu_bin_img) #显示轮廓 cv2.imshow('Edge', edge) # 等待用户按下任意键 cv2.waitKey(0) # 关闭所有窗口 cv2.destroyAllWindows() if __name__ == '__main__': main()