# -*- coding: utf-8 -*- # @Time : 2024/6/26 下午5:31 # @Author : TG # @File : passion_fruit_rgb.py # @Software: PyCharm import os import cv2 import numpy as np import argparse import logging from config import Config as setting class Passion_fruit: def __init__(self, hue_value=setting.hue_value, hue_delta=setting.hue_delta, value_target=setting.value_target, value_delta=setting.value_delta): # 初始化常用参数 self.hue_value = hue_value self.hue_delta = hue_delta self.value_target = value_target self.value_delta = value_delta def create_mask(self, hsv_image): # 创建H通道阈值掩码 lower_hue = np.array([self.hue_value - self.hue_delta, 0, 0]) upper_hue = np.array([self.hue_value + self.hue_delta, 255, 255]) hue_mask = cv2.inRange(hsv_image, lower_hue, upper_hue) # 创建V通道排除中心值的掩码 lower_value_1 = np.array([0, 0, 0]) upper_value_1 = np.array([180, 255, self.value_target - self.value_delta]) lower_value_2 = np.array([0, 0, self.value_target + self.value_delta]) upper_value_2 = np.array([180, 255, 255]) value_mask_1 = cv2.inRange(hsv_image, lower_value_1, upper_value_1) value_mask_1 = cv2.bitwise_not(value_mask_1) value_mask_2 = cv2.inRange(hsv_image, lower_value_2, upper_value_2) value_mask = cv2.bitwise_and(value_mask_1, value_mask_2) # 合并H通道和V通道掩码 return cv2.bitwise_and(hue_mask, value_mask) def apply_morphology(self, mask): # 应用形态学操作 kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) return cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel) def find_largest_component(self, mask): if mask is None or mask.size == 0 or np.all(mask == 0): logging.warning("RGB 图像为空或全黑，返回一个全黑RGB图像。") return np.zeros((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands), dtype=np.uint8) \ if mask is None else np.zeros_like(mask) # 寻找最大连通组件 num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(mask, 4, cv2.CV_32S) if num_labels < 2: return None # 没有找到显著的组件 max_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA]) # 跳过背景 return (labels == max_label).astype(np.uint8) * 255 def draw_contours_on_image(self, original_image, mask_image): """ 在原图上绘制轮廓 :param original_image: 原图的NumPy数组 :param mask_image: 轮廓mask的NumPy数组 :return: 在原图上绘制轮廓后的图像 """ # 确保mask_image是二值图像 _, binary_mask = cv2.threshold(mask_image, 127, 255, cv2.THRESH_BINARY) # 查找mask图像中的轮廓 contours, _ = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 在原图上绘制轮廓 cv2.drawContours(original_image, contours, -1, (0, 255, 0), 2) return original_image def bitwise_and_rgb_with_binary(self, rgb_img, bin_img): ''' 将 RGB 图像与二值图像进行按位与操作，用于将二值区域应用于原始图像。 :param rgb_img: 原始 RGB 图像 :param bin_img: 二值图像 :return: 按位与后的结果图像 ''' # 检查 RGB 图像是否为空或全黑 if rgb_img is None or rgb_img.size == 0 or np.all(rgb_img == 0): logging.warning("RGB 图像为空或全黑，返回一个全黑RGB图像。") return np.zeros((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands), dtype=np.uint8) \ if rgb_img is None else np.zeros_like(rgb_img) # 检查二值图像是否为空或全黑 if bin_img is None or bin_img.size == 0 or np.all(bin_img == 0): logging.warning("二值图像为空或全黑，返回一个全黑RGB图像。") return np.zeros((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands), dtype=np.uint8) \ if bin_img is None else np.zeros_like(bin_img) # 转换二值图像为三通道 try: bin_img_3channel = cv2.cvtColor(bin_img, cv2.COLOR_GRAY2BGR) except cv2.error as e: logging.error(f"转换二值图像时发生错误: {e}") return np.zeros_like(rgb_img) # 进行按位与操作 try: result = cv2.bitwise_and(rgb_img, bin_img_3channel) except cv2.error as e: logging.error(f"执行按位与操作时发生错误: {e}") return np.zeros_like(rgb_img) return result def fill_holes(bin_img): img_filled = bin_img.copy() height, width = bin_img.shape mask = np.zeros((height + 2, width + 2), np.uint8) cv2.floodFill(img_filled, mask, (0, 0), 255) img_filled_inv = cv2.bitwise_not(img_filled) img_filled = cv2.bitwise_or(bin_img, img_filled) img_defect = img_filled_inv[:height, :width] return img_filled, img_defect def contour_process(image_array): # 检查图像是否为空或全黑 if image_array is None or image_array.size == 0 or np.all(image_array == 0): logging.warning("输入的图像为空或全黑，返回一个全黑图像。") return np.zeros_like(image_array) if image_array is not None else np.zeros((100, 100), dtype=np.uint8) # 应用中值滤波 image_filtered = cv2.medianBlur(image_array, 5) # 形态学闭操作 kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (15, 15)) image_closed = cv2.morphologyEx(image_filtered, cv2.MORPH_CLOSE, kernel) # 查找轮廓 contours, _ = cv2.findContours(image_closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 创建空白图像以绘制轮廓 image_contours = np.zeros_like(image_array) # 进行多边形拟合并填充轮廓 for contour in contours: epsilon = 0.001 * cv2.arcLength(contour, True) approx = cv2.approxPolyDP(contour, epsilon, True) if cv2.contourArea(approx) > 100: # 仅处理较大的轮廓 cv2.drawContours(image_contours, [approx], -1, (255, 255, 255), -1) return image_contours def extract_green_pixels_cv(image): """ 使用 OpenCV 提取图像中的绿色像素，并可选择保存结果图像。 参数: image_path (str): 输入图像的文件路径。 save_path (str, optional): 输出图像的保存路径，若提供此参数，则保存提取的绿色像素图像。 返回: 输出图像，绿色像素为白色，其他像素为黑色。 """ image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Define the HSV range for green lower_green = np.array([0, 100, 0]) upper_green = np.array([60, 180, 60]) # Convert the image to HSV hsv = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2HSV) # Create the mask mask = cv2.inRange(hsv, lower_green, upper_green) # Bitwise-AND mask and original image res = cv2.bitwise_and(image_rgb, image_rgb, mask=mask) # Convert result to BGR for display res_bgr = cv2.cvtColor(res, cv2.COLOR_RGB2BGR) return mask def pixel_comparison(defect, mask): """ 比较两幅图像的像素值，如果相同则赋值为0，不同则赋值为255。 参数: defect_path (str): 第一幅图像的路径。 mask_path (str): 第二幅图像的路径。 save_path (str, optional): 结果图像的保存路径。 返回: numpy.ndarray: 处理后的图像数组。 """ # 确保图像是二值图像 _, defect_binary = cv2.threshold(defect, 127, 255, cv2.THRESH_BINARY) _, mask_binary = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY) # 执行像素比较 green_img = np.where(defect_binary == mask_binary, 0, 255).astype(np.uint8) return green_img def main(): parser = argparse.ArgumentParser(description='Process some integers.') parser.add_argument('--dir_path', type=str, default=r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\tg\test', 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() pf = Passion_fruit() for img_file in os.listdir(args.dir_path): if img_file.endswith('.bmp'): img_path = os.path.join(args.dir_path, img_file) img = cv2.imread(img_path) cv2.imshow('img', img) hsv_image = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) cv2.imshow('hsv', hsv_image) combined_mask = pf.create_mask(hsv_image) cv2.imshow('combined_mask1', combined_mask) combined_mask = pf.apply_morphology(combined_mask) cv2.imshow('combined_mask2', combined_mask) max_mask = pf.find_largest_component(combined_mask) cv2.imshow('max_mask', max_mask) filled_img, defect = fill_holes(max_mask) cv2.imshow('filled_img', filled_img) cv2.imshow('defect', defect) contour_mask = contour_process(max_mask) cv2.imshow('contour_mask', contour_mask) fore = pf.bitwise_and_rgb_with_binary(img, contour_mask) cv2.imshow('fore', fore) mask = extract_green_pixels_cv(fore) cv2.imshow('mask', mask) green_img = pixel_comparison(defect, mask) cv2.imshow('green_img', green_img) green_percentage = np.sum(green_img == 255) / np.sum(contour_mask == 255) green_percentage = round(green_percentage, 2) print(np.sum(green_img == 255)) print(np.sum(contour_mask == 255)) print(green_percentage) edge = pf.draw_contours_on_image(img, contour_mask) cv2.imshow('edge', edge) org_defect = pf.bitwise_and_rgb_with_binary(edge, max_mask) cv2.imshow('org_defect', org_defect) cv2.waitKey(0) cv2.destroyAllWindows() if __name__ == '__main__': main()