基于OpenCV和Python实现柚子缺陷识别检测源码+详细代码注释.zip

import cv2 import numpy as np class Detect(object): # 将RGB图像转为HSI图像 返回h通道，s通道，i通道图像以及hsi图像 def rgb2hsi(self, image): b, g, r = cv2.split(image) # 读取通道 r = r / 255.0 # 归一化 g = g / 255.0 b = b / 255.0 eps = 1e-6 # 防止除零 img_i = (r + g + b) / 3 # I分量 img_h = np.zeros(r.shape, dtype=np.float32) img_s = np.zeros(r.shape, dtype=np.float32) min_rgb = np.zeros(r.shape, dtype=np.float32) # 获取RGB中最小值 min_rgb = np.where((r <= g) & (r <= b), r, min_rgb) min_rgb = np.where((g <= r) & (g <= b), g, min_rgb) min_rgb = np.where((b <= g) & (b <= r), b, min_rgb) img_s = 1 - 3 * min_rgb / (r + g + b + eps) # S分量 num = ((r - g) + (r - b)) / 2 den = np.sqrt((r - g) ** 2 + (r - b) * (g - b)) theta = np.arccos(num / (den + eps)) img_h = np.where((b - g) > 0, 2 * np.pi - theta, theta) # H分量 img_h = np.where(img_s == 0, 0, img_h) img_h = img_h / (2 * np.pi) # 归一化 temp_s = img_s - np.min(img_s) temp_i = img_i - np.min(img_i) img_s = temp_s / np.max(temp_s) img_i = temp_i / np.max(temp_i) image_hsi = cv2.merge([img_h, img_s, img_i]) return img_h, img_s, img_i, image_hsi def nothing(self, x): pass # 图像预处理与轮廓获取 def findcont(self, image): image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) reval_T_2, dst_Tri_2 = cv2.threshold(image, 10, 255, cv2.THRESH_BINARY_INV) dst_Tri_2 = ~dst_Tri_2 # 黑白像素转置 contours, hierarchy = cv2.findContours(dst_Tri_2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) return contours, hierarchy # 获取最大面积轮廓的凸包并返回 def get_hull(self, contours): # 找到最大面积的轮廓 area = [] for i in range(len(contours)): area.append(cv2.contourArea(contours[i])) max_index = np.argmax(np.array(area)) # 获取最大面积的凸包[hull] hull = cv2.convexHull(contours[max_index]) return hull # 获取凸包hull的roi区域并返回 def return_roi(self, height, width, hull): roi = np.zeros((height, width), dtype=np.uint8) roi.fill(128) cv2.fillPoly(roi, [hull], (255), 8, 0) return roi # 获取图像中黑色像素的个数 def get_blackNum(self, height, width, image): # 建立一个与图像尺寸相同的全零数组 npim = np.zeros((height, width), dtype=np.uint8) # 将图像3个通道相加赋值给空数组 npim[:] = image[:, :] cont = len(npim[npim == 0]) return cont # 判断是否是好果子 def judge_ifgood(self, cont, hull_area, image): font = cv2.FONT_HERSHEY_SIMPLEX if (cont / hull_area <= 0.2): cv2.putText(image, "This is good", (0, 250), font, 3, (0, 255, 0), 15, cv2.FONT_HERSHEY_SCRIPT_SIMPLEX) else: cv2.putText(image, "This is bad", (0, 250), font, 3, (0, 255, 0), 15, cv2.FONT_HERSHEY_SCRIPT_SIMPLEX) return image