我目前正在处理从无人机拍摄的图像中裁剪太阳能板（附件中有样本图像）。我尝试使用轮廓检测方法，但结果并不理想。有些太阳能板没有被检测到，我在这里遇到了困难。我该如何继续进行？请帮助我解决这个问题。

谢谢，

样本代码：

import cv2import numpy as npimg = cv2.imread('D:\\SolarPanel Images\\solarpanel.jpg')gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)blur = cv2.GaussianBlur(gray,(5,5),0)edges = cv2.Canny(blur,100,200)    th3 = cv2.adaptiveThreshold(edges,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)im2, contours, hierarchy = cv2.findContours(th3, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)print("Len of contours",len(contours)try: hierarchy = hierarchy[0]except: hierarchy = []height, width,  = edges.shapemin_x, min_y = width, heightmax_x = max_y = 0# computes the bounding box for the contour, and draws it on the image,for contour, hier in zip(contours, hierarchy):    area = cv2.contourArea(contour)    if area > 10000 and area < 250000:        (x,y,w,h) = cv2.boundingRect(contour)        min_x, max_x = min(x, min_x), max(x+w, max_x)        min_y, max_y = min(y, min_y), max(y+h, max_y)        if w > 80 and h > 80:            cv2.rectangle(img, (x,y), (x+w,y+h), (255, 0, 0), 2)            cv2.imshow('cont imge', img)            cv2.waitKey(0)

回答：

要在图像中找到轮廓，特别是当重要对象与背景明显不同时，可以尝试将图像转换为HSV格式，然后进行轮廓检测。我做了以下操作：

import cv2import numpy as npimg = cv2.imread('panel.jpg')hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) ret,thresh1 = cv2.threshold(hsv[:,:,0],100,255,cv2.THRESH_BINARY)im2, contours, hierarchy = cv2.findContours(thresh1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)try: hierarchy = hierarchy[0]except: hierarchy = []for contour, hier in zip(contours, hierarchy):    area = cv2.contourArea(contour)    if area > 10000 and area < 250000:       rect = cv2.minAreaRect(contour)       box = cv2.boxPoints(rect)       box = np.int0(box)       cv2.drawContours(img,[box],0,(0,0,255),2)       cv2.imshow('cont imge', img)       cv2.waitKey(0)cv2.imwrite("result.jpg",img)

结果：