Question

我正在开展一个项目，我必须在叶子上发现一些疾病。为此，我要检测显着的特征，即叶子（在我的情况下）并删除图像的背景。我有以下代码。

    import cv2, sys
    import numpy as np

    def backproject(source, target, levels = 2, scale = 1):
            hsv = cv2.cvtColor(source,  cv2.COLOR_BGR2HSV)
            hsvt = cv2.cvtColor(target, cv2.COLOR_BGR2HSV)
            # calculating object histogram
            roihist = cv2.calcHist([hsv],[0, 1], None, [levels, levels], [0, 180, 0, 256] )

            # normalize histogram and apply backprojection
            cv2.normalize(roihist,roihist,0,255,cv2.NORM_MINMAX)
            dst = cv2.calcBackProject([hsvt],[0,1],roihist,[0,180,0,256], scale)
            return dst

    def saliency_by_backprojection(img):
            cv2.pyrMeanShiftFiltering(img, 2, 10, img, 4)

            backproj = np.uint8(backproject(img, img, levels = 2))
            cv2.normalize(backproj,backproj,0,255,cv2.NORM_MINMAX)
            saliencies = [backproj, backproj, backproj]
            saliency = cv2.merge(saliencies)

            cv2.pyrMeanShiftFiltering(saliency, 20, 200, saliency, 2)
            saliency = cv2.cvtColor(saliency, cv2.COLOR_BGR2GRAY)
            cv2.equalizeHist(saliency, saliency)

            return 255-saliency

    def saliency_map(img):
            saliency_hsv = saliency_by_backprojection(img * 1)
            saliency = saliency_hsv
            (T, saliency) = cv2.threshold(saliency, 200, 255, cv2.THRESH_BINARY)
            return saliency

    def largest_contours_rect(saliency):
            contours, hierarchy = cv2.findContours(saliency * 1,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
            contours = sorted(contours, key = cv2.contourArea)
            return cv2.boundingRect(contours[-1])

    def refine_saliency_with_grabcut(img, saliency):
            rect = largest_contours_rect(saliency)
            bgdmodel = np.zeros((1, 65),np.float64)
            fgdmodel = np.zeros((1, 65),np.float64)
            saliency[np.where(saliency > 0)] = cv2.GC_FGD
            mask = saliency
            cv2.grabCut(img, mask, rect, bgdmodel, fgdmodel, 1, cv2.GC_INIT_WITH_RECT)
            mask = np.where((mask==2)|(mask==0),0,1).astype('uint8')
            return mask

    def backprojection_saliency(img):
            saliency = saliency_map(img)
            mask = refine_saliency_with_grabcut(img, saliency)
            return mask

    if __name__ == "__main__":
            name = sys.argv[1].strip('k5.jpg')

            img = cv2.imread(sys.argv[1], 1)
            img = cv2.resize(img, (640/2, 480/2))
            mask = backprojection_saliency(img)
            segmentation = img*mask[:,:,np.newaxis]

            cv2.imshow("original", img)
            cv2.imshow("segmentation", segmentation)
            cv2.waitKey(-1)

由于我是Python和openCV的新手，我无法解决以下错误。

        Traceback (most recent call last):
         File "F:\FYP\Code\saliency-from-backproj-master\saliency.py", line 
      56, in <module>
            name = sys.argv[1].strip('k5.jpg')
           IndexError: list index out of range

为什么会这样？

Answer 1

您没有提供任何命令行参数。
argv变量包含传递给程序的命令行参数。与python your_script.py verbose print时一样，argv变量包含['your_script.py', 'verbose', 'print']。
但是，如果您只提供python your_script.py argv包含['your_script.py']（只有一个元素argv [0]）。因此，致电argv[1]是IndexError。

sys.agv上的Python文档：

<强> sys.argv中
传递给Python脚本的命令行参数列表。 argv [0]是脚本名称（无论这是否是完整路径名，它依赖于操作系统）。如果使用解释器的-c命令行选项执行命令，则将argv [0]设置为字符串'-c'。如果没有脚本名称传递给Python解释器，则argv [0]为空字符串。

sys.argv [1] .strip（）python

1 个答案: