如何只抓取 YOLO 边界框的上半部分？

问题描述

我编写了一个代码来查找边界框内白色像素的百分比。所以基本上代码给出了 % 的白色像素，我用它来进一步将 YOLO 对象检测中的“人”类对象分类为穿白衬衫的人而不是穿白衬衫的人。现在我正在抓取边界框坐标并计算白色像素。如果此人穿着白色裤子/边界框的下半部分，则会出现问题。有没有办法将边界框分成两半，只抓取边界框的上半部分。这是我的代码：

            region = frame[x:x+w,y:y+h] #region is my bounding Box of class Person grabbed from video frame
            #print(region)
                # Here,you define your target color as
                # a tuple of three values: RGB
            if region.size:

                white = [255,255,255]
                #print("hfc")
                # You define an interval that covers the values
                # in the tuple and are below and above them by 15
                diff = 15
                # Be aware that opencv loads image in BGR format,# that's why the color values have been adjusted here:
                boundaries = [([white[2],white[1]-diff,white[0]-diff],[white[2]+diff,white[1]+diff,white[0]+diff])]

                # Scale your BIG image into a small one:
                scalePercent = 0.3

                # Calculate the new dimensions
                width = int(region.shape[1] * scalePercent)
                height = int(region.shape[0] * scalePercent)
                newSize = (width,height) 
            

            
                            # for each range in your boundary list:
                for (lower,upper) in boundaries:

             #    # You get the lower and upper part of the interval:
                    lower = np.array([0,255],dtype=np.uint8)
                    upper =  np.array([255,dtype=np.uint8)


                        # cv2.inRange is used to binarize (i.e.,render in white/black) an image
                        # All the pixels that fall inside your interval [lower,uipper] will be white
                        # All the pixels that do not fall inside this interval will
                        # be rendered in black,for all three channels:
                    mask = cv2.inRange(region,lower,upper)

                   

                        # Now,you AND the mask and the input image
                        # All the pixels that are white in the mask will
                        # survive the AND operation,all the black pixels
                        # will remain black
                    output = cv2.bitwise_and(region,region,mask=mask)

                

                    # You can use the mask to count the number of white pixels.
                    # Divide by the image size and you got the
                    # percentage of white pixels in the original image:
                    ratio_white = cv2.countNonZero(mask)/(region.size/3)

                    # This is the color percent calculation,considering the resize I did earlier.
                    colorPercent = (ratio_white * 100) / scalePercent

                    # Print the color percent,use 2 figures past the decimal point
                    print('white pixel percentage:',np.round(colorPercent,2))
                

解决方法

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