OpenCV findContours循环Python

Question

我很难让我的代码找到我创建的第一个图像后创建的轮廓。这部分程序的目标是首先创建一个图像，如：

然后使用color_separator函数将其分解为以下图像。这样做的目的是将每个单独的图像按颜色分开，以便从上面的图像中，我们可以得到：

然后，我尝试使用OpenCV中的基本findContours来查找轮廓。问题在于findContours部分：它将找到第一组轮廓；但对于其他轮廓，轮廓是空白的。图像加载正确；所有操作都正常，直到第一个之后的所有图像都找到了目录。例如，绿色图片会找到它的轮廓，而不会打印“无轮廓”检查语句；但其余图像将打印“无轮廓”检查语句。如果有人能帮忙，我会非常感激的。在

def getAttributesFromNetwork(self,Network,image):
    ShapeList = []
    AngleList = []
    FillList = []
    SizeList = []
    print Network.letter
    # Open the image path from the Problems (Image Data) folder
    image = Image.open(image)
    grayscale = image.convert("L")
    blackwhite = grayscale.point(self.filter,"1")

    image = blackwhite
    image = image.convert("RGB")
    width,height = image.size
    colorindex = 0
    # Translate the image into pictures with various colors
    while True:
        color = DISTINCT_COLORS[colorindex]
        colorindex += 1
        blackpixel = None
        for x,y,pixel in self.walk(image):
            if pixel == (0,0,0):
                blackpixel = (x,y)
                break
        if not blackpixel:
            break

        neighbors = [blackpixel]
        while len(neighbors) > 0:
            processing = list(neighbors)
            neighbors = []
            for x,y in processing:
                image.putpixel((x,y),color)
                new = [(x-1,y),(x+1,y),(x,y-1),(x,y+1)]
                for x,y, in new:
                    if (x,y) in neighbors:
                        continue
                    if x < 0 or x >= width:
                        continue
                    if y < 0 or y >= height:
                        continue
                    if image.getpixel((x,y)) != (0,0,0):
                        continue
                    neighbors.append((x,y))
    # We use the count to save each network as a different image
    self.count = str(self.count)
    # Save the network image
    image.save("colored"+self.count+".png")
    # Open the network image; here, we'll convert it to a bunch of different 
    # images; each with a different shape
    im = Image.open("colored"+self.count+".png")
    # Separate the images
    colors_dict = color_separator(im)
    #print colors_dict
    # show the images:
    imageCount = 0
    # Iterate through the color dictionary for all of the images
    for key,value in colors_dict.iteritems():
        if key == (255, 255, 255):
            imageCount += 1
            continue
        imageCount = str(imageCount)
        # grab the individual image,
        image = value
        # save it,
        image.save(Network.letter+"coloredSmall"+imageCount+".png")
        # then read it back with OpenCV for processing
        img = cv2.imread(Network.letter+"coloredSmall"+imageCount+".png")
        # Convert it to grayscale; it processes better this way
        imgray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
        ret,thresh = cv2.threshold(imgray,127,255,0)
        # find the contours in the image
        contours,hierarchy = cv2.findContours(thresh,1,2)
        #count = 0
        # iterate through the contours,
        if not contours:
            print "No Contours"
        for cnt in contours:
            print "Looking through contours"
            #if (count%2) == 1:
                #count = count + 1
                #print "Count2: ",count
                #continue
            # approximate how many sides it has
            approx = cv2.approxPolyDP(cnt,0.01*cv2.arcLength(cnt,True),True)
            print len(approx)
            if len(approx) == 5:
                print "Half-Arrow"
                ShapeList.append("Half-Arrow")
            if len(approx) == 7:
                print "Arrow"
                ShapeList.append("Arrow")
            elif len(approx) == 3:
                print "Triangle"
                ShapeList.append("Triangle")
            elif len(approx) == 4:
                print "Square"
                ShapeList.append("Square")
            elif len(approx) >= 13:
                print "Circle"
                ShapeList.append("Circle")
            elif len(approx) == 12:
                print "Cross"
                ShapeList.append("Cross")
            (x,y),(MA,ma),angle = cv2.fitEllipse(cnt)
            AngleList.append(angle)
            #count = count + 1
            #print "Count: ",count3
            print ShapeList
        cv2.imshow("img",img)
        cv2.waitKey(0)
        cv2.destroyAllWindows()
        imageCount = int(imageCount)
        imageCount += 1

Answer 1

问题是图像上的颜色在转换成灰度时太暗了。绿色的还不错，但深蓝一点也不能很好地转换成灰度。故事的寓意：当从RGB转换为灰度时，请确保您的颜色足够轻，可以进行转换。在