Python中的OpenCV轮廓最小尺寸位置

Question

我正在使用背景减法来分析室外场景的移动物体.当太阳出来时我有阴影问题.我正在使用轮廓来隔离对象.目前我只是分析轮廓的上半部分,因为阴影通常位于下半部分.

想象一下橡皮鸭的轮廓,我想要做的是找到鸭子颈部的y位置,即轮廓处于最小水平尺寸的位置.有人可以指出我如何找到"鸭脖"的正确方向？

在代码中,binary是移动对象的阈值图像,HIGHT并且WIDTH是图像的高度和宽度,lab是LAB颜色空间中的相同图像.

我想half = int(h/2)用函数替换该行来找到水平线的y位置以切断"鸭脖".

_,contours,_ = cv2.findContours(binary.copy(), cv2.RETR_EXTERNAL,
                            cv2.CHAIN_APPROX_SIMPLE)

# loop over the contours
for i, cnt in enumerate(contours):

   # compute the bounding box for the contour
   (x, y, w, h) = cv2.boundingRect(cnt)

   # reject contours outside size range
   if w > 250 or w < 30 or h > 250 or h < 30 :
          continue

   # make sure the box is inside the frame
   if x <= 0 or y <= 0 or x+w >= (WIDTH -1) or y+h >= (HIGHT -1):
          continue

   # isolate feature
   half = int(h/2)
   roi = lab[y:y+half, x:x+w]
   mask = binary[y:y+half, x:x+w]

   # calculate the mean of the colour
   mean = cv2.mean(roi, mask = mask)
   # note: mean is L a b
   L = int(mean[0])
   a = int(mean[1])
   b = int(mean[2])
   print L,a,b

我正在使用opencv 3和python 2.7

PS我已经尝试过背景减法器MOG2,据说它可以识别阴影,但是它对于我的使用来说是嘈杂的并且不可行.

Answer 1

您可以定义一个遮罩来腐蚀图像，这样您就可以从山谷中破坏顶部和底部的斑点。您可以将其应用到您的代码中，如下所示：

# loop over the contours
for i, cnt in enumerate(contours):

   # compute the bounding box for the contour
   (x, y, w, h) = cv2.boundingRect(cnt)

   # reject contours outside size range
   if w > 250 or w < 30 or h > 250 or h < 30 :
          continue

   # make sure the box is inside the frame
   if x <= 0 or y <= 0 or x+w >= (WIDTH -1) or y+h >= (HIGHT -1):
          continue

   # ---------------
   # create a mask for erosion, you can play with the mask size/shape
   mask = cv2.getStructuringElement(cv2.MORPH_RECT,(5,5))
   # erode the original image
   eroded_img = cv2.erode(binary,mask,iterations = 1)
   cv2.imshow("Eroded image",eroded_img)
   # find the middle of the two new contours
   _,new_contours,_ = cv2.findContours(eroded_img, cv2.RETR_EXTERNAL,
                      cv2.CHAIN_APPROX_SIMPLE)
   (_, y_t, _, h_t) = cv2.boundingRect(new_contours[0])
   (_, y_b, _, h_b) = cv2.boundingRect(new_contours[1])
   bottom_top_y = max(y_t, y_b) # highest y of bottom part
   top_bottom_y = min(y_t+h_t, y_b+h_b) # lowest y of top part
   half = top_bottom_y + (bottom_top_y - top_bottom_y)/2
   # ------------

   # isolate feature
   roi = lab[y:y+half, x:x+w]
   mask = binary[y:y+half, x:x+w]

   # calculate the mean of the colour
   mean = cv2.mean(roi, mask = mask)
   # note: mean is L a b
   L = int(mean[0])
   a = int(mean[1])
   b = int(mean[2])
   print L,a,b

希望能帮助到你！有关二值图像的形态学操作的更多示例，您可以查看此处。