drawcontours 区域的图像处理

编程问答 2022-05-27

问题描述

我想仅将绘制轮廓区域转换为 RGB 图像，然后再次将其转换为 HSV，以便随着时间的推移更新每帧的上下值。

注意：我想避免使用矩形区域的 ROI，因为 drawcontours 是实际区域。

我尝试根据 drawContours 区域 roi2 = clone1[contour[[0]]] cv2.imshow("roi2",roi2) 而不是矩形区域 roi1 = clone1[y:y + h,x:x + h] cv2.imshow("roi1",roi1)

来显示感兴趣区域 (ROI)

不知道有没有可能。

OR 如何使用原始图像（RGB 图像）的副本为除 drawContours 区域之外的整个图像创建遮罩？类似于roi1 = clone1[...]

完整的代码：

import cv2
import numpy as np
import time

cap = cv2.VideoCapture(0)
width = cap.get(3)  # float
height = cap.get(4)  # float

time.sleep(2.0)
while (1):
    _,img = cap.read()
    clone1 = img.copy()
    if _ is True:
        hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
    else:
        continue

    black_lower = np.array([0,0],np.uint8)
    black_upper = np.array([180,255,30],np.uint8)
    black = cv2.inRange(hsv,black_lower,black_upper)
    # Morphological Transform,dilation

    kernal = np.ones((5,5),"uint8")
    black = cv2.dilate(black,kernal)
    res_black = cv2.bitwise_and(img,img,mask=black)
    (_,contours,hierarchy) = cv2.findContours(black,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
    cv2.imshow("ROI_",_)
    cnts = sorted(contours,key=cv2.contourArea,reverse=True)[:1]  # get largest  contour area
    for pic,contour in enumerate(cnts):
        area = cv2.contourArea(contour)
        if (area > 300):
            x,y,w,h = cv2.boundingRect(contour)
         
            # segmented = max(cnts,key=cv2.contourArea)
          

            img = cv2.rectangle(img,(x,y),(x + w,y + h),(0,0),2)
            roi1 = clone1[y:y + h,x:x + h]
            cv2.imshow("roi1",roi1)
            cv2.putText(img,"Black Colour",cv2.FONT_HERShey_SIMPLEX,0.7,0))
            bbbbb = cv2.drawContours(img,[contour],-1,3)  # segmentation
            # roi2 = clone1[contour[[0]]]
            # cv2.imshow("roi2",roi2)

    cv2.imshow("Color Tracking",img)
    if cv2.waitKey(10) & 0xFF == ord('q'):
        cap.release()
        cv2.destroyAllWindows()
        break

解决方法

import cv2
import numpy as np
import time

cap = cv2.VideoCapture(0)
width = cap.get(3)  # float
height = cap.get(4)  # float
time.sleep(2.0)
while (1):
    _,img = cap.read()
    clone1 = img.copy()
    if _ is True:
        hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
    else:
        continue

    black_lower = np.array([0,0],np.uint8)
    black_upper = np.array([180,255,30],np.uint8)
    black = cv2.inRange(hsv,black_lower,black_upper)
    # Morphological Transform,Dilation

    kernal = np.ones((5,5),"uint8")
    black = cv2.dilate(black,kernal)
    res_black = cv2.bitwise_and(img,img,mask=black)

    (_,contours,hierarchy) = cv2.findContours(black,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

    cnts = sorted(contours,key=cv2.contourArea,reverse=True)[:1]  # get largest  contour area
    for pic,contour in enumerate(cnts):
        # print type(contour),type(img)
        # print contour.dtype,img.dtype
        area = cv2.contourArea(contour)
        if (area > 300):
            x,y,w,h = cv2.boundingRect(contour)


            img = cv2.rectangle(img,(x,y),(x + w,y + h),(0,0),2)
            roi1 = clone1[y:y + h,x:x + w]
            cv2.imshow("roi1",roi1)
            cv2.putText(img,"Black Colour",cv2.FONT_HERSHEY_SIMPLEX,0.7,0))
            bbbbb = cv2.drawContours(img,[contour],-1,3)  # segmentation
            # roi2 = clone1[contour[[0]]]
            # cv2.imshow("roi2",roi2)
            # imaf = np.uint8(contour)
            # imaf = cv2.polylines(img,True,5)
            # print 'imaf',imaf.dtype,img.dtype

            # roi2 = cv2.drawContours(mask,1)
            mask = np.zeros(img.shape,np.uint8)
            # roi_corners = np.array([[(2,2),(150,50),(100,100),(2,100)]],dtype=np.int32)
            # roi_corners = np.array([[(x,y + h)]],dtype=np.int32)
            roi_corners = np.array([contour],dtype=np.int32)
            channel_count = img.shape[2]  # i.e. 3 or 4 depending on your image
            ignore_mask_color = (255,) * channel_count
            cv2.fillPoly(mask,roi_corners,ignore_mask_color)
            # cv2.fillConvexPoly(mask,ignore_mask_color)
            masked_image = cv2.bitwise_and(img,mask)

            hsvRoi1 = cv2.cvtColor(masked_image,cv2.COLOR_BGR2HSV)
            black_lower = np.array(
                [hsvRoi1[:,:,hsvRoi1[:,1],2]])
            black_upper = np.array(
                [hsvRoi1[:,2]])

            h_values = np.array([hsvRoi1[:,0]])
            h_min = h_values[(h_values >= 1)].min()
            h_max = h_values[(h_values <= 178)].max()

            s_values = np.array([hsvRoi1[:,1]])
            s_min = s_values[(s_values >= 1)].min()
            s_max = s_values[(s_values <= 254)].max()

            v_values = np.array([hsvRoi1[:,2]])
            v_min = v_values[(v_values >= 1)].min()
            v_max = v_values[(v_values <= 254)].max()

            black_lower = np.array([h_min,s_min,v_min],np.uint8)
            black_upper = np.array([h_max,s_max,v_max],np.uint8)


            print 'black_lower,black_upper',black_upper
            cv2.imshow("roihsv",masked_image)

    cv2.imshow("Color Tracking",img)
    if cv2.waitKey(10) & 0xFF == ord('q'):
        cap.release()
        cv2.destroyAllWindows()
        break
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