Question

所以，对于一个学校项目，我必须创建一个可以让你的手臂上出现纹身的应用程序。目前，openCV使用皮肤的颜色来检测图像的哪个部分是皮肤。

我的问题是这个：在代码的最后一步，纹身的蒙版合并到视频源，数组的大小改变

        #attempt to save the ROI coordinates
        fy1=y1
        fy2=y1+tatHeight
        fx1=x1
        fx2=x1+tatWidth

        #create a ROI mask
        roi = frame[fy1:fy2,fx1:fx2]

        #merge the roi mask with the tatoo and the inverted tatoo masks
        roi_bg = cv2.bitwise_and(roi,roi,mask = mask2inv)
        roi_fg = cv2.bitwise_and(tatoo,tatoo,mask = mask2)



        #merge the background and foreground ROI masks
        dst = cv2.add(roi_bg,roi_fg)



        # add the merged mask to the video feed
        roiColor[fy1:fy2,fx1:fx2]=dst #the problem is here

我收到此错误

ValueError: could not broadcast input array from shape (33,2,3) into shape (0,0,3)

有人可以帮我弄清楚为什么fx和fy的价值会发生变化吗？

您可以找到包含完整代码here

的仓库

感谢任何可以提供帮助的人

编辑：This是我发现代码灵感的网站

编辑2：这是代码

    # USAGE
# python ball_tracking.py --video ball_tracking_example.mp4
# python ball_tracking.py

# import the necessary packages
from collections import deque
import numpy as np
import argparse
import imutils
import cv2

#load tatoo image
imgTatoo=cv2.imread('mustache.png',-1)
tatMask=imgTatoo[:,:,3]
#create a mask from the image
invTatMask=cv2.bitwise_not(tatMask)
imgTatoo=imgTatoo[:,:,0:3]
#define original sizes for the tatoo
tatOrigHeight,tatOrigWidth = imgTatoo.shape[:2]


# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video",
    help="path to the (optional) video file")
ap.add_argument("-b", "--buffer", type=int, default=64,
    help="max buffer size")
args = vars(ap.parse_args())

# define the lower and upper boundaries of the "green"
# ball in the HSV color space, then initialize the
# list of tracked points
greenLower = (0, 0, 73)
greenUpper = (35, 93, 255)
pts = deque(maxlen=args["buffer"])

# if a video path was not supplied, grab the reference
# to the webcam
if not args.get("video", False):
    camera = cv2.VideoCapture(0)

# otherwise, grab a reference to the video file
else:
    camera = cv2.VideoCapture(args["video"])

# keep looping
while True:
    # grab the current frame
    (grabbed, frame) = camera.read()

    # if we are viewing a video and we did not grab a frame,
    # then we have reached the end of the video
    if args.get("video") and not grabbed:
        break

    # resize the frame, blur it, and convert it to the HSV
    # color space
    frame = imutils.resize(frame, width=600)
    frame = cv2.bilateralFilter(frame, 11, 17, 17)
    # blurred = cv2.GaussianBlur(frame, (11, 11), 0)
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    # construct a mask for the color "green", then perform
    # a series of dilations and erosions to remove any small
    # blobs left in the mask
    mask = cv2.inRange(hsv, greenLower, greenUpper)
    mask = cv2.erode(mask, None, iterations=2)
    mask = cv2.dilate(mask, None, iterations=2)

    # find contours in the mask and initialize the current
    # (x, y) center of the ball
    cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
        cv2.CHAIN_APPROX_SIMPLE)[-2]
    center = None



    # only proceed if at least one contour was found
    if len(cnts) > 0:
        # find the largest contour in the mask, then use
        # it to compute the minimum enclosing circle and
        # centroid
        c = max(cnts, key=cv2.contourArea)
        ((x, y), radius) = cv2.minEnclosingCircle(c)
        M = cv2.moments(c)
        center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))

        # only proceed if the radius meets a minimum size
        if radius > 10:
            #draw contour of desired shape
            cv2.drawContours( frame, c, -1, (239, 0, 0),6 )
            #create the smallest box containing that contour
            rect = cv2.minAreaRect(c)
            box = cv2.boxPoints(rect)
            box = np.int0(box)
            #draw the box
            cv2.drawContours(frame,[box],0,(0,0,255),2)
            #Save the box parameters (center,height,width and angle)
            areaCenter=rect[0]
            areaX,areaY=int(areaCenter[0]),int(areaCenter[1])
            areaSize=rect[1]
            areaHeight=int(areaSize[0])
            areaWidth=int(areaSize[1])
            areaAngle=rect[2]
            #define the tattoo size
            tatWidth=int(0.2*areaWidth)
            tatHeight=tatWidth * tatOrigHeight // tatOrigWidth

            #face = cv2.rectangle(frame,(areaX-areaWidth//4,areaY-areaHeight//4),(areaX+areaWidth//4,areaY+areaHeight//4),(255,0,0),2)

            #roiGray=gray[areaY-areaHeight//2:areaY+areaHeight//2, areaX-areaWidth//2:areaX+areaWidth//2]

            #create a mask from the video feed with the size of the region of interest (box created before)
            roiColor=frame[areaY-areaHeight//2:areaY+areaHeight//2, areaX-areaWidth//2:areaX+areaWidth//2]

            # print(areaX,areaY,areaWidth,areaHeight)
            # print(tatWidth,tatHeight)

            # save the center of the region of interest (ROI)
            x1 = areaX - (tatWidth//2)
            x2 = areaX + (tatWidth//2)
            y1 = areaY - (tatHeight//2)
            y2 = areaY + (tatHeight//2)

            # protect from wierd center coordinates (outside of the frame)
            if x1 < 0:
                x1 = 0
            if y1 < 0:
                y1 = 0
            if x2 > areaWidth:
                x2 = areaWidth
            if y2 > areaHeight:
                y2 = areaHeight
            print(x1,x2,y1,y2)

            # resize the tattoo to match the ROI size
            tatHeight=tatWidth * tatOrigHeight // tatOrigWidth
            tatWidth=x2-x1

            # protect from wierd (negative) tatoo sizes
            if tatHeight<=0:
                tatHeight=1
            if tatWidth<=0:
                tatWidth=2
            print(tatHeight)
            print(tatWidth)

            # resize all the masks to the same size in order to merge them
            tatoo=cv2.resize(imgTatoo,(tatWidth,tatHeight),interpolation=cv2.INTER_AREA)
            mask2=cv2.resize(tatMask,(tatWidth,tatHeight),interpolation=cv2.INTER_AREA)
            mask2inv=cv2.resize(invTatMask,(tatWidth,tatHeight),interpolation=cv2.INTER_AREA)


            print(mask2inv.shape)

            #attempt to save the ROI coordinates
            fy1=y1
            fy2=y1+tatHeight
            fx1=x1
            fx2=x1+tatWidth

            #create a ROI mask
            roi = frame[fy1:fy2,fx1:fx2]
            print(roi.shape)

            #merge the roi mask with the tatoo and the inverted tatoo masks
            roi_bg = cv2.bitwise_and(roi,roi,mask = mask2inv)
            roi_fg = cv2.bitwise_and(tatoo,tatoo,mask = mask2)

            print(roi_bg.shape,roi_fg.shape)

            #merge the background and foreground ROI masks
            dst = cv2.add(roi_bg,roi_fg)

            print("dst: ",dst.shape)
            print("roi: ",roiColor.shape)
            print(fy1,fy2,fy2-fy1)
            print(fx1,fx2,fx2-fx1)

            # add the merged mask to the video feed
            roiColor[fy1:fy2,fx1:fx2]=dst


    # show the frame to our screen
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1) & 0xFF

    # if the 'q' key is pressed, stop the loop
    if key == ord("q"):
        break

# cleanup the camera and close any open windows
camera.release()
cv2.destroyAllWindows()

Answer 1

好的，感谢Dan Masek，如果通过绕过以下行没用的roiColor来解决问题：

frame[fy1:fy2,fx1:fx2]=dst

为了确保我想要的区域可以包含在原始图像中。

openCV跟踪程序中的自变量数组大小

1 个答案: