cv2.imshow zeigt 9 Bildschirme anstelle von 1

Question

Ich baue einen Code, um Haut von Webcam-Video adaptiv zu erkennen. Ich habe es fast funktioniert, aber bei der Ausgabe des Videos zeigt es 9 Bildschirme der "Haut" -Maske statt nur einer. Es scheint, als würde ich einfach etwas Einfaches vermissen, aber ich kann es nicht herausfinden.

image shown here

-Code unten:

# first let's train the data 
data, labels = ReadData() 
classifier = TrainTree(data, labels) 

# get the webcam. The input is either a video file or the camera number 
# since using laptop webcam (only 1 cam), input is 0. A 2nd cam would be input 1 
camera = cv2.VideoCapture(0) 

while True: 
    # reads in the current frame 
    # .read() returns True if frame read correctly, and False otherwise 
    ret, frame = camera.read() # frame.shape: (480,640,3) 

    if ret: 
     # reshape the frame to follow format of training data (rows*col, 3) 
     data = np.reshape(frame, (frame.shape[0] * frame.shape[1], 3)) 
     bgr = np.reshape(data, (data.shape[0], 1, 3)) 
     hsv = cv2.cvtColor(np.uint8(bgr), cv2.COLOR_BGR2HSV) 
     # once we have converted to HSV, we reshape back to original shape of (245057,3) 
     data = np.reshape(hsv, (hsv.shape[0], 3)) 
     predictedLabels = classifier.predict(data) 

     # the AND operator applies the skinMask to the image 
     # predictedLabels consists of 1 (skin) and 2 (non-skin), needs to change to 0 (non-skin) and 255 (skin) 
     predictedMask = (-(predictedLabels - 1) + 1) * 255 # predictedMask.shape: (307200,) 

     # resize to match frame shape 
     imgLabels = np.resize(predictedMask, (frame.shape[0], frame.shape[1], 3)) # imgLabels.shape: (480,640,3) 
     # masks require 1 channel, not 3, so change from BGR to GRAYSCALE 
     imgLabels = cv2.cvtColor(np.uint8(imgLabels), cv2.COLOR_BGR2GRAY) # imgLabels.shape: (480,640) 

     # do bitwsie AND to pull out skin pixels. All skin pixels are anded with 255 and all others are 0 
     skin = cv2.bitwise_and(frame, frame, mask=imgLabels) # skin.shape: (480,640,3) 
     # show the skin in the image along with the mask, show images side-by-side 
     # **********THE BELOW LINE OUTPUTS 9 screens of the skin mask instead of just 1 **************** 
     cv2.imshow("images", np.hstack([frame, skin])) 

     # if the 'q' key is pressed, stop the loop 
     if cv2.waitKey(1) & 0xFF == ord("q"): 
      break 
    else: 
     break 

# release the video capture 
camera.release() 
cv2.destroyAllWindows() 

Answer 1

Sie arbeiten mit Bitmaps. Um eine Idee zu bekommen, was sie halten, cv2.imshow sie einzeln. Dann werden Sie (wörtlich) sehen, wo die Daten falsch laufen.

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Nun ist der Täter höchstwahrscheinlich np.resize():

np.resize(a, new_shape)

Return a new array with the specified shape.

If the new array is larger than the original array, then the new array is filled with repeated copies of a. Note that this behavior is different from a.resize(new_shape) which fills with zeros instead of repeated copies of a .

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Um Skala eine Bitmap (= Größe ändern, während bestrebt, das gleiche visuelle Bild zu erhalten), verwenden cv2.resize() gemäß OpenCV: Geometric Transformations of Images .