Я обнаружил бликовые пятна на моей фотографии с opencv- python. Как я могу удалить их? - PullRequest
0 голосов
/ 26 марта 2020

Ниже кода я использовал для обнаружения бликовых пятен на изображении. Я выполняю серию эрозий и расширений, чтобы удалить и выполнить анализ связанных компонентов на пороге. Как я могу удалить их с открытым c - python?

path = "desire image path"
image = cv2.imread(path)
gray = cv2.cvtColor( image, cv2.COLOR_BGR2GRAY )
blurred = cv2.GaussianBlur( gray, (11, 11), 0 )

# threshold the image to reveal light regions in the
# blurred image
thresh = cv2.threshold( blurred, 200, 255, cv2.THRESH_BINARY )[1]

# perform a series of erosions and dilations to remove
# any small blobs of noise from the thresholded image
thresh = cv2.erode( thresh, None, iterations=2 )
thresh = cv2.dilate( thresh, None, iterations=4 )

# perform a connected component analysis on the thresholded
# image, then initialize a mask to store only the "large"
# components
labels = measure.label( thresh, neighbors=8, background=0 )
mask = np.zeros( thresh.shape, dtype="uint8" )
# loop over the unique components
for label in np.unique( labels ):
    # if this is the background label, ignore it
    if label == 0:
        continue
    # otherwise, construct the label mask and count the
    # number of pixels
    labelMask = np.zeros( thresh.shape, dtype="uint8" )
    labelMask[labels == label] = 255
    numPixels = cv2.countNonZero( labelMask )
    # if the number of pixels in the component is sufficiently
    # large, then add it to our mask of "large blobs"
    if numPixels > 300:
        mask = cv2.add( mask, labelMask )

# find the contours in the mask, then sort them from left to
# right
cnts = cv2.findContours( mask.copy(), cv2.RETR_EXTERNAL,
                         cv2.CHAIN_APPROX_SIMPLE )
cnts = imutils.grab_contours( cnts )
cnts = contours.sort_contours( cnts )[0]
# loop over the contours
for (i, c) in enumerate( cnts ):
    # draw the bright spot on the image
    (x, y, w, h) = cv2.boundingRect( c )
    ((cX, cY), radius) = cv2.minEnclosingCircle( c )
    cv2.circle( image, (int( cX ), int( cY )), int( radius ),
                (0, 0, 255), 3 )
    cv2.putText( image, "#{}".format( i + 1 ), (x, y - 15), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2 )
    # image = remove_glare( image, x, y )
# show the output image
cv2.imshow( "Image", image )
cv2.imwrite("spots_detected.jpg",image)
cv2.waitKey( 0 )

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