modernprometheus / rockfinder Goto Github PK

    execution = execution + 1
    print(str(execution))
    thresh = cv2.threshold(blurred, 173, 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 > 100:
            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 = cnts[0] if imutils.is_cv2() else cnts[1]
    try:
        cnts = contours.sort_contours(cnts)[0]
    except:
        print('NO CNTS FOUND')
         
    # loop over the contours
    for (i, c) in enumerate(cnts):
    # draw the bright spot on the image
        print(str(cnts))
        (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)
    if(i == 2):
        cv2.imshow("Image", image)
        cv2.waitKey(0)
        stopNow = 2

    # 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 > 100:
            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 = cnts[0] if imutils.is_cv2() else cnts[1]
    try:
        cnts = contours.sort_contours(cnts)[0]
    except:
        print('NO CNTS FOUND')
         
    # loop over the contours
    for (i, c) in enumerate(cnts):
    # draw the bright spot on the image
        print(str(cnts))
        (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)
    if(i == 2):
        cv2.imshow("Image", image)
        cv2.waitKey(0)
        stopNow = 2