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track_pool_video.py
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track_pool_video.py
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import cv2
import numpy as np
from imutils import contours
import imutils
if __name__ == "__main__":
cap = cv2.VideoCapture('./in/video_ai.mp4')
# Set up the detector with default parameters.
detector = cv2.SimpleBlobDetector_create()
frame_index = 0
if cap.isOpened() is False:
print("Error opening video stream or file")
while cap.isOpened():
ret_val, image = cap.read()
if image is None:
break
frame_index = frame_index + 1
orig = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Blur to remove noise (radius must be ODD)
gray = cv2.GaussianBlur(gray, (41, 41), 0)
# (minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(gray)
# image = orig.copy()
# cv2.circle(image, maxLoc, 21, (255, 0, 0), 2)
# threshold the image to reveal light regions in the blurred image
thresh = cv2.threshold(gray, 210, 255, cv2.THRESH_BINARY)[1]
# display the results of the naive attempt
#cv2.imshow("Naive", image)
# 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)
# Contours
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
if len(cnts) != 0:
cnts = imutils.contours.sort_contours(cnts)[0]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
# Just get the largest contour
cnt = cnts[0]
area = cv2.contourArea(cnt)
cv2.drawContours(image, [cnt], -1, (0, 0, 255), 2)
# ((cX, cY), radius) = cv2.minEnclosingCircle(cnt)
# cv2.circle(image, (int(cX), int(cY)), int(radius), (0, 0, 255), 3)
cv2.putText(image, str(area), (70, 70), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
# Detect blobs.
# keypoints = detector.detect(image)
# Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
# for marker in keypoints:
# img2 = cv2.drawMarker(img2, tuple(int(i) for i in marker.pt), color=(0, 255, 0))
#im_with_keypoints = cv2.drawKeypoints(image, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
#cv2.putText(image, "FPS: %f" % (1.0 / (time.time() - fps_time)), (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
# cv2.imshow('tf-pose-estimation result', image)
# Write frame
cv2.imwrite('./out/protoshape/frame' + '_' + f'{frame_index:04}' + '.png', image)
#fps_time = time.time()
# if cv2.waitKey(1) == 27:
# break
print ("Processing frame " + str(frame_index))