class Pipeline():
def __init__(self, out_img_dir=None):
# tool for processing one frame and save the frame when the ball is released to out_img_dir
self.tool = Tool(out_img_dir)
def process_video(self, video_path):
'''
Process one video, write the frame ID for when the ball was released to the log file
:param video_path: path to the video of a baseball pitch
'''
cap = cv2.VideoCapture(video_path)
video_base_name = self.tool.get_base(video_path)
# process videos frame by frame, from the first to the last frame
frame_indices = np.arange(1, int(cap.get(cv2.CAP_PROP_FRAME_COUNT)))
for i in tqdm.tqdm(frame_indices):
success, frame = cap.read()
if success:
# stop the program after finding the frame where ball was released, and record the frame index
found_ball = self.tool.find_ball(i, frame, video_base_name)
if found_ball:
frame_ball_release = i
break
# save results to log file
self.parse_log_file.write(
'Ball released at frame {} for video {}\n'.format(
frame_ball_release, video_base_name))
self.parse_log_file.flush()
def process_all_video(self, video_dir, parse_log):
'''
Process all videos in a directory
:param video_dir: Directory containing all videos to be processed
:param parse_log: Log file that records the frame index where the ball was released
'''
self.parse_log_file = open(parse_log, "w")
# get a list of video paths, in the alphabetical order of file names
video_paths = sorted(glob.glob(video_dir + "*.mp4"),
key=self.tool.get_base)
for video_path in video_paths:
# process each video and write the frame index where the ball was released to parse_log_file
self.process_video(video_path)
self.parse_log_file.close()
