text stringlengths 1 93.6k |
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for enhancing_iteration in tqdm(range(num_iterations), desc="Enhancing iterations"):
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# Opening the video and extracting essential properties
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video = cv2.VideoCapture(str(mp4))
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original_video_fps = video.get(cv2.CAP_PROP_FPS)
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width, height = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)), int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
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original_num_frames = sum(video.read()[0] for _ in range(int(video.get(cv2.CAP_PROP_FRAME_COUNT))))
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# Informing the user of video details before processing
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print(f"Video Name: {original_seq_name}")
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print(f"Original Frame Rate (FPS): {original_video_fps}")
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print(f"Original Total Number of Frames: {original_num_frames}")
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img_array = []
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# Processing each set of 4 frames for frame rate enhancement
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for t in tqdm(range(0, original_num_frames - 3), desc="Processing frames"):
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video.set(cv2.CAP_PROP_POS_FRAMES, t)
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_, rawFrame0 = video.read()
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_, rawFrame1 = video.read()
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_, rawFrame2 = video.read()
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_, rawFrame3 = video.read()
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# If any frame in the set of 4 is missing, stop processing
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if any(frame is None for frame in [rawFrame0, rawFrame1, rawFrame2, rawFrame3]):
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break
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# Convert frames to tensors and move them to GPU
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frame0 = TF.to_tensor(rawFrame0)[None, ...].cuda()
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frame1 = TF.to_tensor(rawFrame1)[None, ...].cuda()
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frame2 = TF.to_tensor(rawFrame2)[None, ...].cuda()
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frame3 = TF.to_tensor(rawFrame3)[None, ...].cuda()
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# Use the trained model to predict enhanced frames
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with torch.no_grad():
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out = self.model(frame0, frame1, frame2, frame3)
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# Special handling for the very first
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if t == 0:
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img_array += [tensor2rgb(frame0)[0]] * 2 + [tensor2rgb(frame1)[0]]
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img_array += [tensor2rgb(out)[0], tensor2rgb(frame2)[0]]
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# Special handling for the last sets of frames
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if t == original_num_frames - 4:
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img_array += [tensor2rgb(frame3)[0]] * 2
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video.release()
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# Decide the output video's fps
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new_num_frames = len(img_array)
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output_fps = (
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new_num_frames * original_video_fps
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) / original_num_frames # Compute the fps that keeps video playback constant (duration of video)
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if (not keep_original_duration) and (custom_fps is not None) and (custom_fps >= 1):
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output_fps = custom_fps
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# Create and write frames to the output video
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avi_outname = f"{original_seq_name}_{enhancing_iteration}.avi"
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new_num_frames = len(img_array)
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print(f"Output filename: {avi_outname}")
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print(f"New Total Number of Frames: {new_num_frames}")
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cv2writer = cv2.VideoWriter(
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avi_outname,
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cv2.VideoWriter_fourcc(*"DIVX"), # NOTE: codec issues mean we have to export as avi using DIVX
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output_fps,
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(width, height),
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)
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for frame in img_array:
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cv2writer.write(frame)
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cv2writer.release()
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# Convert the AVI video to MP4 format using ffmpeg (NOTE: We use ffmpeg because we have codec issues with cv2 and mp4)
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mp4_outname = avi_outname.replace(".avi", ".mp4")
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cmd = ["ffmpeg", "-i", avi_outname, mp4_outname, "-y"]
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subprocess.run(cmd)
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# Append the output path and prepare for the next iteration if needed
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mp4 = mp4_outname
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yield Path(mp4_outname)
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# <FILESEP>
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'''
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main.py
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----------
|
Matthew Chatham
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June 6, 2018
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Given a company's landing page on Glassdoor and an output filename, scrape the
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following information about each employee review:
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Review date
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Employee position
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Employee location
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Employee status (current/former)
|
Review title
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Number of helpful votes
|
Pros text
|
Cons text
|
Advice to mgmttext
|
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