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--- |
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license: cc-by-nc-sa-4.0 |
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task_categories: |
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- text-to-image |
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- image-to-image |
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language: |
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- en |
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tags: |
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- southpark |
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- cartoon |
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- animation |
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- comedy |
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- images |
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- frames |
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pretty_name: southpark |
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size_categories: |
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- 100K<n<1M |
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--- |
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# South Park |
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## South Park Images Dataset |
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*** |
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%3C%2Fspan%3E%3C!-- HTML_TAG_END --> |
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*** |
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# Installation |
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```python |
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from huggingface_hub import snapshot_download |
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repo_id = "asigalov61/South-Park" |
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repo_type = 'dataset' |
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local_dir = "./South-Park" |
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snapshot_download(repo_id, repo_type=repo_type, local_dir=local_dir) |
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``` |
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*** |
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# Make your own dataset |
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```sh |
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!pip install opencv-python |
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``` |
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```python |
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import cv2 |
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import os |
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from tqdm import tqdm |
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#=============================================================================================== |
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def scan_videos(directory, videos_extensions=['.mkv', '.mp4', '.avi']): |
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video_files = [os.path.join(directory, f) for f in os.listdir(directory) if os.path.splitext(f)[1].lower() in videos_extensions] |
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return video_files |
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def extract_frames(video_path, |
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output_folder, |
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interval=0.1, |
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square_size=480, |
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scale_size=128, |
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images_ext='.jpg' |
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): |
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if not os.path.exists(output_folder): |
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os.makedirs(output_folder) |
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cap = cv2.VideoCapture(video_path) |
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fps = cap.get(cv2.CAP_PROP_FPS) |
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frame_interval = int(fps * interval) |
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frame_count = 0 |
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) |
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print('Video file:', os.path.basename(video_path)) |
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with tqdm(total=total_frames, desc='Extracting frames') as pbar: |
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while True: |
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ret, frame = cap.read() |
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if not ret: |
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break |
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if frame_count % frame_interval == 0: |
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# Calculate the coordinates for cropping the center square |
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height, width = frame.shape[:2] |
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center_y, center_x = height // 2, width // 2 |
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half_size = square_size // 2 |
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top_left_x = max(center_x - half_size, 0) |
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top_left_y = max(center_y - half_size, 0) |
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bottom_right_x = min(center_x + half_size, width) |
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bottom_right_y = min(center_y + half_size, height) |
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square_frame = frame[top_left_y:bottom_right_y, top_left_x:bottom_right_x] |
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# Normalize brightness and contrast |
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normalized_frame = cv2.normalize(square_frame, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX) |
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# Resize |
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resized_frame = cv2.resize(normalized_frame, (scale_size, scale_size)) |
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frame_name = os.path.join(output_folder, f"frame_{frame_count}{images_ext}") |
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cv2.imwrite(frame_name, resized_frame) |
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frame_count += 1 |
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pbar.update(1) |
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cap.release() |
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print(f"Frames extracted to {output_folder}") |
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#=============================================================================================== |
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videos_dir = 'Videos' |
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videos_extensions = ['.mkv', '.mp4', '.avi'] |
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frames_output_dir = 'Output' |
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frames_extraction_interval = 0.1 # FPS * frames_extraction_interval |
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original_frame_size = 480 |
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final_frame_size = 128 |
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output_frames_extension = '.jpg' |
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#=============================================================================================== |
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print('=' * 70) |
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print('Scanning videos dir...') |
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video_files = scan_videos(videos_dir) |
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print('Done!') |
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print('=' * 70) |
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print('Found', len(video_files), 'video files') |
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print('=' * 70) |
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print('Starting extraction...') |
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print('=' * 70) |
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for video in video_files: |
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extract_frames(video, |
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os.path.join(frames_output_dir, os.path.splitext(os.path.basename(video))[0]), |
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frames_extraction_interval, |
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original_frame_size, |
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final_frame_size, |
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output_frames_extension |
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) |
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print('=' * 70) |
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print('Extraction finished!') |
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print('=' * 70) |
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print('Scanning for extracted frames...') |
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frames_list = list() |
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for (dirpath, dirnames, filenames) in os.walk(frames_output_dir): |
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frames_list += [os.path.join(dirpath, file) for file in filenames if file.endswith(output_frames_extension)] |
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print('Done!') |
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print('=' * 70) |
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print('Found', len(frames_list), 'video frames') |
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print('=' * 70) |
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print('Done!') |
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print('=' * 70) |
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``` |
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*** |
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### Project Los Angeles |
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### Tegridy Code 2024 |
