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213321e0fc
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/main_dataset/ |
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/dataset/ |
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from deepface import DeepFace |
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import os |
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import glob |
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import json |
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import numpy as np |
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baseFolderPath = "main_dataset" |
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lostFolderPath = "lost-images" |
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output_file = "sv_embeddings.json" |
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embeddings = [] |
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backends = [ |
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'opencv', |
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'ssd', |
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'dlib', |
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'mtcnn', |
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'fastmtcnn', |
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'retinaface', |
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'mediapipe', |
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'yolov8', |
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'yunet', |
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'centerface', |
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] |
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def main(): |
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video_folders = setup() |
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for folder_info in video_folders: |
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foldername, video_path, image_paths = folder_info |
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print(f"Processing {foldername}\n") |
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run(image_paths, foldername, video_path) |
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saveToJson() |
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def processImage(image, idx, foldername, video_path): |
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try: |
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print(f"Processing {image}", end="\r") |
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# Initialize variables to accumulate sums |
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sums = { |
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'angry': 0, |
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'disgust': 0, |
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'fear': 0, |
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'happy': 0, |
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'sad': 0, |
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'surprise': 0, |
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'neutral': 0 |
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} |
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anaylse_obj = DeepFace.analyze( |
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img_path=image, |
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actions=['emotion'], |
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enforce_detection=True, |
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detector_backend=backends[5], |
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silent=True |
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) |
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emotions = anaylse_obj[0]['emotion'] |
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selected_emotions = [ |
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emotions['happy'], |
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emotions['sad'], |
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emotions['angry'], |
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emotions['neutral'] |
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] |
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num_of_faces = len(anaylse_obj) |
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# Get average emotion |
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# for face in anaylse_obj: |
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# emotions = face['emotion'] |
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# for emotion in sums: |
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# sums[emotion] += emotions.get(emotion, 0) |
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# averages = {emotion: sums[emotion] / num_of_faces for emotion in sums} |
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raw_emotions = [value for value in emotions.values()] |
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#normalized_emotions = [value / 100 for value in raw_emotions] |
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normalized_emotions = [value / 100 for value in selected_emotions] |
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#normalised_emotions = l2_normalize(np.array(raw_emotions)) |
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# normalised_emotions = normalised_emotions.tolist() |
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entry = { |
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"folder": foldername, |
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"image": image, # Extract the filename from the path |
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"error": 0, |
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"vector": normalized_emotions |
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} |
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# Add the entry to the JSON data list |
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embeddings.append(entry) |
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except: |
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# if model does not detect any emotions |
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# set lost -> 1 & generate a random emotion vector |
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random_vector = np.random.rand(4) |
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random_vector_list = random_vector.tolist() |
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entry = { |
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"folder": foldername, |
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"image": image, # Extract the filename from the path |
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"error": 1, |
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"vector": random_vector_list |
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} |
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# Add the entry to the JSON data list |
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embeddings.append(entry) |
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pass |
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print(entry["vector"]) |
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def setup(): |
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video_folders = [] |
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subfolders = [f.path for f in os.scandir(baseFolderPath) if f.is_dir()] |
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for subfolder in subfolders: |
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frames_folder_path = os.path.join(subfolder, "segmented_images") |
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# Find the video file in the subfolder |
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# video_files = [f for f in os.listdir(subfolder) if f.endswith(('.mp4', '.avi', '.mov'))] |
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# if video_files: |
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# video_path = os.path.join(subfolder, video_files[0]) |
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# List of all image files in the frames folder |
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image_files = glob.glob(os.path.join(frames_folder_path, '*')) |
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# Filter by specific image extensions (e.g., .jpg, .png) |
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image_paths = [img for img in image_files if img.endswith(('.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.gif'))] |
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# Sort the image paths by filename (assuming the filenames are numerically ordered) |
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image_paths.sort(key=lambda x: int(os.path.splitext(os.path.basename(x))[0])) |
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video_path = "null" |
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# Add the information to the list |
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video_folders.append((os.path.basename(subfolder), video_path, image_paths)) |
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return video_folders |
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def run(image_paths, foldername, video_path): |
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for idx, image in enumerate(image_paths): |
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processImage(image, idx, foldername, video_path) |
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def l2_normalize(vector): |
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norm = np.linalg.norm(vector) |
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if norm == 0: |
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return vector |
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return vector / norm |
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def saveToJson(): |
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with open(output_file, 'w') as f: |
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json.dump(embeddings, f, indent=4) |
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print("Successfully created JSON file.") |
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if __name__ == "__main__": |
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main() |
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import os |
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import subprocess |
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import re |
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import shutil |
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def convert_video(video_path, output_path): |
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print(f"Converting video, {video_path}") |
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# Codec that works with openframeworks! |
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command = [ |
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"ffmpeg", |
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"-i", video_path, # Input file |
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"-c:v", "libx264", # Video codec |
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"-profile:v", "high", # Video profile |
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"-c:a", "copy", # Copy audio without re-encoding |
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output_path # Output file |
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] |
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results = subprocess.run(command, stderr=subprocess.PIPE, text=True) |
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output = results.stderr |
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print(f"Video converted {output}") |
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def get_frame_rate(video_path): |
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""" |
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Get the frame rate of a video file using ffmpeg. |
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""" |
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command = [ |
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'ffmpeg', |
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'-i', video_path, |
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'-vcodec', 'png', |
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'-f', 'null', |
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'-' |
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] |
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result = subprocess.run(command, stderr=subprocess.PIPE, text=True) |
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output = result.stderr |
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match = re.search(r'(\d+(\.\d+)?) fps', output) |
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if match: |
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return float(match.group(1)) |
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else: |
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raise ValueError("Frame rate not found in video information.") |
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def extract_frames_from_video(video_path, output_folder): |
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""" |
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Extract frames from a video file at 1 fps. |
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""" |
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os.makedirs(output_folder, exist_ok=True) |
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command = [ |
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'ffmpeg', |
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'-i', video_path, # Input file |
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'-vf', 'fps=1/10', # Extract 1 frame per second |
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'-vsync', 'vfr', # Variable frame rate |
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os.path.join(output_folder, 'frame_%09d.png') # Output pattern |
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] |
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subprocess.run(command, check=True) |
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def rename_frames(output_folder, frame_rate): |
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""" |
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Rename the extracted frames to match their original frame numbers. |
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""" |
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frame_files = sorted(f for f in os.listdir(output_folder) if f.startswith('frame_') and f.endswith('.png')) |
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for i, filename in enumerate(frame_files): |
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# Calculate the original frame number |
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original_frame_number = int(i * frame_rate) |
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# Create the new filename with the original frame number |
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new_filename = f"{original_frame_number:09d}.png" |
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os.rename( |
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os.path.join(output_folder, filename), |
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os.path.join(output_folder, new_filename) |
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) |
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def process_videos_in_folder(folder_path): |
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""" |
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Process all video files in the specified folder and extract frames from each video. |
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""" |
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for filename in os.listdir(folder_path): |
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if filename.lower().endswith(('.mp4', '.avi', '.mov', '.mkv', '.flv')): |
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video_path = os.path.join(folder_path, filename) |
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output_folder = os.path.join(folder_path, os.path.splitext(filename)[0]) # Create a folder for each video |
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# New location for .mp4 |
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video_copy_location = os.path.join(output_folder, filename) |
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# Create frames folder |
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frames_folder = os.path.join(output_folder, "frames") |
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if not os.path.exists(output_folder): |
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os.mkdir(output_folder) |
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if not os.path.exists(frames_folder): |
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os.mkdir(frames_folder) |
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print(f'Processing video: {video_path}') |
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try: |
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#convert_video(video_path, video_copy_location) |
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frame_rate = get_frame_rate(video_path) |
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print(f'Original frame rate: {frame_rate} fps') |
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extract_frames_from_video(video_path, frames_folder) |
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rename_frames(frames_folder, frame_rate) |
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print(f'Frames saved and renamed to: {frames_folder}') |
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except ValueError as e: |
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print(f'Error processing video {video_path}: {e}') |
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if __name__ == "__main__": |
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# Path to the folder containing videos |
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folder_path = 'main_dataset' |
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process_videos_in_folder(folder_path) |
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@ -0,0 +1,74 @@ |
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absl-py==2.1.0 |
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astunparse==1.6.3 |
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beautifulsoup4==4.12.3 |
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blinker==1.8.2 |
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certifi==2024.7.4 |
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charset-normalizer==3.3.2 |
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click==8.1.7 |
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deepface==0.0.93 |
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filelock==3.15.4 |
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fire==0.6.0 |
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Flask==3.0.3 |
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Flask-Cors==5.0.0 |
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flatbuffers==24.3.25 |
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gast==0.6.0 |
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gdown==5.2.0 |
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google-pasta==0.2.0 |
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grpcio==1.65.4 |
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gunicorn==23.0.0 |
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h5py==3.11.0 |
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idna==3.7 |
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importlib_metadata==8.2.0 |
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itsdangerous==2.2.0 |
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Jinja2==3.1.4 |
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keras==3.5.0 |
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libclang==18.1.1 |
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Markdown==3.6 |
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markdown-it-py==3.0.0 |
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MarkupSafe==2.1.5 |
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mdurl==0.1.2 |
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ml-dtypes==0.4.0 |
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mtcnn==0.1.1 |
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namex==0.0.8 |
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numpy==1.26.4 |
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nvidia-cublas-cu12==12.3.4.1 |
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nvidia-cuda-cupti-cu12==12.3.101 |
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nvidia-cuda-nvcc-cu12==12.3.107 |
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nvidia-cuda-nvrtc-cu12==12.3.107 |
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nvidia-cuda-runtime-cu12==12.3.101 |
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nvidia-cudnn-cu12==8.9.7.29 |
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nvidia-cufft-cu12==11.0.12.1 |
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nvidia-curand-cu12==10.3.4.107 |
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nvidia-cusolver-cu12==11.5.4.101 |
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nvidia-cusparse-cu12==12.2.0.103 |
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nvidia-nccl-cu12==2.19.3 |
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nvidia-nvjitlink-cu12==12.3.101 |
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opencv-python==4.10.0.84 |
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opt-einsum==3.3.0 |
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optree==0.12.1 |
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packaging==24.1 |
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pandas==2.2.2 |
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pillow==10.4.0 |
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protobuf==4.25.4 |
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Pygments==2.18.0 |
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PySocks==1.7.1 |
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python-dateutil==2.9.0.post0 |
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pytz==2024.1 |
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requests==2.32.3 |
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retina-face==0.0.17 |
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rich==13.7.1 |
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six==1.16.0 |
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soupsieve==2.6 |
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tensorboard==2.17.1 |
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tensorboard-data-server==0.7.2 |
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tensorflow==2.17.0 |
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tensorflow-io-gcs-filesystem==0.37.1 |
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termcolor==2.4.0 |
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tf_keras==2.17.0 |
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tqdm==4.66.5 |
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typing_extensions==4.12.2 |
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tzdata==2024.1 |
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urllib3==2.2.2 |
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Werkzeug==3.0.3 |
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wrapt==1.16.0 |
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zipp==3.20.0 |
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