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167 lines
4.4 KiB
167 lines
4.4 KiB
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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