attempt at making loadscript

load_script.py

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+import os
+import json
+import cv2
+from datasets import Dataset, DatasetDict, Features, Value, Array2D, Array3D, Sequence
+from pathlib import Path
+import numpy as np
+
+# Define constants
+VIDEO_EXTENSIONS = ['.avi']
+JSON_EXTENSIONS = ['.json']
+KEYPOINTS = [
+    "nose", "left_eye", "right_eye", "left_ear", "right_ear", "left_shoulder", "right_shoulder",
+    "left_elbow", "right_elbow", "left_wrist", "right_wrist", "left_hip", "right_hip", 
+    "left_knee", "right_knee", "left_ankle", "right_ankle"
+]
+
+def load_video(video_path):
+    """Reads a video file and returns a list of frames as NumPy arrays."""
+    cap = cv2.VideoCapture(video_path)
+    frames = []
+    
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        frames.append(frame)
+    
+    cap.release()
+    return np.array(frames)
+
+def load_json(json_path):
+    """Loads the JSON keypoint data for each frame."""
+    with open(json_path, 'r') as f:
+        data = json.load(f)
+    return data
+
+def process_frame_data(frame_data):
+    """Converts the frame's keypoints into a structured format."""
+    detections = []
+    
+    # Check if 'data' exists in the frame_data
+    if 'detections' in frame_data:
+        for detection in frame_data['detections']:  # Now using 'data' instead of 'detections'
+            if detection:  # Check if there's any valid detection data
+                person = {
+                    "confidence": detection.get("confidence", 0),
+                    "box": detection.get("box", {}),
+                    "keypoints": {
+                        keypoint['label']: keypoint['coordinates']
+                        for keypoint in detection.get('keypoints', [])
+                    }
+                }
+                detections.append(person)
+            else:
+                print(f"Warning: Empty detection in frame {frame_data['frame_index']}")
+    else:
+        # Handle the case where 'data' is missing in the frame data
+        print(f"Warning: 'data' key missing in frame data: {frame_data}")
+    
+    return detections
+
+
+
+def get_file_paths(base_path, split="train"):
+    """Returns video and JSON file paths."""
+    video_paths = []
+    json_paths = []
+    split_path = os.path.join(base_path, split)
+    
+    for label in ['Fight', 'NonFight']:
+        label_path = os.path.join(split_path, label)
+        for video_folder in os.listdir(label_path):
+            video_folder_path = os.path.join(label_path, video_folder)
+            video_file = next((f for f in os.listdir(video_folder_path) if any(f.endswith(ext) for ext in VIDEO_EXTENSIONS)), None)
+            json_file = next((f for f in os.listdir(video_folder_path) if any(f.endswith(ext) for ext in JSON_EXTENSIONS)), None)
+            
+            if video_file and json_file:
+                video_paths.append(os.path.join(video_folder_path, video_file))
+                json_paths.append(os.path.join(video_folder_path, json_file))
+    
+    return video_paths, json_paths
+
+def load_data(base_path, split="train"):
+    """Loads and processes the data for a given split (train or val)."""
+    video_paths, json_paths = get_file_paths(base_path, split)
+    dataset = []
+    
+    for video_path, json_path in zip(video_paths, json_paths):
+        # Load video frames
+        frames = load_video(video_path)
+        
+        # Load JSON keypoints
+        keypoints_data = load_json(json_path)
+        
+        # Process the data
+        frame_data = [process_frame_data(frame) for frame in keypoints_data]
+        
+        # Construct the data record
+        dataset.append({
+            'video': frames,
+            'keypoints': frame_data,
+            'video_path': video_path,
+            'json_path': json_path
+        })
+    
+    return dataset
+
+def main():
+    # Path to the dataset directory
+    dataset_dir = '.'  # Replace with your actual dataset path
+    
+    # Load training and validation data
+    train_data = load_data(dataset_dir, split="train")
+    val_data = load_data(dataset_dir, split="val")
+    
+    # Convert to Hugging Face Dataset
+    train_features = Features({
+        'video': Array3D(dtype='int32', shape=(None, None, None)),  # None indicates variable sizes
+        'keypoints': Sequence(Features({
+            'person_id': Value('int32'),
+            'confidence': Value('float32'),
+            'box': {
+                'x1': Value('float32'),
+                'y1': Value('float32'),
+                'x2': Value('float32'),
+                'y2': Value('float32')
+            },
+            'keypoints': {key: Array2D(dtype='float32', shape=(2,)) for key in KEYPOINTS}
+        })),
+        'video_path': Value('string'),
+        'json_path': Value('string')
+    })
+    
+    # Create DatasetDict
+    dataset_dict = DatasetDict({
+        'train': Dataset.from_dict(train_data, features=train_features),
+        'val': Dataset.from_dict(val_data, features=train_features)
+    })
+    
+    # Save or push dataset to Hugging Face
+    dataset_dict.save_to_disk("keypoints_keyger")
+    # Or to upload: dataset_dict.push_to_hub("your_dataset_name")
+
+if __name__ == "__main__":
+    main()