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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()
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