Datasets:
File size: 4,859 Bytes
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import json
import webdataset as wds
import io
import decord
import numpy as np
import torch
import matplotlib.pyplot as plt
import glob
import cv2
from pathlib import Path
import concurrent.futures
import os
import argparse
import sys
from huggingface_hub import HfFileSystem, get_token, hf_hub_url
executor = concurrent.futures.ThreadPoolExecutor(
max_workers=None,
thread_name_prefix="JPG_Saver"
)
fs = HfFileSystem()
files = [fs.resolve_path(path) for path in fs.glob("hf://datasets/CVML-TueAI/grounding-YT-dataset/frames/*.tar")]
urls = [hf_hub_url(file.repo_id, file.path_in_repo, repo_type="dataset") for file in files]
urls = f"pipe: curl -s -L -H 'Authorization:Bearer {get_token()}' {'::'.join(urls)}"
PRED_FILE = 'random_preds.json'
OUTPUT_DIR = Path('./output_annotations')
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
def save_annotated_frame(image_array_rgb, bbox, point, gt_action, pred_action, output_path):
COLOR_GT = (0, 150, 0) # Green
COLOR_PRED = (0, 0, 255) # Red
COLOR_BOX = (255, 0, 0) # Blue
COLOR_POINT = (0, 0, 255) # Red
if gt_action == pred_action:
COLOR_PRED = (0, 150, 0) # Make prediction green if correct
TOP_PADDING = 70 # Pixels to add for the title header
TEXT_OFFSET_X = 10
image_bgr = cv2.cvtColor(image_array_rgb, cv2.COLOR_RGB2BGR)
h, w = image_bgr.shape[:2]
final_image = np.full((h + TOP_PADDING, w, 3), 255, dtype=np.uint8)
final_image[TOP_PADDING : h + TOP_PADDING, 0:w] = image_bgr
cv2.putText(
final_image,
f"Ground Truth: {gt_action}",
(TEXT_OFFSET_X, 30), # Position (x, y)
cv2.FONT_HERSHEY_SIMPLEX, # Font
0.8, # Font scale
COLOR_GT, # Color
2 # Thickness
)
cv2.putText(
final_image,
f"Prediction: {str(pred_action)}", #Because pred_action can be None if not present
(TEXT_OFFSET_X, 60), # Position (x, y)
cv2.FONT_HERSHEY_SIMPLEX,
0.8,
COLOR_PRED,
2
)
# Bounding Box
x_min, y_min, x_max, y_max = [int(coord) for coord in bbox] # Get coordinates
# Top-left corner (x1, y1)
pt1 = (x_min, y_min + TOP_PADDING)
# Bottom-right corner (x2, y2)
pt2 = (x_max, y_max + TOP_PADDING)
cv2.rectangle(
final_image,
pt1,
pt2,
COLOR_BOX,
thickness=2
)
# Point
a, b = point
pt_center = (a, b + TOP_PADDING)
#Dot
cv2.circle(
final_image,
pt_center,
radius=3,
color=COLOR_POINT,
thickness=-1
)
#Outer cirlce
cv2.circle(
final_image,
pt_center,
radius=10,
color=(255, 255, 255),
thickness=2
)
cv2.imwrite(output_path, final_image, [int(cv2.IMWRITE_JPEG_QUALITY), 95])
print(f"Saved annotated image to {output_path}")
def main():
dataset = (
wds.WebDataset(urls, shardshuffle=False)
.decode('torchrgb')
.to_tuple("__key__","jpg", "json")
)
parser = argparse.ArgumentParser()
parser.add_argument(
"--predictions", type=str, required=True, help="Path to json file with predictions for each clip"
)
args = parser.parse_args()
with open(args.predictions, 'r', encoding='utf-8') as f:
preds = json.load(f)
for key, image_tensor, meta in dataset:
frame_no = meta['frame'] #int
video_name = meta['video']
if preds.get(key) is not None: #frame prediction present
image_hwc = image_tensor.permute(1,2,0) #image_tensor is [C,H,W] -> change to [H,W,C]
image_scaled = image_hwc * 255.0 #int pixel values
image_numpy_uint8 = image_scaled.numpy().astype(np.uint8) #change from tensor to numpy
pred_point = preds[key].get(str(frame_no)).get('point')
pred_action = preds[key].get(str(frame_no)).get('action')
output_dir = OUTPUT_DIR / 'frames' / video_name
output_dir.mkdir(parents=True, exist_ok=True)
output_img = output_dir / f'{key}.jpg'
#image_array_rgb, bbox, point, gt_action, pred_action, output_path
executor.submit(
save_annotated_frame,
image_array_rgb=image_numpy_uint8,
bbox=meta['box'],
point = pred_point,
gt_action=meta['step_name'],
pred_action = pred_action,
output_path = output_img
)
print("Main loop finished. Waiting for file saving to complete...")
executor.shutdown(wait=True)
print("All files saved.")
if __name__ == '__main__':
main()
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