Push frame visualization

visualization/frames/plot_frames.py

@@ -0,0 +1,167 @@
+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()
+
+