import spaces import gradio as gr import cv2 from PIL import Image, ImageDraw, ImageFont import torch from transformers import Owlv2Processor, Owlv2ForObjectDetection import numpy as np import os import matplotlib.pyplot as plt import tempfile import shutil device = "cuda" processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16") model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16") model = model.to(device) def process_video(video_path, target, progress=gr.Progress()): if video_path is None: return None, None, "Error: No video uploaded" if not os.path.exists(video_path): return None, None, f"Error: Video file not found at {video_path}" cap = cv2.VideoCapture(video_path) if not cap.isOpened(): return None, None, f"Error: Unable to open video file at {video_path}" frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) original_fps = int(cap.get(cv2.CAP_PROP_FPS)) output_fps = 1 frame_duration = 1 / output_fps video_duration = frame_count / original_fps frame_scores = [] temp_dir = tempfile.mkdtemp() frame_paths = [] batch_size = 1 batch_frames = [] batch_indices = [] for i, time in enumerate(progress.tqdm(np.arange(0, video_duration, frame_duration))): frame_number = int(time * original_fps) cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number) ret, img = cap.read() if not ret: break # Convert to RGB without resizing pil_img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) batch_frames.append(pil_img) batch_indices.append(i) if len(batch_frames) == batch_size or i == int(video_duration / frame_duration) - 1: # Process batch inputs = processor(text=[target] * len(batch_frames), images=batch_frames, return_tensors="pt", padding=True).to(device) with torch.no_grad(): outputs = model(**inputs) target_sizes = torch.Tensor([pil_img.size[::-1] for _ in batch_frames]).to(device) results = processor.post_process_object_detection(outputs, target_sizes=target_sizes) for idx, (pil_img, result) in enumerate(zip(batch_frames, results)): draw = ImageDraw.Draw(pil_img) max_score = 0 boxes, scores, labels = result["boxes"], result["scores"], result["labels"] # Inside the loop where bounding boxes are drawn for box, score, label in zip(boxes, scores, labels): if score.item() >= 0.5: box = [round(i, 2) for i in box.tolist()] object_label = target confidence = round(score.item(), 3) annotation = f"{object_label}: {confidence}" # Increase line width for the bounding box draw.rectangle(box, outline="red", width=3) # Calculate font size based on image dimensions img_width, img_height = pil_img.size font_size = int(min(img_width, img_height) * 0.03) # 3% of the smaller dimension try: font = ImageFont.truetype("arial.ttf", font_size) except IOError: font = ImageFont.load_default() # Calculate text size text_bbox = draw.textbbox((0, 0), annotation, font=font) text_width = text_bbox[2] - text_bbox[0] text_height = text_bbox[3] - text_bbox[1] # Position text inside the top of the bounding box text_position = (box[0], box[1]) # Draw semi-transparent background for text draw.rectangle([text_position[0], text_position[1], text_position[0] + text_width, text_position[1] + text_height], fill=(0, 0, 0, 128)) # Draw text in red draw.text(text_position, annotation, fill="red", font=font) max_score = max(max_score, confidence) frame_path = os.path.join(temp_dir, f"frame_{batch_indices[idx]:04d}.png") pil_img.save(frame_path) frame_paths.append(frame_path) frame_scores.append(max_score) # Clear batch batch_frames = [] batch_indices = [] # Clear GPU cache every 10 frames if i % 10 == 0: torch.cuda.empty_cache() cap.release() return frame_paths, frame_scores, None def create_heatmap(frame_scores, current_frame): plt.figure(figsize=(16, 4)) plt.imshow([frame_scores], cmap='hot_r', aspect='auto') plt.title('Object Detection Heatmap', fontsize=14) plt.xlabel('Frame', fontsize=12) plt.yticks([]) num_frames = len(frame_scores) step = max(1, num_frames // 20) frame_numbers = range(0, num_frames, step) plt.xticks(frame_numbers, [str(i) for i in frame_numbers], rotation=90, ha='right') plt.axvline(x=current_frame, color='blue', linestyle='--', linewidth=2) plt.tight_layout() with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as tmp_file: plt.savefig(tmp_file.name, format='png', dpi=400, bbox_inches='tight') plt.close() return tmp_file.name def load_sample_frame(video_path, target_frame=87, original_fps=30, processing_fps=1): cap = cv2.VideoCapture(video_path) if not cap.isOpened(): return None # Calculate the corresponding frame number in the original video original_frame_number = int(target_frame * (original_fps / processing_fps)) # Set the frame position cap.set(cv2.CAP_PROP_POS_FRAMES, original_frame_number) ret, frame = cap.read() cap.release() if not ret: return None frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) return frame_rgb def update_frame_and_heatmap(frame_index, frame_paths, scores): if frame_paths and 0 <= frame_index < len(frame_paths): frame = Image.open(frame_paths[frame_index]) heatmap_path = create_heatmap(scores, frame_index) return np.array(frame), heatmap_path return None, None def gradio_app(): with gr.Blocks() as app: gr.Markdown("# Video Object Detection with Owlv2") video_input = gr.Video(label="Upload Video") target_input = gr.Textbox(label="Target Object", value="Elephant") frame_slider = gr.Slider(minimum=0, maximum=100, step=1, label="Frame", value=0) heatmap_output = gr.Image(label="Detection Heatmap") output_image = gr.Image(label="Processed Frame") error_output = gr.Textbox(label="Error Messages", visible=False) sample_video_frame = gr.Image( value=load_sample_frame("Drone Video of African Wildlife Wild Botswan.mp4", target_frame=87), label="Drone Video of African Wildlife Wild Botswan by wildimagesonline.com - Sample Video Frame (Frame 87 at 1 FPS)" ) use_sample_button = gr.Button("Use Sample Video") progress_bar = gr.Progress() frame_paths = gr.State([]) frame_scores = gr.State([]) def process_and_update(video, target): paths, scores, error = process_video(video, target, progress_bar) if paths is not None: heatmap_path = create_heatmap(scores, 0) first_frame = Image.open(paths[0]) return paths, scores, np.array(first_frame), heatmap_path, error, gr.Slider(maximum=len(paths) - 1, value=0) return None, None, None, None, error, gr.Slider(maximum=100, value=0) video_input.upload(process_and_update, inputs=[video_input, target_input], outputs=[frame_paths, frame_scores, output_image, heatmap_output, error_output, frame_slider]) frame_slider.change(update_frame_and_heatmap, inputs=[frame_slider, frame_paths, frame_scores], outputs=[output_image, heatmap_output]) def use_sample_video(): sample_video_path = "Drone Video of African Wildlife Wild Botswan.mp4" return process_and_update(sample_video_path, "Elephant") use_sample_button.click(use_sample_video, inputs=None, outputs=[frame_paths, frame_scores, output_image, heatmap_output, error_output, frame_slider]) # Layout with gr.Row(): with gr.Column(scale=2): output_image with gr.Column(scale=1): sample_video_frame use_sample_button return app if __name__ == "__main__": app = gradio_app() app.launch() # Cleanup temporary files def cleanup(): for path in frame_paths.value: if os.path.exists(path): os.remove(path) if os.path.exists(temp_dir): shutil.rmtree(temp_dir) # Make sure to call cleanup when the app is closed # This might require additional setup depending on how you're running the app