app.py

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
from io import BytesIO
import tempfile

# Check if CUDA is available, otherwise use CPU
device = 'cuda' if torch.cuda.is_available() else 'cpu'

processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16")
model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16").to(device)

@spaces.GPU(duration=120)
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

    processed_frames = []
    frame_scores = []

    for time in 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

        pil_img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
        
        # Process single image
        inputs = processor(text=[target], images=pil_img, return_tensors="pt", padding=True).to(device)
        outputs = model(**inputs)

        target_sizes = torch.Tensor([pil_img.size[::-1]])
        results = processor.post_process_object_detection(outputs, target_sizes=target_sizes)
        
        draw = ImageDraw.Draw(pil_img)
        max_score = 0

        try:
            font = ImageFont.truetype("arial.ttf", 40)
        except IOError:
            font = ImageFont.load_default()

        boxes, scores, labels = results[0]["boxes"], results[0]["scores"], results[0]["labels"]

        for box, score, label in zip(boxes, scores, labels):
            if score.item() >= 0.25:
                box = [round(i, 2) for i in box.tolist()]
                object_label = target
                confidence = round(score.item(), 3)
                annotation = f"{object_label}: {confidence}"

                draw.rectangle(box, outline="red", width=2)
                text_position = (box[0], box[1] - 30)
                draw.text(text_position, annotation, fill="white", font=font)

                max_score = max(max_score, confidence)

        processed_frames.append(np.array(pil_img))
        frame_scores.append(max_score)

    cap.release()
    return processed_frames, frame_scores, None

def create_heatmap(frame_scores, current_frame):
    plt.figure(figsize=(12, 3))
    plt.imshow([frame_scores], cmap='hot_r', aspect='auto')  # 'hot_r' for reversed hot colormap
    cbar = plt.colorbar(label='Confidence')
    cbar.ax.yaxis.set_ticks_position('left')
    cbar.ax.yaxis.set_label_position('left')
    plt.title('Object Detection Heatmap')
    plt.xlabel('Frame')
    plt.yticks([])

    # Add more frame numbers on x-axis
    num_frames = len(frame_scores)
    step = max(1, num_frames // 10)  # Show at most 10 frame numbers
    frame_numbers = range(0, num_frames, step)
    plt.xticks(frame_numbers, [str(i) for i in frame_numbers])

    # Add vertical line for current frame
    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):
    cap = cv2.VideoCapture(video_path)
    if not cap.isOpened():
        return None
    ret, frame = cap.read()
    cap.release()
    if not ret:
        return None
    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    return frame_rgb

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"), label="Drone Video of African Wildlife Wild Botswan by wildimagesonline.com - Sample Video Frame")
        use_sample_button = gr.Button("Use Sample Video")
        progress_bar = gr.Progress()

        processed_frames = gr.State([])
        frame_scores = gr.State([])

        def process_and_update(video, target):
            frames, scores, error = process_video(video, target, progress_bar)
            if frames is not None:
                heatmap_path = create_heatmap(scores, 0)  # Initial heatmap with current frame at 0
                return frames, scores, frames[0], heatmap_path, error, gr.Slider(maximum=len(frames) - 1, value=0)
            return None, None, None, None, error, gr.Slider(maximum=100, value=0)

        def update_frame_and_heatmap(frame_index, frames, scores):
            if frames and 0 <= frame_index < len(frames):
                heatmap_path = create_heatmap(scores, frame_index)
                return frames[frame_index], heatmap_path
            return None, None

        video_input.upload(process_and_update, 
                           inputs=[video_input, target_input], 
                           outputs=[processed_frames, frame_scores, output_image, heatmap_output, error_output, frame_slider])

        frame_slider.change(update_frame_and_heatmap, 
                            inputs=[frame_slider, processed_frames, 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=[processed_frames, 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(share=True)