app.py

import gradio as gr
import torch
import cv2
import os
import shutil

# Load the model
model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt')

def detect_image(image):
    results = model(image)
    return results.render()[0]


def detect_video(video_path):
    video = cv2.VideoCapture(video_path)
    frame_rate = video.get(cv2.CAP_PROP_FPS)
    
    # Create a directory to store the frames
    frames_dir = 'frames'
    os.makedirs(frames_dir, exist_ok=True)
    
    # Remove existing contents of the frames directory
    for file_name in os.listdir(frames_dir):
        file_path = os.path.join(frames_dir, file_name)
        if os.path.isfile(file_path):
            os.remove(file_path)

    frame_count = 0
    process_frame_count = 0
    while True:
        success, frame = video.read()
        if not success:
            break
        if frame_count % 2 == 0:  # Process every 2nd frame (adjust as needed)
            frame_output_path = os.path.join(frames_dir, f'frame_{process_frame_count:04d}.jpg')
            cv2.imwrite(frame_output_path, frame)
            process_frame_count += 1
        frame_count += 1

    video.release()
    cv2.destroyAllWindows()

    # Process the frames with object detection and save the results
    results_dir = 'results'
    os.makedirs(results_dir, exist_ok=True)
    
    # Remove existing contents of the results directory
    for file_name in os.listdir(results_dir):
        file_path = os.path.join(results_dir, file_name)
        if os.path.isfile(file_path):
            os.remove(file_path)

    for i in range(process_frame_count):
        frame_path = os.path.join(frames_dir, f'frame_{i:04d}.jpg')
        frame = cv2.imread(frame_path)
        results = model(frame)
        results_output_path = os.path.join(results_dir, f'results_{i:04d}.jpg')
        cv2.imwrite(results_output_path, results.render()[0])

    # Create the output video from the processed frames
    frame_files = sorted(os.listdir(results_dir))
    frame_path = os.path.join(results_dir, frame_files[0])
    frame = cv2.imread(frame_path)
    height, width, _ = frame.shape

    video_output_path = 'output_video.mp4'  # Replace with your desired output video path
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # You can change the codec as needed
    video_writer = cv2.VideoWriter(video_output_path, fourcc, frame_rate/2, (width, height))  # Adjust frame rate

    for frame_file in frame_files:
        frame_path = os.path.join(results_dir, frame_file)
        frame = cv2.imread(frame_path)
        video_writer.write(frame)

    video_writer.release()

    # Clean up the temporary directories
    shutil.rmtree(frames_dir)
    shutil.rmtree(results_dir)

    return video_output_path

# Create Gradio interfaces for different modes
img_interface = gr.Interface(
    fn=detect_image,
    examples=[os.path.join(os.path.abspath(''), "plastic_bottles1.jpg")],
    inputs=gr.inputs.Image(source="upload"),
    outputs="image",
    title="Image",
    cache_examples=True
)

vid_interface = gr.Interface(
    fn=detect_video,
    inputs=gr.inputs.Video(source="upload"),
    examples=[
        os.path.join(os.path.abspath(''), "a-plastic-bag-is-floating-in-a-sea-ocean-plastic-pollution-big-environmental-p-SBV-347235576-preview.mp4"),
        os.path.join(os.path.abspath(''), "hand-woman-in-yellow-gloves-picking-up-empty-plastic-bottles-cleaning-on-the-b-SBV-346452144-preview.mp4"),
        os.path.join(os.path.abspath(''), "plastic-bottle-being-dumped-in-to-the-sea-ocean-pollution-in-ocean-is-a-big-en-SBV-347235586-preview.mp4"),
        os.path.join(os.path.abspath(''), "pollution-garbages-plastic-and-wastes-on-the-beach-after-winter-storms-SBV-331355306-preview.mp4"),
        os.path.join(os.path.abspath(''), "volunteer-woman-picking-plastic-bottle-into-trash-plastic-bag-black-for-cleani-SBV-346871657-preview.mp4")
    ],
    outputs="video",
    title="Video",
    cache_examples=True
)

# Add examples
# with gr.Blocks() as demo:
#     gr.Examples(
#         inputs= "image",
#         outputs= "image",
#         fn=detect_image,
#     )

# Create a list of interfaces
interfaces = [img_interface, vid_interface]

# Create the tabbed interface
tabbed_interface = gr.TabbedInterface(interfaces, ["Image", "Video"])

# Launch the tabbed interface 
tabbed_interface.launch(debug=True)