app.py

import cv2
import gradio as gr
import numpy as np
import os
import datetime

# Load YOLO model
net = cv2.dnn.readNetFromDarknet('yolov3.cfg', 'yolov3.weights')

# Set classes
classes = []
with open('coco.names', 'r') as f:
    classes = [line.strip() for line in f.readlines()]

# Function to detect objects in a video frame
def detect_birds(video_file):
    cap = cv2.VideoCapture(video_file)
    frame_count = 0
    output_frames = []

    # Variables for object count and duration
    object_counts = {class_name: 0 for class_name in classes}
    object_durations = {class_name: datetime.timedelta() for class_name in classes}
    last_frame_time = None

    while True:
        ret, frame = cap.read()
        if not ret:
            break

        if frame is None:
            continue

        height, width, _ = frame.shape

        # Create a blob from the frame and pass it through the network
        blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False)
        net.setInput(blob)
        layer_names = net.getLayerNames()
        output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
        detections = net.forward(output_layers)

        # Process detections
        boxes = []
        confidences = []
        class_ids = []
        for detection in detections:
            for detection_result in detection:
                scores = detection_result[5:]
                class_id = np.argmax(scores)
                confidence = scores[class_id]

                if confidence > 0.5:
                    center_x = int(detection_result[0] * width)
                    center_y = int(detection_result[1] * height)
                    w = int(detection_result[2] * width)
                    h = int(detection_result[3] * height)

                    x = int(center_x - w / 2)
                    y = int(center_y - h / 2)

                    boxes.append([x, y, w, h])
                    confidences.append(float(confidence))
                    class_ids.append(class_id)

        # Apply non-maxima suppression to eliminate redundant overlapping boxes
        indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

        # Draw bounding boxes and labels
        if len(indices) > 0:
            for i in indices.flatten():
                x, y, w, h = boxes[i]
                label = classes[class_ids[i]]
                confidence = confidences[i]

                cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
                cv2.putText(frame, f'{label} {confidence:.2f}', (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
                            (0, 255, 0), 2)

                # Update object count and duration
                object_counts[label] += 1
                if last_frame_time is not None:
                    duration = datetime.datetime.now() - last_frame_time
                    object_durations[label] += duration
                last_frame_time = datetime.datetime.now()

        # Save the frame with bounding boxes as an image
        output_frame_path = f'output_frames/frame_{frame_count:04d}.jpg'
        cv2.imwrite(output_frame_path, frame)
        output_frames.append(output_frame_path)

        frame_count += 1

    cap.release()

    # Combine the output frames into a video file
    output_video_path = 'output.mp4'
    if frame_count > 0:
        frame = cv2.imread(output_frames[0])
        if frame is not None:
            height, width, _ = frame.shape

            fourcc = cv2.VideoWriter_fourcc(*'mp4v')
            writer = cv2.VideoWriter(output_video_path, fourcc, 30, (width, height))

            for frame_path in output_frames:
                frame = cv2.imread(frame_path)
                if frame is not None:
                    writer.write(frame)

            writer.release()
        else:
            output_video_path = None
    else:
        output_video_path = None

    cv2.destroyAllWindows()

    # Remove the output frames directory
    for frame_path in output_frames:
        os.remove(frame_path)

    # Format object count and duration as text
    count_text = '\n'.join([f'{label}: {count}' for label, count in object_counts.items() if count > 0])
    duration_text = '\n'.join([f'{label}: {str(duration).split(".")[0]}' for label, duration in object_durations.items() if duration.total_seconds() > 0])

    return output_video_path, count_text, duration_text

# Create a Gradio interface
inputs = gr.inputs.Video(label='Input Video')
outputs = [
    gr.outputs.Video(label='Output Video'),
    gr.outputs.Textbox(label='Object Count', type='text'),
    gr.outputs.Textbox(label='Duration', type='text')
]

gr.Interface(fn=detect_birds, inputs=inputs, outputs=outputs, capture_session=True, share=True).launch()