app.py

import cv2
import tempfile
import gradio as gr
from ultralytics import YOLO
import pandas as pd
import matplotlib.pyplot as plt

# Define the label mapping
label_mapping = {
    0: 'Hymenoptera',
    1: 'Mantodea',
    2: 'Odonata',
    3: 'Orthoptera',
    4: 'Coleoptera',
    5: 'Lepidoptera',
    6: 'Hemiptera'
}

def process_video(video_file):
    # Define colors for each class (8 classes)
    colors = [
        (255, 0, 0),   # Class 0 - Blue
        (50, 205, 50),   # Class 1 - Green
        (0, 0, 255),   # Class 2 - Red
        (255, 255, 0), # Class 3 - Cyan
        (255, 0, 255), # Class 4 - Magenta
        (255, 140, 0), # Class 5 - Orange
        (128, 0, 128), # Class 6 - Purple
        (0, 128, 128)  # Class 7 - Teal
    ]

    # Load the YOLOv8 model
    model = YOLO("insect_detection4.pt")

    # Open the video file
    cap = cv2.VideoCapture(video_file)

    # Prepare DataFrame for storing detection data
    columns = ["frame", "insect_id", "class", "x", "y", "w", "h"]
    df = pd.DataFrame(columns=columns)

    frame_id = 0
    unique_insect_crops = {}

    # Loop through the video frames
    while cap.isOpened():
        # Read a frame from the video
        success, frame = cap.read()

        if success:
            frame_id += 1

            # Run YOLOv8 tracking on the frame, persisting tracks between frames
            results = model.track(frame, persist=True, tracker="insect_tracker.yaml")

            for result in results:
                boxes = result.boxes.cpu().numpy()
                confidences = boxes.conf
                class_ids = boxes.cls

                for i, box in enumerate(boxes):
                    class_id = int(class_ids[i])
                    confidence = confidences[i]
                    insect_id = int(box.id[0])  # Ensure the ID is an integer

                    # Append detection data to DataFrame
                    new_row = pd.DataFrame({
                        "frame": [frame_id],
                        "insect_id": [insect_id],
                        "class": [class_id],
                        "x": [box.xywh[0][0]],
                        "y": [box.xywh[0][1]],
                        "w": [box.xywh[0][2]],
                        "h": [box.xywh[0][3]]
                    })
                    df = pd.concat([df, new_row], ignore_index=True)

                    # Crop and save the image of the insect
                    if insect_id not in unique_insect_crops:
                        x_center, y_center, width, height = box.xywh[0]
                        x1 = int(x_center - width / 2)
                        y1 = int(y_center - height / 2)
                        x2 = int(x_center + width / 2)
                        y2 = int(y_center + height / 2)
                        insect_crop = frame[y1:y2, x1:x2]
                        crop_path = tempfile.mktemp(suffix=".png")
                        cv2.imwrite(crop_path, insect_crop)
                        unique_insect_crops[insect_id] = (crop_path, label_mapping[class_id])

        else:
            break

    # Release the video capture
    cap.release()

    # Save DataFrame to CSV
    csv_path = tempfile.mktemp(suffix=".csv")
    df.to_csv(csv_path, index=False)

    # Read the DataFrame from the CSV file
    df_from_csv = pd.read_csv(csv_path)

    # Create the plot from the CSV data
    plt.figure(figsize=(10, 6))
    for insect_id in df_from_csv['insect_id'].unique():
        frames = df_from_csv[df_from_csv['insect_id'] == insect_id]['frame']
        insect_class = label_mapping[df_from_csv[df_from_csv['insect_id'] == insect_id]['class'].values[0]]
        plt.plot(frames, [insect_id] * len(frames), 'o-', label=f'{insect_class} {insect_id}')

    plt.xlabel('Frame')
    plt.ylabel('Insect ID')
    plt.title('Temporal distribution of insects')
    plt.legend()
    plt.grid(True)

    # Save the plot to a temporary file
    plot_path = tempfile.mktemp(suffix=".png")
    plt.savefig(plot_path)
    plt.close()

    gallery_items = [(crop_path, f'{label} {insect_id}') for insect_id, (crop_path, label) in unique_insect_crops.items()]

    return plot_path, gallery_items, csv_path

# Create a Gradio interface
example_video = "path_to_example_video.mp4"  # Replace with the actual path to your example video

inputs = gr.Video(label="Input Insect Trap Video", value=example_video)
outputs = [
    gr.Image(label="Insect Detection Plot"),
    gr.Gallery(label="Unique Insect Crops"),  # Added a gallery to display insect crops with labels
    gr.File(label="Download CSV")
]

gr.Interface(fn=process_video, inputs=inputs, outputs=outputs, examples=[example_video]).launch()