app.py

import cv2
import tempfile
import gradio as gr
from ultralytics import YOLO
import pandas as pd
import plotly.graph_objects as go
import numpy as np

# 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):
    # 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]) if box.id is not None else -1  # Use -1 if ID is not available

                    # 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 interactive plot from the CSV data
    fig = go.Figure()

    for insect_id, group in df_from_csv.groupby('insect_id'):
        class_name = label_mapping[group.iloc[0]['class']]
        color = 'rgb({}, {}, {})'.format(*np.random.randint(0, 256, 3))
        hover_text = group.apply(lambda row: f'Insect ID: {int(row["insect_id"])}, Class: {class_name}, Frame: {int(row["frame"])}', axis=1)
        fig.add_trace(go.Scatter(x=group['frame'], y=group['insect_id'], mode='markers', marker=dict(color=color), name=f'{class_name} {insect_id}', 
                                 hoverinfo='text', hovertext=hover_text))

    fig.update_layout(title='Temporal distribution of insects',
                      xaxis_title='Frame',
                      yaxis_title='Insect ID',
                      hovermode='closest')

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

    return fig, gallery_items, csv_path

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

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

description = """
Uncover the Secret Lives of Insects of the Amazonian Forest! 🐝🦋🕷️

Upload your video now to track, visualize, and explore insect activity with our cutting-edge detection tool. You can get started with the example video.
"""

gr.Interface(fn=process_video, inputs=inputs, outputs=outputs, title= 'InsectSpy 🕵️‍♂️🦗', description=description, examples=[example_video]).launch()