app.py

import os
os.system('pip install gradio==4.29.0')

import random
from dataclasses import dataclass
from typing import Any, List, Dict, Optional, Union, Tuple
import cv2
import torch
import requests
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from transformers import AutoModelForMaskGeneration, AutoProcessor, pipeline
import gradio as gr
import spaces
import json

@dataclass
class BoundingBox:
    xmin: int
    ymin: int
    xmax: int
    ymax: int

    @property
    def xyxy(self) -> List[float]:
        return [self.xmin, self.ymin, self.xmax, self.ymax]

@dataclass
class DetectionResult:
    score: float
    label: str
    box: BoundingBox
    mask: Optional[np.ndarray] = None

    @classmethod
    def from_dict(cls, detection_dict: Dict) -> 'DetectionResult':
        return cls(
            score=detection_dict['score'],
            label=detection_dict['label'],
            box=BoundingBox(
                xmin=detection_dict['box']['xmin'],
                ymin=detection_dict['box']['ymin'],
                xmax=detection_dict['box']['xmax'],
                ymax=detection_dict['box']['ymax']
            )
        )

def mask_to_min_max(mask):
    """Convert mask to min and max coordinates of the bounding box."""
    y, x = np.where(mask)
    xmin, xmax = x.min(), x.max()
    ymin, ymax = y.min(), y.max()
    return xmin, ymin, xmax, ymax

def extract_and_paste_insect(original_image, detection, background):
    mask = detection.mask
    xmin, ymin, xmax, ymax = mask_to_min_max(mask)
    insect_crop = original_image[ymin:ymax, xmin:xmax]
    mask_crop = mask[ymin:ymax, xmin:xmax]

    insect = cv2.bitwise_and(insect_crop, insect_crop, mask=mask_crop)
    x_offset, y_offset = detection.box.xmin, detection.box.ymin
    x_end, y_end = x_offset + insect.shape[1], y_offset + insect.shape[0]

    inverse_mask = cv2.bitwise_not(mask_crop)
    bg_region = background[y_offset:y_end, x_offset:x_end]
    bg_ready = cv2.bitwise_and(bg_region, bg_region, mask=inverse_mask)
    combined = cv2.add(insect, bg_ready)
    background[y_offset:y_end, x_offset:x_end] = combined

def create_yellow_background_with_insects(image, detections):
    # Create a plain yellow background
    yellow_background = np.full_like(image, (0, 255, 255), dtype=np.uint8)

    # Extract and paste each insect on the background
    for detection in detections:
        if detection.mask is not None:
            extract_and_paste_insect(image, detection, yellow_background)

    return yellow_background

def run_length_encoding(mask):
    pixels = mask.flatten()
    rle = []
    last_val = 0
    count = 0
    for pixel in pixels:
        if pixel == last_val:
            count += 1
        else:
            if count > 0:
                rle.append(count)
            count = 1
            last_val = pixel
    if count > 0:
        rle.append(count)
    return rle

def detections_to_json(detections):
    detections_list = []
    for detection in detections:
        detection_dict = {
            "score": detection.score,
            "label": detection.label,
            "box": {
                "xmin": detection.box.xmin,
                "ymin": detection.box.ymin,
                "xmax": detection.box.xmax,
                "ymax": detection.box.ymax
            },
            "mask": run_length_encoding(detection.mask) if detection.mask is not None else None
        }
        detections_list.append(detection_dict)
    return detections_list

def process_image(image):
    labels = ["insect"]
    original_image, detections = grounded_segmentation(image, labels, threshold=0.3, polygon_refinement=True)
    yellow_background_with_insects = create_yellow_background_with_insects(np.array(original_image), detections)
    detections_json = detections_to_json(detections)
    json_output_path = "insect_detections.json"
    with open(json_output_path, 'w') as json_file:
        json.dump(detections_json, json_file, indent=4)
    return yellow_background_with_insects, json.dumps(detections_json, separators=(',', ':'))

gr.Interface(
    fn=process_image,
    inputs=gr.Image(type="pil"),
    outputs=[gr.Image(type="numpy"), gr.Textbox()],
    title="🐞 InsectSAM + GroundingDINO Inference",
).launch()