app.py

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

@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.array] = 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 annotate(image: Union[Image.Image, np.ndarray], detection_results: List[DetectionResult]) -> np.ndarray:
    image_cv2 = np.array(image) if isinstance(image, Image.Image) else image
    image_cv2 = cv2.cvtColor(image_cv2, cv2.COLOR_RGB2BGR)

    for detection in detection_results:
        label = detection.label
        score = detection.score
        box = detection.box
        mask = detection.mask

        color = np.random.randint(0, 256, size=3)

        cv2.rectangle(image_cv2, (box.xmin, box.ymin), (box.xmax, box.ymax), color.tolist(), 2)
        cv2.putText(image_cv2, f'{label}: {score:.2f}', (box.xmin, box.ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color.tolist(), 2)

        if mask is not None:
            mask_uint8 = (mask * 255).astype(np.uint8)
            contours, _ = cv2.findContours(mask_uint8, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
            cv2.drawContours(image_cv2, contours, -1, color.tolist(), 2)

    return cv2.cvtColor(image_cv2, cv2.COLOR_BGR2RGB)

def plot_detections(image: Union[Image.Image, np.ndarray], detections: List[DetectionResult]) -> np.ndarray:
    annotated_image = annotate(image, detections)
    return annotated_image

def load_image(image: Union[str, Image.Image]) -> Image.Image:
    if isinstance(image, str) and image.startswith("http"):
        image = Image.open(requests.get(image, stream=True).raw).convert("RGB")
    elif isinstance(image, str):
        image = Image.open(image).convert("RGB")
    else:
        image = image.convert("RGB")
    return image

def get_boxes(detection_results: List[DetectionResult]) -> List[List[List[float]]]:
    boxes = []
    for result in detection_results:
        xyxy = result.box.xyxy
        boxes.append(xyxy)
    return [boxes]

def refine_masks(masks: torch.BoolTensor, polygon_refinement: bool = False) -> List[np.ndarray]:
    masks = masks.cpu().float().permute(0, 2, 3, 1).mean(axis=-1).numpy().astype(np.uint8)
    masks = (masks > 0).astype(np.uint8)
    if polygon_refinement:
        for idx, mask in enumerate(masks):
            shape = mask.shape
            polygon = mask_to_polygon(mask)
            masks[idx] = polygon_to_mask(polygon, shape)
    return list(masks)

def detect(image: Image.Image, labels: List[str], threshold: float = 0.3, detector_id: Optional[str] = None) -> List[Dict[str, Any]]:
    device = "cuda" if torch.cuda.is_available() else "cpu"
    detector_id = detector_id if detector_id else "IDEA-Research/grounding-dino-base"
    object_detector = pipeline(model=detector_id, task="zero-shot-object-detection", device=device)
    labels = [label if label.endswith(".") else label+"." for label in labels]
    results = object_detector(image, candidate_labels=labels, threshold=threshold)
    return [DetectionResult.from_dict(result) for result in results]

def segment(image: Image.Image, detection_results: List[DetectionResult], polygon_refinement: bool = False, segmenter_id: Optional[str] = None) -> List[DetectionResult]:
    device = "cuda" if torch.cuda.is_available() else "cpu"
    segmenter_id = segmenter_id if segmenter_id else "martintmv/InsectSAM"
    segmentator = AutoModelForMaskGeneration.from_pretrained(segmenter_id).to(device)
    processor = AutoProcessor.from_pretrained(segmenter_id)

    boxes = get_boxes(detection_results)
    inputs = processor(images=image, input_boxes=boxes, return_tensors="pt").to(device)
    outputs = segmentator(**inputs)
    masks = processor.post_process_masks(masks=outputs.pred_masks, original_sizes=inputs.original_sizes, reshaped_input_sizes=inputs.reshaped_input_sizes)[0]
    masks = refine_masks(masks, polygon_refinement)

    for detection_result, mask in zip(detection_results, masks):
        detection_result.mask = mask

    return detection_results

def grounded_segmentation(image: Union[Image.Image, str], labels: List[str], threshold: float = 0.3, polygon_refinement: bool = False, detector_id: Optional[str] = None, segmenter_id: Optional[str] = None) -> Tuple[np.ndarray, List[DetectionResult]]:
    image = load_image(image)
    detections = detect(image, labels, threshold, detector_id)
    detections = segment(image, detections, polygon_refinement, segmenter_id)
    return np.array(image), detections

def extract_insect_masks(image: np.ndarray, detections: List[DetectionResult]) -> List[np.ndarray]:
    return [detection.mask for detection in detections if detection.mask is not None]

def put_masks_on_yellow_background(image_shape: Tuple[int, int], masks: List[np.ndarray]) -> np.ndarray:
    yellow_background = np.full((image_shape[0], image_shape[1], 3), (0, 255, 255), dtype=np.uint8)
    for mask in masks:
        mask_rgb = cv2.cvtColor(mask, cv2.COLOR_GRAY2RGB)
        for c in range(3):
            yellow_background[:,:,c] = cv2.bitwise_or(yellow_background[:,:,c], mask_rgb[:,:,c])
    return yellow_background

def mask_to_min_max(mask: np.ndarray) -> Tuple[int, int, int, int]:
    y, x = np.where(mask)
    return x.min(), y.min(), x.max(), y.max()

def extract_and_paste_insect(original_image: np.ndarray, detection: DetectionResult, background: np.ndarray) -> None:
    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: np.ndarray, detections: List[DetectionResult]) -> np.ndarray:
    yellow_background = np.full_like(image, (0, 255, 255), dtype=np.uint8)
    for detection in detections:
        if detection.mask is not None:
            extract_and_paste_insect(image, detection, yellow_background)
    return yellow_background

def draw_classification_boxes(image_with_insects: np.ndarray, detections: List[DetectionResult]) -> np.ndarray:
    for detection in detections:
        label = detection.label
        score = detection.score
        box = detection.box
        color = np.random.randint(0, 256, size=3).tolist()
        cv2.rectangle(image_with_insects, (box.xmin, box.ymin), (box.xmax, box.ymax), color, 2)
        (text_width, text_height), baseline = cv2.getTextSize(f"{label}: {score:.2f}", cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2)
        cv2.rectangle(image_with_insects, (box.xmin, box.ymin - text_height - baseline), (box.xmin + text_width, box.ymin), color, thickness=cv2.FILLED)
        cv2.putText(image_with_insects, f"{label}: {score:.2f}", (box.xmin, box.ymin - baseline), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
    return image_with_insects

def process_image(image):
    labels = ["ant", "beetle", "butterfly", "caterpillar", "dragonfly"]
    original_image, detections = grounded_segmentation(image, labels, threshold=0.3, polygon_refinement=True)
    masked_image = plot_detections(original_image, detections)
    insect_masks = extract_insect_masks(original_image, detections)
    yellow_background_with_masks = put_masks_on_yellow_background(original_image.shape[:2], insect_masks)
    yellow_background_with_insects = create_yellow_background_with_insects(original_image, detections)
    yellow_background_with_boxes = draw_classification_boxes(yellow_background_with_insects, detections)

    return masked_image, yellow_background_with_masks, yellow_background_with_boxes

gr.Interface(
    fn=process_image,
    inputs=gr.Image(type="pil"),
    outputs=[gr.Image(type="numpy"), gr.Image(type="numpy"), gr.Image(type="numpy")],
    title="Insect Detection and Masking"
).launch()