import os os.system('pip install gradio==4.29.0') # as gradio==4.29.0 doesn't work in requirements.txt 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 time import httpx @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 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).tolist() cv2.rectangle(image_cv2, (box.xmin, box.ymin), (box.xmax, box.ymax), color, 2) cv2.putText(image_cv2, f'{label}: {score:.2f}', (box.xmin, box.ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 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, 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 mask_to_polygon(mask: np.ndarray) -> np.ndarray: contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) if len(contours) == 0: return np.array([]) largest_contour = max(contours, key=cv2.contourArea) return largest_contour 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] = cv2.fillPoly(np.zeros(shape, dtype=np.uint8), [polygon], 1) return list(masks) def startup_report_with_retries(client, retries=5, delay=2): for i in range(retries): try: client.startup_report() return except httpx.ConnectTimeout: if i < retries - 1: time.sleep(delay) else: raise @spaces.GPU def detect(image: Image.Image, labels: List[str], threshold: float = 0.3, detector_id: Optional[str] = None) -> List[Dict[str, Any]]: 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="cuda") # Initialize and call startup report with retries client = httpx.Client() startup_report_with_retries(client) 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] @spaces.GPU def segment(image: Image.Image, detection_results: List[DetectionResult], polygon_refinement: bool = False, segmenter_id: Optional[str] = None) -> List[DetectionResult]: segmenter_id = segmenter_id if segmenter_id else "martintmv/InsectSAM" segmentator = AutoModelForMaskGeneration.from_pretrained(segmenter_id).to("cuda") processor = AutoProcessor.from_pretrained(segmenter_id) boxes = get_boxes(detection_results) inputs = processor(images=image, input_boxes=boxes, return_tensors="pt").to("cuda") 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 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] # Ensure that we keep the original colors of the insect insect = cv2.bitwise_and(insect_crop, insect_crop, mask=mask_crop) x_offset, y_offset = xmin, ymin x_end, y_end = x_offset + insect.shape[1], y_offset + insect.shape[0] # Place the insect onto the yellow background background[y_offset:y_end, x_offset:x_end] = insect def create_yellow_background_with_insects(image: np.ndarray, detections: List[DetectionResult]) -> np.ndarray: yellow_background = np.full((image.shape[0], image.shape[1], 3), (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, detections): for detection in detections: label = detection.label score = detection.score box = detection.box color = (0, 255, 255) # Yellow color for bounding box 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, (255, 255, 255), 2 ) return image_with_insects def process_image(image): labels = ["insect"] original_image, detections = grounded_segmentation(image, labels, threshold=0.3, polygon_refinement=True) annotated_image = plot_detections(original_image, detections) yellow_background_with_insects = create_yellow_background_with_insects(np.array(original_image), detections) yellow_background_with_boxes = draw_classification_boxes(yellow_background_with_insects.copy(), detections) return annotated_image, yellow_background_with_boxes gr.Interface( fn=process_image, inputs=gr.Image(type="pil"), outputs=[gr.Image(type="numpy"), gr.Image(type="numpy")], title="🐞 InsectSAM + GroundingDINO Inference", ).launch()