import os
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 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 annotate(image: Union[Image.Image, np.ndarray], detection_results: List[DetectionResult], include_bboxes: bool = True) -> 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
if include_bboxes:
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)
return cv2.cvtColor(image_cv2, cv2.COLOR_BGR2RGB)
def plot_detections(image: Union[Image.Image, np.ndarray], detections: List[DetectionResult], include_bboxes: bool = True) -> np.ndarray:
annotated_image = annotate(image, detections, include_bboxes)
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 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="cpu")
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]:
segmenter_id = segmenter_id if segmenter_id else "martintmv/InsectSAM"
segmentator = AutoModelForMaskGeneration.from_pretrained(
segmenter_id).to("cpu")
processor = AutoProcessor.from_pretrained(segmenter_id)
boxes = get_boxes(detection_results)
inputs = processor(images=image, input_boxes=boxes,
return_tensors="pt").to("cpu")
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]
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]
insect_area = background[y_offset:y_end, x_offset:x_end]
insect_area[mask_crop == 1] = insect[mask_crop == 1]
def create_yellow_background_with_insects(image: np.ndarray) -> np.ndarray:
labels = ["insect"]
original_image, detections = grounded_segmentation(
image, labels, threshold=0.3, polygon_refinement=True)
yellow_background = np.full(
(original_image.shape[0], original_image.shape[1], 3), (0, 255, 255), dtype=np.uint8) # BGR for yellow
for detection in detections:
if detection.mask is not None:
extract_and_paste_insect(
original_image, detection, yellow_background)
# Convert back to RGB to match Gradio's expected input format
yellow_background = cv2.cvtColor(yellow_background, cv2.COLOR_BGR2RGB)
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
},
"mask": run_length_encoding(detection.mask) if detection.mask is not None else None
}
detections_list.append(detection_dict)
return detections_list
def crop_bounding_boxes_with_yellow_background(image: np.ndarray, yellow_background: np.ndarray, detections: List[DetectionResult]) -> List[np.ndarray]:
crops = []
for detection in detections:
xmin, ymin, xmax, ymax = detection.box.xyxy
crop = yellow_background[ymin:ymax, xmin:xmax]
crops.append(crop)
return crops