app.py

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

@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)

@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")
    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]
    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((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 = 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,
            (255, 255, 255),
             2
        )
    return image_with_insects

def plot_detections_plotly(image: np.ndarray, detections: List[DetectionResult]) -> str:
    from plotly import graph_objects as go
    import plotly.express as px
    fig = px.imshow(image)
    class_colors = {i: f'rgb({random.randint(0, 255)}, {random.randint(0, 255)}, {random.randint(0, 255)})' for i in range(len(detections))}
    for idx, detection in enumerate(detections):
        label = detection.label
        box = detection.box
        score = detection.score
        mask = detection.mask
        polygon = mask_to_polygon(mask)
        fig.add_trace(go.Scatter(
            x=[point[0] for point in polygon] + [polygon[0][0]],
            y=[point[1] for point in polygon] + [polygon[0][1]],
            mode='lines',
            line=dict(color=class_colors[idx], width=2),
            fill='toself',
            name=f"{label}: {score:.2f}"
        ))
        xmin, ymin, xmax, ymax = box.xyxy
        fig.add_shape(
            type="rect",
            x0=xmin, y0=ymin, x1=xmax, y1=ymax,
            line=dict(color=class_colors[idx])
        )
        fig.add_annotation(
            x=(xmin + xmax) // 2, y=(ymin + ymax) // 2,
            text=f"{label}: {score:.2f}",
        )
    fig.update_layout(xaxis=dict(visible=False), yaxis=dict(visible=False))
    file_path = "/tmp/plotly_image.html"
    fig.write_html(file_path)
    return file_path

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)
    plotly_image_path = plot_detections_plotly(original_image, detections)
    return annotated_image, yellow_background_with_boxes, plotly_image_path

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