Delete app.py

app.py

@@ -1,200 +0,0 @@
-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 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()