# Ultralytics YOLO 🚀, AGPL-3.0 license import os from pathlib import Path import cv2 import matplotlib.pyplot as plt import numpy as np import torch from PIL import Image from ultralytics.utils import LOGGER class FastSAMPrompt: def __init__(self, img_path, results, device='cuda') -> None: # self.img_path = img_path self.device = device self.results = results self.img_path = str(img_path) self.ori_img = cv2.imread(self.img_path) # Import and assign clip try: import clip # for linear_assignment except ImportError: from ultralytics.utils.checks import check_requirements check_requirements('git+https://github.com/openai/CLIP.git') # required before installing lap from source import clip self.clip = clip @staticmethod def _segment_image(image, bbox): image_array = np.array(image) segmented_image_array = np.zeros_like(image_array) x1, y1, x2, y2 = bbox segmented_image_array[y1:y2, x1:x2] = image_array[y1:y2, x1:x2] segmented_image = Image.fromarray(segmented_image_array) black_image = Image.new('RGB', image.size, (255, 255, 255)) # transparency_mask = np.zeros_like((), dtype=np.uint8) transparency_mask = np.zeros((image_array.shape[0], image_array.shape[1]), dtype=np.uint8) transparency_mask[y1:y2, x1:x2] = 255 transparency_mask_image = Image.fromarray(transparency_mask, mode='L') black_image.paste(segmented_image, mask=transparency_mask_image) return black_image @staticmethod def _format_results(result, filter=0): annotations = [] n = len(result.masks.data) for i in range(n): mask = result.masks.data[i] == 1.0 if torch.sum(mask) < filter: continue annotation = { 'id': i, 'segmentation': mask.cpu().numpy(), 'bbox': result.boxes.data[i], 'score': result.boxes.conf[i]} annotation['area'] = annotation['segmentation'].sum() annotations.append(annotation) return annotations @staticmethod def filter_masks(annotations): # filter the overlap mask annotations.sort(key=lambda x: x['area'], reverse=True) to_remove = set() for i in range(len(annotations)): a = annotations[i] for j in range(i + 1, len(annotations)): b = annotations[j] if i != j and j not in to_remove and b['area'] < a['area'] and \ (a['segmentation'] & b['segmentation']).sum() / b['segmentation'].sum() > 0.8: to_remove.add(j) return [a for i, a in enumerate(annotations) if i not in to_remove], to_remove @staticmethod def _get_bbox_from_mask(mask): mask = mask.astype(np.uint8) contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) x1, y1, w, h = cv2.boundingRect(contours[0]) x2, y2 = x1 + w, y1 + h if len(contours) > 1: for b in contours: x_t, y_t, w_t, h_t = cv2.boundingRect(b) # 将多个bbox合并成一个 x1 = min(x1, x_t) y1 = min(y1, y_t) x2 = max(x2, x_t + w_t) y2 = max(y2, y_t + h_t) h = y2 - y1 w = x2 - x1 return [x1, y1, x2, y2] def plot(self, annotations, output, bbox=None, points=None, point_label=None, mask_random_color=True, better_quality=True, retina=False, withContours=True): if isinstance(annotations[0], dict): annotations = [annotation['segmentation'] for annotation in annotations] result_name = os.path.basename(self.img_path) image = self.ori_img image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) original_h = image.shape[0] original_w = image.shape[1] # for macOS only # plt.switch_backend('TkAgg') fig = plt.figure(figsize=(original_w / 100, original_h / 100)) # Add subplot with no margin. plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0) plt.margins(0, 0) plt.gca().xaxis.set_major_locator(plt.NullLocator()) plt.gca().yaxis.set_major_locator(plt.NullLocator()) plt.imshow(image) if better_quality: if isinstance(annotations[0], torch.Tensor): annotations = np.array(annotations.cpu()) for i, mask in enumerate(annotations): mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8)) annotations[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8)) if self.device == 'cpu': annotations = np.array(annotations) self.fast_show_mask( annotations, plt.gca(), random_color=mask_random_color, bbox=bbox, points=points, pointlabel=point_label, retinamask=retina, target_height=original_h, target_width=original_w, ) else: if isinstance(annotations[0], np.ndarray): annotations = torch.from_numpy(annotations) self.fast_show_mask_gpu( annotations, plt.gca(), random_color=mask_random_color, bbox=bbox, points=points, pointlabel=point_label, retinamask=retina, target_height=original_h, target_width=original_w, ) if isinstance(annotations, torch.Tensor): annotations = annotations.cpu().numpy() if withContours: contour_all = [] temp = np.zeros((original_h, original_w, 1)) for i, mask in enumerate(annotations): if isinstance(mask, dict): mask = mask['segmentation'] annotation = mask.astype(np.uint8) if not retina: annotation = cv2.resize( annotation, (original_w, original_h), interpolation=cv2.INTER_NEAREST, ) contours, hierarchy = cv2.findContours(annotation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contour_all.extend(iter(contours)) cv2.drawContours(temp, contour_all, -1, (255, 255, 255), 2) color = np.array([0 / 255, 0 / 255, 1.0, 0.8]) contour_mask = temp / 255 * color.reshape(1, 1, -1) plt.imshow(contour_mask) save_path = Path(output) / result_name save_path.parent.mkdir(exist_ok=True, parents=True) plt.axis('off') fig.savefig(save_path) LOGGER.info(f'Saved to {save_path.absolute()}') # CPU post process def fast_show_mask( self, annotation, ax, random_color=False, bbox=None, points=None, pointlabel=None, retinamask=True, target_height=960, target_width=960, ): msak_sum = annotation.shape[0] height = annotation.shape[1] weight = annotation.shape[2] # 将annotation 按照面积 排序 areas = np.sum(annotation, axis=(1, 2)) sorted_indices = np.argsort(areas) annotation = annotation[sorted_indices] index = (annotation != 0).argmax(axis=0) if random_color: color = np.random.random((msak_sum, 1, 1, 3)) else: color = np.ones((msak_sum, 1, 1, 3)) * np.array([30 / 255, 144 / 255, 1.0]) transparency = np.ones((msak_sum, 1, 1, 1)) * 0.6 visual = np.concatenate([color, transparency], axis=-1) mask_image = np.expand_dims(annotation, -1) * visual show = np.zeros((height, weight, 4)) h_indices, w_indices = np.meshgrid(np.arange(height), np.arange(weight), indexing='ij') indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None)) # 使用向量化索引更新show的值 show[h_indices, w_indices, :] = mask_image[indices] if bbox is not None: x1, y1, x2, y2 = bbox ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor='b', linewidth=1)) # draw point if points is not None: plt.scatter( [point[0] for i, point in enumerate(points) if pointlabel[i] == 1], [point[1] for i, point in enumerate(points) if pointlabel[i] == 1], s=20, c='y', ) plt.scatter( [point[0] for i, point in enumerate(points) if pointlabel[i] == 0], [point[1] for i, point in enumerate(points) if pointlabel[i] == 0], s=20, c='m', ) if not retinamask: show = cv2.resize(show, (target_width, target_height), interpolation=cv2.INTER_NEAREST) ax.imshow(show) def fast_show_mask_gpu( self, annotation, ax, random_color=False, bbox=None, points=None, pointlabel=None, retinamask=True, target_height=960, target_width=960, ): msak_sum = annotation.shape[0] height = annotation.shape[1] weight = annotation.shape[2] areas = torch.sum(annotation, dim=(1, 2)) sorted_indices = torch.argsort(areas, descending=False) annotation = annotation[sorted_indices] # 找每个位置第一个非零值下标 index = (annotation != 0).to(torch.long).argmax(dim=0) if random_color: color = torch.rand((msak_sum, 1, 1, 3)).to(annotation.device) else: color = torch.ones((msak_sum, 1, 1, 3)).to(annotation.device) * torch.tensor([30 / 255, 144 / 255, 1.0]).to( annotation.device) transparency = torch.ones((msak_sum, 1, 1, 1)).to(annotation.device) * 0.6 visual = torch.cat([color, transparency], dim=-1) mask_image = torch.unsqueeze(annotation, -1) * visual # 按index取数，index指每个位置选哪个batch的数，把mask_image转成一个batch的形式 show = torch.zeros((height, weight, 4)).to(annotation.device) h_indices, w_indices = torch.meshgrid(torch.arange(height), torch.arange(weight), indexing='ij') indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None)) # 使用向量化索引更新show的值 show[h_indices, w_indices, :] = mask_image[indices] show_cpu = show.cpu().numpy() if bbox is not None: x1, y1, x2, y2 = bbox ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor='b', linewidth=1)) # draw point if points is not None: plt.scatter( [point[0] for i, point in enumerate(points) if pointlabel[i] == 1], [point[1] for i, point in enumerate(points) if pointlabel[i] == 1], s=20, c='y', ) plt.scatter( [point[0] for i, point in enumerate(points) if pointlabel[i] == 0], [point[1] for i, point in enumerate(points) if pointlabel[i] == 0], s=20, c='m', ) if not retinamask: show_cpu = cv2.resize(show_cpu, (target_width, target_height), interpolation=cv2.INTER_NEAREST) ax.imshow(show_cpu) # clip @torch.no_grad() def retrieve(self, model, preprocess, elements, search_text: str, device) -> int: preprocessed_images = [preprocess(image).to(device) for image in elements] tokenized_text = self.clip.tokenize([search_text]).to(device) stacked_images = torch.stack(preprocessed_images) image_features = model.encode_image(stacked_images) text_features = model.encode_text(tokenized_text) image_features /= image_features.norm(dim=-1, keepdim=True) text_features /= text_features.norm(dim=-1, keepdim=True) probs = 100.0 * image_features @ text_features.T return probs[:, 0].softmax(dim=0) def _crop_image(self, format_results): image = Image.fromarray(cv2.cvtColor(self.ori_img, cv2.COLOR_BGR2RGB)) ori_w, ori_h = image.size annotations = format_results mask_h, mask_w = annotations[0]['segmentation'].shape if ori_w != mask_w or ori_h != mask_h: image = image.resize((mask_w, mask_h)) cropped_boxes = [] cropped_images = [] not_crop = [] filter_id = [] # annotations, _ = filter_masks(annotations) # filter_id = list(_) for _, mask in enumerate(annotations): if np.sum(mask['segmentation']) <= 100: filter_id.append(_) continue bbox = self._get_bbox_from_mask(mask['segmentation']) # mask 的 bbox cropped_boxes.append(self._segment_image(image, bbox)) # 保存裁剪的图片 # cropped_boxes.append(segment_image(image,mask["segmentation"])) cropped_images.append(bbox) # 保存裁剪的图片的bbox return cropped_boxes, cropped_images, not_crop, filter_id, annotations def box_prompt(self, bbox): assert (bbox[2] != 0 and bbox[3] != 0) masks = self.results[0].masks.data target_height = self.ori_img.shape[0] target_width = self.ori_img.shape[1] h = masks.shape[1] w = masks.shape[2] if h != target_height or w != target_width: bbox = [ int(bbox[0] * w / target_width), int(bbox[1] * h / target_height), int(bbox[2] * w / target_width), int(bbox[3] * h / target_height), ] bbox[0] = max(round(bbox[0]), 0) bbox[1] = max(round(bbox[1]), 0) bbox[2] = min(round(bbox[2]), w) bbox[3] = min(round(bbox[3]), h) # IoUs = torch.zeros(len(masks), dtype=torch.float32) bbox_area = (bbox[3] - bbox[1]) * (bbox[2] - bbox[0]) masks_area = torch.sum(masks[:, bbox[1]:bbox[3], bbox[0]:bbox[2]], dim=(1, 2)) orig_masks_area = torch.sum(masks, dim=(1, 2)) union = bbox_area + orig_masks_area - masks_area IoUs = masks_area / union max_iou_index = torch.argmax(IoUs) return np.array([masks[max_iou_index].cpu().numpy()]) def point_prompt(self, points, pointlabel): # numpy 处理 masks = self._format_results(self.results[0], 0) target_height = self.ori_img.shape[0] target_width = self.ori_img.shape[1] h = masks[0]['segmentation'].shape[0] w = masks[0]['segmentation'].shape[1] if h != target_height or w != target_width: points = [[int(point[0] * w / target_width), int(point[1] * h / target_height)] for point in points] onemask = np.zeros((h, w)) for i, annotation in enumerate(masks): mask = annotation['segmentation'] if isinstance(annotation, dict) else annotation for i, point in enumerate(points): if mask[point[1], point[0]] == 1 and pointlabel[i] == 1: onemask += mask if mask[point[1], point[0]] == 1 and pointlabel[i] == 0: onemask -= mask onemask = onemask >= 1 return np.array([onemask]) def text_prompt(self, text): format_results = self._format_results(self.results[0], 0) cropped_boxes, cropped_images, not_crop, filter_id, annotations = self._crop_image(format_results) clip_model, preprocess = self.clip.load('ViT-B/32', device=self.device) scores = self.retrieve(clip_model, preprocess, cropped_boxes, text, device=self.device) max_idx = scores.argsort() max_idx = max_idx[-1] max_idx += sum(np.array(filter_id) <= int(max_idx)) return np.array([annotations[max_idx]['segmentation']]) def everything_prompt(self): return self.results[0].masks.data