import cv2 import os import numpy as np from segment_anything import sam_model_registry, SamPredictor input_dir = 'scripts/input/images' output_dir = 'scripts/output/mask' crop_mode = True print('最好是每加一个点就按w键predict一次') os.makedirs(output_dir, exist_ok=True) image_files = [f for f in os.listdir(input_dir) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.JPG', '.JPEG', '.PNG', '.tiff'))] sam = sam_model_registry["vit_b"](checkpoint=r"D:\Program Files\Pycharm items\segment-anything-model\weights\vit_b.pth") _ = sam.to(device="cuda") predictor = SamPredictor(sam) WINDOW_WIDTH = 1280 WINDOW_HEIGHT = 720 cv2.namedWindow("image", cv2.WINDOW_NORMAL) cv2.resizeWindow("image", WINDOW_WIDTH, WINDOW_HEIGHT) cv2.moveWindow("image", (1920 - WINDOW_WIDTH) // 2, (1080 - WINDOW_HEIGHT) // 2) def mouse_click(event, x, y, flags, param): global input_point, input_label, input_stop if not input_stop: if event == cv2.EVENT_LBUTTONDOWN: input_point.append([x, y]) input_label.append(1) elif event == cv2.EVENT_RBUTTONDOWN: input_point.append([x, y]) input_label.append(0) else: if event == cv2.EVENT_LBUTTONDOWN or event == cv2.EVENT_RBUTTONDOWN: print('此时不能添加点,按w退出mask选择模式') def apply_mask(image, mask, alpha_channel=True): if alpha_channel: alpha = np.zeros_like(image[..., 0]) alpha[mask == 1] = 255 image = cv2.merge((image[..., 0], image[..., 1], image[..., 2], alpha)) else: image = np.where(mask[..., None] == 1, image, 0) return image def apply_color_mask(image, mask, color, color_dark=0.5): for c in range(3): image[:, :, c] = np.where(mask == 1, image[:, :, c] * (1 - color_dark) + color_dark * color[c], image[:, :, c]) return image def get_next_filename(base_path, filename): name, ext = os.path.splitext(filename) for i in range(1, 101): new_name = f"{name}_{i}{ext}" if not os.path.exists(os.path.join(base_path, new_name)): return new_name return None def save_masked_image(image, mask, output_dir, filename, crop_mode_): if crop_mode_: y, x = np.where(mask) y_min, y_max, x_min, x_max = y.min(), y.max(), x.min(), x.max() cropped_mask = mask[y_min:y_max + 1, x_min:x_max + 1] cropped_image = image[y_min:y_max + 1, x_min:x_max + 1] masked_image = apply_mask(cropped_image, cropped_mask) else: masked_image = apply_mask(image, mask) filename = filename[:filename.rfind('.')] + '.png' new_filename = get_next_filename(output_dir, filename) if new_filename: if masked_image.shape[-1] == 4: cv2.imwrite(os.path.join(output_dir, new_filename), masked_image, [cv2.IMWRITE_PNG_COMPRESSION, 9]) else: cv2.imwrite(os.path.join(output_dir, new_filename), masked_image) print(f"Saved as {new_filename}") else: print("Could not save the image. Too many variations exist.") current_index = 0 cv2.namedWindow("image") cv2.setMouseCallback("image", mouse_click) input_point = [] input_label = [] input_stop = False while True: filename = image_files[current_index] image_orign = cv2.imread(os.path.join(input_dir, filename)) image_crop = image_orign.copy() image = cv2.cvtColor(image_orign.copy(), cv2.COLOR_BGR2RGB) selected_mask = None logit_input = None while True: input_stop = False image_display = image_orign.copy() display_info = f'{filename} ' cv2.putText(image_display, display_info, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2, cv2.LINE_AA) for point, label in zip(input_point, input_label): color = (0, 255, 0) if label == 1 else (0, 0, 255) cv2.circle(image_display, tuple(point), 5, color, -1) if selected_mask is not None: color = tuple(np.random.randint(0, 256, 3).tolist()) selected_image = apply_color_mask(image_display, selected_mask, color) cv2.imshow("image", image_display) key = cv2.waitKey(1) if key == ord(" "): input_point = [] input_label = [] selected_mask = None logit_input = None elif key == ord("w"): input_stop = True if len(input_point) > 0 and len(input_label) > 0: predictor.set_image(image) input_point_np = np.array(input_point) input_label_np = np.array(input_label) masks, scores, logits = predictor.predict( point_coords=input_point_np, point_labels=input_label_np, mask_input=logit_input[None, :, :] if logit_input is not None else None, multimask_output=True, ) mask_idx = 0 num_masks = len(masks) while (1): color = tuple(np.random.randint(0, 256, 3).tolist()) image_select = image_orign.copy() selected_mask = masks[mask_idx] selected_image = apply_color_mask(image_select, selected_mask, color) mask_info = f'Total: {num_masks} | Current: {mask_idx} | Score: {scores[mask_idx]:.2f} | w 预测 | d 切换下一个 | a 切换上一个 | q 移除最后一个 | s 保存' cv2.putText(selected_image, mask_info, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2, cv2.LINE_AA) cv2.imshow("image", selected_image) key = cv2.waitKey(10) if key == ord('q') and len(input_point) > 0: input_point.pop(-1) input_label.pop(-1) elif key == ord('s'): save_masked_image(image_crop, selected_mask, output_dir, filename, crop_mode_=crop_mode) elif key == ord('a'): if mask_idx > 0: mask_idx -= 1 else: mask_idx = num_masks - 1 elif key == ord('d'): if mask_idx < num_masks - 1: mask_idx += 1 else: mask_idx = 0 elif key == ord('w'): break elif key == ord(" "): input_point = [] input_label = [] selected_mask = None logit_input = None break logit_input = logits[mask_idx, :, :] print('max score:', np.argmax(scores), ' select:', mask_idx) elif key == ord('a'): current_index = max(0, current_index - 1) input_point = [] input_label = [] break elif key == ord('d'): current_index = min(len(image_files) - 1, current_index + 1) input_point = [] input_label = [] break elif key == 27: break elif key == ord('q') and len(input_point) > 0: input_point.pop(-1) input_label.pop(-1) elif key == ord('s') and selected_mask is not None: save_masked_image(image_crop, selected_mask, output_dir, filename, crop_mode_=crop_mode) if key == 27: break