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