ultralytics 8.0.158 add benchmarks to coverage (#4432)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
This commit is contained in:
Glenn Jocher
GitHub
@ -26,7 +26,7 @@ class FastSAMPrompt:
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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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check_requirements('git+https://github.com/openai/CLIP.git')
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import clip
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self.clip = clip
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@ -91,8 +91,6 @@ class FastSAMPrompt:
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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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@ -104,9 +102,11 @@ class FastSAMPrompt:
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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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with_countouers=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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if isinstance(annotations, torch.Tensor):
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annotations = annotations.cpu().numpy()
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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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@ -123,41 +123,22 @@ class FastSAMPrompt:
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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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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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if with_countouers:
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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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@ -184,8 +165,8 @@ class FastSAMPrompt:
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LOGGER.info(f'Saved to {save_path.absolute()}')
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# CPU post process
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@staticmethod
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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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@ -196,32 +177,29 @@ class FastSAMPrompt:
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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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n, h, w = annotation.shape # batch, height, width
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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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annotation = annotation[np.argsort(areas)]
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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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color = np.random.random((n, 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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color = np.ones((n, 1, 1, 3)) * np.array([30 / 255, 144 / 255, 1.0])
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transparency = np.ones((n, 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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show = np.zeros((h, w, 4))
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h_indices, w_indices = np.meshgrid(np.arange(h), np.arange(w), 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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# 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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@ -240,63 +218,6 @@ class FastSAMPrompt:
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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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