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add yolo v10 and modify pipeline

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王庆刚
2025-03-28 13:19:54 +08:00
parent 183299c06b
commit 798c596acc
471 changed files with 19109 additions and 7342 deletions
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154
ultralytics/models/fastsam/prompt.py
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@ -13,54 +13,73 @@ from ultralytics.utils import TQDM
class FastSAMPrompt:
"""
Fast Segment Anything Model class for image annotation and visualization.
def __init__(self, source, results, device='cuda') -> None:
Attributes:
device (str): Computing device ('cuda' or 'cpu').
results: Object detection or segmentation results.
source: Source image or image path.
clip: CLIP model for linear assignment.
"""
def __init__(self, source, results, device="cuda") -> None:
"""Initializes FastSAMPrompt with given source, results and device, and assigns clip for linear assignment."""
self.device = device
self.results = results
self.source = source
# Import and assign clip
try:
import clip # for linear_assignment
import clip
except ImportError:
from ultralytics.utils.checks import check_requirements
check_requirements('git+https://github.com/openai/CLIP.git')
check_requirements("git+https://github.com/openai/CLIP.git")
import clip
self.clip = clip
@staticmethod
def _segment_image(image, bbox):
"""Segments the given image according to the provided bounding box coordinates."""
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))
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')
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):
"""Formats detection results into list of annotations each containing ID, segmentation, bounding box, score and
area.
"""
annotations = []
n = len(result.masks.data) if result.masks is not None else 0
for i in range(n):
mask = result.masks.data[i] == 1.0
if torch.sum(mask) >= filter:
annotation = {
'id': i,
'segmentation': mask.cpu().numpy(),
'bbox': result.boxes.data[i],
'score': result.boxes.conf[i]}
annotation['area'] = annotation['segmentation'].sum()
"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 _get_bbox_from_mask(mask):
"""Applies morphological transformations to the mask, displays it, and if with_contours is True, draws
contours.
"""
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])
@ -74,22 +93,38 @@ class FastSAMPrompt:
y2 = max(y2, y_t + h_t)
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,
with_contours=True):
def plot(
self,
annotations,
output,
bbox=None,
points=None,
point_label=None,
mask_random_color=True,
better_quality=True,
retina=False,
with_contours=True,
):
"""
Plots annotations, bounding boxes, and points on images and saves the output.
Args:
annotations (list): Annotations to be plotted.
output (str or Path): Output directory for saving the plots.
bbox (list, optional): Bounding box coordinates [x1, y1, x2, y2]. Defaults to None.
points (list, optional): Points to be plotted. Defaults to None.
point_label (list, optional): Labels for the points. Defaults to None.
mask_random_color (bool, optional): Whether to use random color for masks. Defaults to True.
better_quality (bool, optional): Whether to apply morphological transformations for better mask quality. Defaults to True.
retina (bool, optional): Whether to use retina mask. Defaults to False.
with_contours (bool, optional): Whether to plot contours. Defaults to True.
"""
pbar = TQDM(annotations, total=len(annotations))
for ann in pbar:
result_name = os.path.basename(ann.path)
image = ann.orig_img
image = ann.orig_img[..., ::-1] # BGR to RGB
original_h, original_w = ann.orig_shape
# for macOS only
# For macOS only
# plt.switch_backend('TkAgg')
plt.figure(figsize=(original_w / 100, original_h / 100))
# Add subplot with no margin.
@ -134,19 +169,13 @@ class FastSAMPrompt:
contour_mask = temp / 255 * color.reshape(1, 1, -1)
plt.imshow(contour_mask)
plt.axis('off')
fig = plt.gcf()
# Check if the canvas has been drawn
if fig.canvas.get_renderer() is None: # macOS requires this or tests fail
fig.canvas.draw()
# Save the figure
save_path = Path(output) / result_name
save_path.parent.mkdir(exist_ok=True, parents=True)
image = Image.frombytes('RGB', fig.canvas.get_width_height(), fig.canvas.tostring_rgb())
image.save(save_path)
plt.axis("off")
plt.savefig(save_path, bbox_inches="tight", pad_inches=0, transparent=True)
plt.close()
pbar.set_description(f'Saving {result_name} to {save_path}')
pbar.set_description(f"Saving {result_name} to {save_path}")
@staticmethod
def fast_show_mask(
@ -160,6 +189,20 @@ class FastSAMPrompt:
target_height=960,
target_width=960,
):
"""
Quickly shows the mask annotations on the given matplotlib axis.
Args:
annotation (array-like): Mask annotation.
ax (matplotlib.axes.Axes): Matplotlib axis.
random_color (bool, optional): Whether to use random color for masks. Defaults to False.
bbox (list, optional): Bounding box coordinates [x1, y1, x2, y2]. Defaults to None.
points (list, optional): Points to be plotted. Defaults to None.
pointlabel (list, optional): Labels for the points. Defaults to None.
retinamask (bool, optional): Whether to use retina mask. Defaults to True.
target_height (int, optional): Target height for resizing. Defaults to 960.
target_width (int, optional): Target width for resizing. Defaults to 960.
"""
n, h, w = annotation.shape # batch, height, width
areas = np.sum(annotation, axis=(1, 2))
@ -175,26 +218,26 @@ class FastSAMPrompt:
mask_image = np.expand_dims(annotation, -1) * visual
show = np.zeros((h, w, 4))
h_indices, w_indices = np.meshgrid(np.arange(h), np.arange(w), indexing='ij')
h_indices, w_indices = np.meshgrid(np.arange(h), np.arange(w), indexing="ij")
indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
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))
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',
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',
c="m",
)
if not retinamask:
@ -203,6 +246,7 @@ class FastSAMPrompt:
@torch.no_grad()
def retrieve(self, model, preprocess, elements, search_text: str, device) -> int:
"""Processes images and text with a model, calculates similarity, and returns softmax score."""
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)
@ -214,12 +258,13 @@ class FastSAMPrompt:
return probs[:, 0].softmax(dim=0)
def _crop_image(self, format_results):
"""Crops an image based on provided annotation format and returns cropped images and related data."""
if os.path.isdir(self.source):
raise ValueError(f"'{self.source}' is a directory, not a valid source for this function.")
image = Image.fromarray(cv2.cvtColor(self.results[0].orig_img, cv2.COLOR_BGR2RGB))
ori_w, ori_h = image.size
annotations = format_results
mask_h, mask_w = annotations[0]['segmentation'].shape
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 = []
@ -227,18 +272,19 @@ class FastSAMPrompt:
not_crop = []
filter_id = []
for _, mask in enumerate(annotations):
if np.sum(mask['segmentation']) <= 100:
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_images.append(bbox) # 保存裁剪的图片的bbox
bbox = self._get_bbox_from_mask(mask["segmentation"]) # bbox from mask
cropped_boxes.append(self._segment_image(image, bbox)) # save cropped image
cropped_images.append(bbox) # save cropped image bbox
return cropped_boxes, cropped_images, not_crop, filter_id, annotations
def box_prompt(self, bbox):
"""Modifies the bounding box properties and calculates IoU between masks and bounding box."""
if self.results[0].masks is not None:
assert (bbox[2] != 0 and bbox[3] != 0)
assert bbox[2] != 0 and bbox[3] != 0
if os.path.isdir(self.source):
raise ValueError(f"'{self.source}' is a directory, not a valid source for this function.")
masks = self.results[0].masks.data
@ -250,7 +296,8 @@ class FastSAMPrompt:
int(bbox[0] * w / target_width),
int(bbox[1] * h / target_height),
int(bbox[2] * w / target_width),
int(bbox[3] * h / target_height), ]
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)
@ -259,29 +306,30 @@ class FastSAMPrompt:
# 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))
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)
iou = masks_area / union
max_iou_index = torch.argmax(iou)
self.results[0].masks.data = torch.tensor(np.array([masks[max_iou_index].cpu().numpy()]))
return self.results
def point_prompt(self, points, pointlabel): # numpy 处理
def point_prompt(self, points, pointlabel): # numpy
"""Adjusts points on detected masks based on user input and returns the modified results."""
if self.results[0].masks is not None:
if os.path.isdir(self.source):
raise ValueError(f"'{self.source}' is a directory, not a valid source for this function.")
masks = self._format_results(self.results[0], 0)
target_height, target_width = self.results[0].orig_shape
h = masks[0]['segmentation'].shape[0]
w = masks[0]['segmentation'].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 annotation in masks:
mask = annotation['segmentation'] if isinstance(annotation, dict) else annotation
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
@ -292,16 +340,18 @@ class FastSAMPrompt:
return self.results
def text_prompt(self, text):
"""Processes a text prompt, applies it to existing results and returns the updated results."""
if self.results[0].masks is not None:
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)
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))
self.results[0].masks.data = torch.tensor(np.array([ann['segmentation'] for ann in annotations]))
self.results[0].masks.data = torch.tensor(np.array([annotations[max_idx]["segmentation"]]))
return self.results
def everything_prompt(self):
"""Returns the processed results from the previous methods in the class."""
return self.results