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王庆刚
2024-05-20 20:01:06 +08:00
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ultralytics/models/fastsam/__init__.py Normal file
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# Ultralytics YOLO 🚀, AGPL-3.0 license
from .model import FastSAM
from .predict import FastSAMPredictor
from .prompt import FastSAMPrompt
from .val import FastSAMValidator
__all__ = 'FastSAMPredictor', 'FastSAM', 'FastSAMPrompt', 'FastSAMValidator'

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ultralytics/models/fastsam/model.py Normal file
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# Ultralytics YOLO 🚀, AGPL-3.0 license
from pathlib import Path
from ultralytics.engine.model import Model
from .predict import FastSAMPredictor
from .val import FastSAMValidator
class FastSAM(Model):
"""
FastSAM model interface.
Example:
```python
from ultralytics import FastSAM
model = FastSAM('last.pt')
results = model.predict('ultralytics/assets/bus.jpg')
```
"""
def __init__(self, model='FastSAM-x.pt'):
"""Call the __init__ method of the parent class (YOLO) with the updated default model"""
if str(model) == 'FastSAM.pt':
model = 'FastSAM-x.pt'
assert Path(model).suffix not in ('.yaml', '.yml'), 'FastSAM models only support pre-trained models.'
super().__init__(model=model, task='segment')
@property
def task_map(self):
return {'segment': {'predictor': FastSAMPredictor, 'validator': FastSAMValidator}}

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ultralytics/models/fastsam/predict.py Normal file
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# Ultralytics YOLO 🚀, AGPL-3.0 license
import torch
from ultralytics.engine.results import Results
from ultralytics.models.fastsam.utils import bbox_iou
from ultralytics.models.yolo.detect.predict import DetectionPredictor
from ultralytics.utils import DEFAULT_CFG, ops
class FastSAMPredictor(DetectionPredictor):
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
super().__init__(cfg, overrides, _callbacks)
self.args.task = 'segment'
def postprocess(self, preds, img, orig_imgs):
p = ops.non_max_suppression(preds[0],
self.args.conf,
self.args.iou,
agnostic=self.args.agnostic_nms,
max_det=self.args.max_det,
nc=len(self.model.names),
classes=self.args.classes)
full_box = torch.zeros(p[0].shape[1], device=p[0].device)
full_box[2], full_box[3], full_box[4], full_box[6:] = img.shape[3], img.shape[2], 1.0, 1.0
full_box = full_box.view(1, -1)
critical_iou_index = bbox_iou(full_box[0][:4], p[0][:, :4], iou_thres=0.9, image_shape=img.shape[2:])
if critical_iou_index.numel() != 0:
full_box[0][4] = p[0][critical_iou_index][:, 4]
full_box[0][6:] = p[0][critical_iou_index][:, 6:]
p[0][critical_iou_index] = full_box
if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list
orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
results = []
proto = preds[1][-1] if len(preds[1]) == 3 else preds[1] # second output is len 3 if pt, but only 1 if exported
for i, pred in enumerate(p):
orig_img = orig_imgs[i]
img_path = self.batch[0][i]
if not len(pred): # save empty boxes
masks = None
elif self.args.retina_masks:
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], orig_img.shape[:2]) # HWC
else:
masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True) # HWC
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
return results

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ultralytics/models/fastsam/prompt.py Normal file
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# 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 TQDM
class FastSAMPrompt:
def __init__(self, source, results, device='cuda') -> None:
self.device = device
self.results = results
self.source = source
# 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')
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) 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()
annotations.append(annotation)
return annotations
@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)
x1 = min(x1, x_t)
y1 = min(y1, y_t)
x2 = max(x2, x_t + w_t)
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):
pbar = TQDM(annotations, total=len(annotations))
for ann in pbar:
result_name = os.path.basename(ann.path)
image = ann.orig_img
original_h, original_w = ann.orig_shape
# for macOS only
# plt.switch_backend('TkAgg')
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 ann.masks is not None:
masks = ann.masks.data
if better_quality:
if isinstance(masks[0], torch.Tensor):
masks = np.array(masks.cpu())
for i, mask in enumerate(masks):
mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))
masks[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8))
self.fast_show_mask(
masks,
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 with_contours:
contour_all = []
temp = np.zeros((original_h, original_w, 1))
for i, mask in enumerate(masks):
mask = mask.astype(np.uint8)
if not retina:
mask = cv2.resize(mask, (original_w, original_h), interpolation=cv2.INTER_NEAREST)
contours, _ = cv2.findContours(mask, 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)
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_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.close()
pbar.set_description(f'Saving {result_name} to {save_path}')
@staticmethod
def fast_show_mask(
annotation,
ax,
random_color=False,
bbox=None,
points=None,
pointlabel=None,
retinamask=True,
target_height=960,
target_width=960,
):
n, h, w = annotation.shape # batch, height, width
areas = np.sum(annotation, axis=(1, 2))
annotation = annotation[np.argsort(areas)]
index = (annotation != 0).argmax(axis=0)
if random_color:
color = np.random.random((n, 1, 1, 3))
else:
color = np.ones((n, 1, 1, 3)) * np.array([30 / 255, 144 / 255, 1.0])
transparency = np.ones((n, 1, 1, 1)) * 0.6
visual = np.concatenate([color, transparency], axis=-1)
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')
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))
# 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)
@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):
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
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 = []
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_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):
if self.results[0].masks is not None:
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
target_height, target_width = self.results[0].orig_shape
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)
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
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]
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
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
self.results[0].masks.data = torch.tensor(np.array([onemask]))
return self.results
def text_prompt(self, text):
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)
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]))
return self.results
def everything_prompt(self):
return self.results

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ultralytics/models/fastsam/utils.py Normal file
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# Ultralytics YOLO 🚀, AGPL-3.0 license
import torch
def adjust_bboxes_to_image_border(boxes, image_shape, threshold=20):
"""
Adjust bounding boxes to stick to image border if they are within a certain threshold.
Args:
boxes (torch.Tensor): (n, 4)
image_shape (tuple): (height, width)
threshold (int): pixel threshold
Returns:
adjusted_boxes (torch.Tensor): adjusted bounding boxes
"""
# Image dimensions
h, w = image_shape
# Adjust boxes
boxes[boxes[:, 0] < threshold, 0] = 0 # x1
boxes[boxes[:, 1] < threshold, 1] = 0 # y1
boxes[boxes[:, 2] > w - threshold, 2] = w # x2
boxes[boxes[:, 3] > h - threshold, 3] = h # y2
return boxes
def bbox_iou(box1, boxes, iou_thres=0.9, image_shape=(640, 640), raw_output=False):
"""
Compute the Intersection-Over-Union of a bounding box with respect to an array of other bounding boxes.
Args:
box1 (torch.Tensor): (4, )
boxes (torch.Tensor): (n, 4)
iou_thres (float): IoU threshold
image_shape (tuple): (height, width)
raw_output (bool): If True, return the raw IoU values instead of the indices
Returns:
high_iou_indices (torch.Tensor): Indices of boxes with IoU > thres
"""
boxes = adjust_bboxes_to_image_border(boxes, image_shape)
# obtain coordinates for intersections
x1 = torch.max(box1[0], boxes[:, 0])
y1 = torch.max(box1[1], boxes[:, 1])
x2 = torch.min(box1[2], boxes[:, 2])
y2 = torch.min(box1[3], boxes[:, 3])
# compute the area of intersection
intersection = (x2 - x1).clamp(0) * (y2 - y1).clamp(0)
# compute the area of both individual boxes
box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])
box2_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
# compute the area of union
union = box1_area + box2_area - intersection
# compute the IoU
iou = intersection / union # Should be shape (n, )
if raw_output:
return 0 if iou.numel() == 0 else iou
# return indices of boxes with IoU > thres
return torch.nonzero(iou > iou_thres).flatten()

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ultralytics/models/fastsam/val.py Normal file
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# Ultralytics YOLO 🚀, AGPL-3.0 license
from ultralytics.models.yolo.segment import SegmentationValidator
from ultralytics.utils.metrics import SegmentMetrics
class FastSAMValidator(SegmentationValidator):
def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
"""Initialize SegmentationValidator and set task to 'segment', metrics to SegmentMetrics."""
super().__init__(dataloader, save_dir, pbar, args, _callbacks)
self.args.task = 'segment'
self.args.plots = False # disable ConfusionMatrix and other plots to avoid errors
self.metrics = SegmentMetrics(save_dir=self.save_dir, on_plot=self.on_plot)