You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
406 lines
16 KiB
406 lines
16 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
|
|
|
import os |
|
|
|
import cv2 |
|
import matplotlib.pyplot as plt |
|
import numpy as np |
|
import torch |
|
from PIL import Image |
|
|
|
|
|
class FastSAMPrompt: |
|
|
|
def __init__(self, img_path, results, device='cuda') -> None: |
|
# self.img_path = img_path |
|
self.device = device |
|
self.results = results |
|
self.img_path = img_path |
|
self.ori_img = cv2.imread(img_path) |
|
|
|
# Import and assign clip |
|
try: |
|
import clip # for linear_assignment |
|
except ImportError: |
|
from ultralytics.yolo.utils.checks import check_requirements |
|
check_requirements('git+https://github.com/openai/CLIP.git') # required before installing lap from source |
|
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) |
|
for i in range(n): |
|
mask = result.masks.data[i] == 1.0 |
|
|
|
if torch.sum(mask) < filter: |
|
continue |
|
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 filter_masks(annotations): # filter the overlap mask |
|
annotations.sort(key=lambda x: x['area'], reverse=True) |
|
to_remove = set() |
|
for i in range(len(annotations)): |
|
a = annotations[i] |
|
for j in range(i + 1, len(annotations)): |
|
b = annotations[j] |
|
if i != j and j not in to_remove and b['area'] < a['area'] and \ |
|
(a['segmentation'] & b['segmentation']).sum() / b['segmentation'].sum() > 0.8: |
|
to_remove.add(j) |
|
|
|
return [a for i, a in enumerate(annotations) if i not in to_remove], to_remove |
|
|
|
@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) |
|
# 将多个bbox合并成一个 |
|
x1 = min(x1, x_t) |
|
y1 = min(y1, y_t) |
|
x2 = max(x2, x_t + w_t) |
|
y2 = max(y2, y_t + h_t) |
|
h = y2 - y1 |
|
w = x2 - x1 |
|
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, |
|
withContours=True): |
|
if isinstance(annotations[0], dict): |
|
annotations = [annotation['segmentation'] for annotation in annotations] |
|
result_name = os.path.basename(self.img_path) |
|
image = self.ori_img |
|
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
|
original_h = image.shape[0] |
|
original_w = image.shape[1] |
|
# 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 better_quality: |
|
if isinstance(annotations[0], torch.Tensor): |
|
annotations = np.array(annotations.cpu()) |
|
for i, mask in enumerate(annotations): |
|
mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8)) |
|
annotations[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8)) |
|
if self.device == 'cpu': |
|
annotations = np.array(annotations) |
|
self.fast_show_mask( |
|
annotations, |
|
plt.gca(), |
|
random_color=mask_random_color, |
|
bbox=bbox, |
|
points=points, |
|
pointlabel=point_label, |
|
retinamask=retina, |
|
target_height=original_h, |
|
target_width=original_w, |
|
) |
|
else: |
|
if isinstance(annotations[0], np.ndarray): |
|
annotations = torch.from_numpy(annotations) |
|
self.fast_show_mask_gpu( |
|
annotations, |
|
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 isinstance(annotations, torch.Tensor): |
|
annotations = annotations.cpu().numpy() |
|
if withContours: |
|
contour_all = [] |
|
temp = np.zeros((original_h, original_w, 1)) |
|
for i, mask in enumerate(annotations): |
|
if type(mask) == dict: |
|
mask = mask['segmentation'] |
|
annotation = mask.astype(np.uint8) |
|
if not retina: |
|
annotation = cv2.resize( |
|
annotation, |
|
(original_w, original_h), |
|
interpolation=cv2.INTER_NEAREST, |
|
) |
|
contours, hierarchy = cv2.findContours(annotation, 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) |
|
|
|
save_path = output |
|
if not os.path.exists(save_path): |
|
os.makedirs(save_path) |
|
plt.axis('off') |
|
fig = plt.gcf() |
|
plt.draw() |
|
|
|
try: |
|
buf = fig.canvas.tostring_rgb() |
|
except AttributeError: |
|
fig.canvas.draw() |
|
buf = fig.canvas.tostring_rgb() |
|
cols, rows = fig.canvas.get_width_height() |
|
img_array = np.frombuffer(buf, dtype=np.uint8).reshape(rows, cols, 3) |
|
cv2.imwrite(os.path.join(save_path, result_name), cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)) |
|
|
|
# CPU post process |
|
def fast_show_mask( |
|
self, |
|
annotation, |
|
ax, |
|
random_color=False, |
|
bbox=None, |
|
points=None, |
|
pointlabel=None, |
|
retinamask=True, |
|
target_height=960, |
|
target_width=960, |
|
): |
|
msak_sum = annotation.shape[0] |
|
height = annotation.shape[1] |
|
weight = annotation.shape[2] |
|
# 将annotation 按照面积 排序 |
|
areas = np.sum(annotation, axis=(1, 2)) |
|
sorted_indices = np.argsort(areas) |
|
annotation = annotation[sorted_indices] |
|
|
|
index = (annotation != 0).argmax(axis=0) |
|
if random_color: |
|
color = np.random.random((msak_sum, 1, 1, 3)) |
|
else: |
|
color = np.ones((msak_sum, 1, 1, 3)) * np.array([30 / 255, 144 / 255, 1.0]) |
|
transparency = np.ones((msak_sum, 1, 1, 1)) * 0.6 |
|
visual = np.concatenate([color, transparency], axis=-1) |
|
mask_image = np.expand_dims(annotation, -1) * visual |
|
|
|
show = np.zeros((height, weight, 4)) |
|
h_indices, w_indices = np.meshgrid(np.arange(height), np.arange(weight), indexing='ij') |
|
indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None)) |
|
# 使用向量化索引更新show的值 |
|
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) |
|
|
|
def fast_show_mask_gpu( |
|
self, |
|
annotation, |
|
ax, |
|
random_color=False, |
|
bbox=None, |
|
points=None, |
|
pointlabel=None, |
|
retinamask=True, |
|
target_height=960, |
|
target_width=960, |
|
): |
|
msak_sum = annotation.shape[0] |
|
height = annotation.shape[1] |
|
weight = annotation.shape[2] |
|
areas = torch.sum(annotation, dim=(1, 2)) |
|
sorted_indices = torch.argsort(areas, descending=False) |
|
annotation = annotation[sorted_indices] |
|
# 找每个位置第一个非零值下标 |
|
index = (annotation != 0).to(torch.long).argmax(dim=0) |
|
if random_color: |
|
color = torch.rand((msak_sum, 1, 1, 3)).to(annotation.device) |
|
else: |
|
color = torch.ones((msak_sum, 1, 1, 3)).to(annotation.device) * torch.tensor([30 / 255, 144 / 255, 1.0]).to( |
|
annotation.device) |
|
transparency = torch.ones((msak_sum, 1, 1, 1)).to(annotation.device) * 0.6 |
|
visual = torch.cat([color, transparency], dim=-1) |
|
mask_image = torch.unsqueeze(annotation, -1) * visual |
|
# 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 |
|
show = torch.zeros((height, weight, 4)).to(annotation.device) |
|
h_indices, w_indices = torch.meshgrid(torch.arange(height), torch.arange(weight), indexing='ij') |
|
indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None)) |
|
# 使用向量化索引更新show的值 |
|
show[h_indices, w_indices, :] = mask_image[indices] |
|
show_cpu = show.cpu().numpy() |
|
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_cpu = cv2.resize(show_cpu, (target_width, target_height), interpolation=cv2.INTER_NEAREST) |
|
ax.imshow(show_cpu) |
|
|
|
# clip |
|
@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): |
|
|
|
image = Image.fromarray(cv2.cvtColor(self.ori_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 = [] |
|
# annotations, _ = filter_masks(annotations) |
|
# filter_id = list(_) |
|
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_boxes.append(segment_image(image,mask["segmentation"])) |
|
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): |
|
|
|
assert (bbox[2] != 0 and bbox[3] != 0) |
|
masks = self.results[0].masks.data |
|
target_height = self.ori_img.shape[0] |
|
target_width = self.ori_img.shape[1] |
|
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) |
|
|
|
return np.array([masks[max_iou_index].cpu().numpy()]) |
|
|
|
def point_prompt(self, points, pointlabel): # numpy 处理 |
|
|
|
masks = self._format_results(self.results[0], 0) |
|
target_height = self.ori_img.shape[0] |
|
target_width = self.ori_img.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 i, annotation in enumerate(masks): |
|
mask = annotation['segmentation'] if type(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
|
|
|