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https://github.com/modelscope/modelscope.git
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Merge branch 'master' into release/1.0
This commit is contained in:
wenmeng.zwm
@@ -12,8 +12,11 @@ from modelscope.pipelines import pipeline
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from modelscope.pipelines.builder import PIPELINES
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from modelscope.preprocessors import LoadImage
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from modelscope.utils.constant import ModelFile, Tasks
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from modelscope.utils.logger import get_logger
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from .base import EasyCVPipeline
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logger = get_logger()
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@PIPELINES.register_module(
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Tasks.face_2d_keypoints, module_name=Pipelines.face_2d_keypoints)
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@@ -110,67 +113,29 @@ class Face2DKeypointsPipeline(EasyCVPipeline):
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for (x, y) in landmark])
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return M, landmark_
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def random_normal(self):
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"""
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3-sigma rule
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return: (-1, +1)
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"""
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mu, sigma = 0, 1
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while True:
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s = np.random.normal(mu, sigma)
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if s < mu - 3 * sigma or s > mu + 3 * sigma:
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continue
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return s / 3 * sigma
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def rotate_crop_img(self, img, pts, M):
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image_size = 256
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enlarge_ratio = 1.1
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imgT = cv2.warpAffine(img, M, (int(img.shape[1]), int(img.shape[0])))
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x1 = pts[5][0]
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x2 = pts[5][0]
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y1 = pts[5][1]
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x2 = pts[6][0]
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y2 = pts[6][1]
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w = x2 - x1 + 1
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h = y2 - y1 + 1
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x1 = int(x1 - (enlarge_ratio - 1.0) / 2.0 * w)
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y1 = int(y1 - (enlarge_ratio - 1.0) / 2.0 * h)
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new_w = int(enlarge_ratio * (1 + self.random_normal() * 0.1) * w)
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new_h = int(enlarge_ratio * (1 + self.random_normal() * 0.1) * h)
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new_x1 = x1 + int(self.random_normal() * image_size * 0.05)
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new_y1 = y1 + int(self.random_normal() * image_size * 0.05)
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new_x2 = new_x1 + new_w
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new_y2 = new_y1 + new_h
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y2 = pts[5][1]
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for i in range(0, 9):
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x1 = min(x1, pts[i][0])
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x2 = max(x2, pts[i][0])
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y1 = min(y1, pts[i][1])
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y2 = max(y2, pts[i][1])
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height, width, _ = imgT.shape
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dx = max(0, -new_x1)
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dy = max(0, -new_y1)
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new_x1 = max(0, new_x1)
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new_y1 = max(0, new_y1)
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x1 = min(max(0, int(x1)), width)
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y1 = min(max(0, int(y1)), height)
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x2 = min(max(0, int(x2)), width)
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y2 = min(max(0, int(y2)), height)
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sub_imgT = imgT[y1:y2, x1:x2]
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edx = max(0, new_x2 - width)
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edy = max(0, new_y2 - height)
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new_x2 = min(width, new_x2)
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new_y2 = min(height, new_y2)
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return sub_imgT, imgT, [x1, y1, x2, y2]
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sub_imgT = imgT[new_y1:new_y2, new_x1:new_x2]
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if dx > 0 or dy > 0 or edx > 0 or edy > 0:
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sub_imgT = cv2.copyMakeBorder(
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sub_imgT,
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dy,
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edy,
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dx,
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edx,
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cv2.BORDER_CONSTANT,
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value=(103.94, 116.78, 123.68))
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return sub_imgT, imgT, [new_x1, new_y1, new_x2,
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new_y2], [dx, dy, edx, edy]
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def crop_img(self, imgT, pts, angle):
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image_size = 256
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def crop_img(self, imgT, pts):
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enlarge_ratio = 1.1
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x1 = np.min(pts[:, 0])
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@@ -181,94 +146,87 @@ class Face2DKeypointsPipeline(EasyCVPipeline):
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h = y2 - y1 + 1
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x1 = int(x1 - (enlarge_ratio - 1.0) / 2.0 * w)
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y1 = int(y1 - (enlarge_ratio - 1.0) / 2.0 * h)
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x1 = max(0, x1)
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y1 = max(0, y1)
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new_w = int(enlarge_ratio * (1 + self.random_normal() * 0.1) * w)
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new_h = int(enlarge_ratio * (1 + self.random_normal() * 0.1) * h)
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new_x1 = x1 + int(self.random_normal() * image_size * 0.05)
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new_y1 = y1 + int(self.random_normal() * image_size * 0.05)
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new_w = int(enlarge_ratio * w)
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new_h = int(enlarge_ratio * h)
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new_x1 = x1
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new_y1 = y1
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new_x2 = new_x1 + new_w
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new_y2 = new_y1 + new_h
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new_xy = new_x1, new_y1
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pts = pts - new_xy
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height, width, _ = imgT.shape
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dx = max(0, -new_x1)
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dy = max(0, -new_y1)
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new_x1 = max(0, new_x1)
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new_y1 = max(0, new_y1)
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edx = max(0, new_x2 - width)
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edy = max(0, new_y2 - height)
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new_x2 = min(width, new_x2)
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new_y2 = min(height, new_y2)
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new_x1 = min(max(0, new_x1), width)
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new_y1 = min(max(0, new_y1), height)
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new_x2 = max(min(width, new_x2), 0)
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new_y2 = max(min(height, new_y2), 0)
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sub_imgT = imgT[new_y1:new_y2, new_x1:new_x2]
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if dx > 0 or dy > 0 or edx > 0 or edy > 0:
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sub_imgT = cv2.copyMakeBorder(
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sub_imgT,
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dy,
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edy,
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dx,
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edx,
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cv2.BORDER_CONSTANT,
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value=(103.94, 116.78, 123.68))
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return sub_imgT, [new_x1, new_y1, new_x2, new_y2], [dx, dy, edx, edy]
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return sub_imgT, [new_x1, new_y1, new_x2, new_y2]
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def __call__(self, inputs) -> Any:
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image_size = 256
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img = LoadImage.convert_to_ndarray(inputs)
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h, w, c = img.shape
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img_rgb = copy.deepcopy(img)
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img_rgb = img_rgb[:, :, ::-1]
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det_result = self.face_detection(img_rgb)
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bboxes = np.array(det_result[OutputKeys.BOXES])
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if bboxes.shape[0] == 0:
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logger.warn('No face detected!')
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results = {
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OutputKeys.KEYPOINTS: [],
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OutputKeys.POSES: [],
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OutputKeys.BOXES: []
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}
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return results
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boxes, keypoints = self._choose_face(det_result)
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output_boxes = []
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output_keypoints = []
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output_poses = []
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for idx, box_ori in enumerate(boxes):
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box = self.expend_box(box_ori, w, h, scalex=0.15, scaley=0.15)
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for index, box_ori in enumerate(boxes):
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box = self.expend_box(box_ori, w, h, scalex=0.1, scaley=0.1)
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y0 = int(box[1])
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y1 = int(box[3])
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x0 = int(box[0])
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x1 = int(box[2])
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sub_img = img[y0:y1, x0:x1]
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keypoint = keypoints[idx]
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keypoint = keypoints[index]
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pts = [[keypoint[0], keypoint[1]], [keypoint[2], keypoint[3]],
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[keypoint[4], keypoint[5]], [keypoint[6], keypoint[7]],
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[keypoint[8], keypoint[9]], [box[0], box[1]],
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[box[2], box[3]]]
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[box[2], box[1]], [box[0], box[3]], [box[2], box[3]]]
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# radian
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angle = math.atan2((pts[1][1] - pts[0][1]),
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(pts[1][0] - pts[0][0]))
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# angle
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theta = angle * (180 / np.pi)
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center = [image_size // 2, image_size // 2]
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center = [w // 2, h // 2]
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cx, cy = center
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M, landmark_ = self.rotate_point(theta, (cx, cy), pts)
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sub_img, imgT, bbox, delta_border = self.rotate_crop_img(
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img, pts, M)
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sub_imgT, imgT, bbox = self.rotate_crop_img(img, landmark_, M)
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outputs = self.predict_op([sub_img])[0]
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outputs = self.predict_op([sub_imgT])[0]
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tmp_keypoints = outputs['point']
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for idx in range(0, len(tmp_keypoints)):
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tmp_keypoints[idx][0] += (delta_border[0] + bbox[0])
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tmp_keypoints[idx][1] += (delta_border[1] + bbox[1])
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tmp_keypoints[idx][0] += bbox[0]
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tmp_keypoints[idx][1] += bbox[1]
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for idx in range(0, 3):
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sub_img, bbox, delta_border = self.crop_img(
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imgT, tmp_keypoints, 0)
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for idx in range(0, 6):
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sub_img, bbox = self.crop_img(imgT, tmp_keypoints)
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outputs = self.predict_op([sub_img])[0]
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tmp_keypoints = outputs['point']
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for idx in range(0, len(tmp_keypoints)):
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tmp_keypoints[idx][0] += (delta_border[0] + bbox[0])
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tmp_keypoints[idx][1] += (delta_border[1] + bbox[1])
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tmp_keypoints[idx][0] += bbox[0]
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tmp_keypoints[idx][1] += bbox[1]
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M2, tmp_keypoints = self.rotate_point(-theta, (cx, cy),
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tmp_keypoints)
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