mirror of https://github.com/alibaba/EasyCV.git
127 lines
4.8 KiB
Python
127 lines
4.8 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import cv2
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from easycv.predictors.builder import PREDICTORS
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from .base import PredictorV2
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face_contour_point_index = [
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
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21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32
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]
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left_eye_brow_point_index = [33, 34, 35, 36, 37, 38, 39, 40, 41, 33]
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right_eye_brow_point_index = [42, 43, 44, 45, 46, 47, 48, 49, 50, 42]
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left_eye_point_index = [66, 67, 68, 69, 70, 71, 72, 73, 66]
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right_eye_point_index = [75, 76, 77, 78, 79, 80, 81, 82, 75]
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nose_bridge_point_index = [51, 52, 53, 54]
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nose_contour_point_index = [55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]
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mouth_outer_point_index = [84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 84]
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mouth_inter_point_index = [96, 97, 98, 99, 100, 101, 102, 103, 96]
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@PREDICTORS.register_module()
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class FaceKeypointsPredictor(PredictorV2):
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"""Predict pipeline for face keypoint
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Args:
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model_path (str): Path of model path
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config_file (str): config file path for model and processor to init. Defaults to None.
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batch_size (int): batch size for forward.
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device (str): Support 'cuda' or 'cpu', if is None, detect device automatically.
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save_results (bool): Whether to save predict results.
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save_path (str): File path for saving results, only valid when `save_results` is True.
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pipelines (list[dict]): Data pipeline configs.
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"""
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def __init__(self,
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model_path,
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config_file,
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batch_size=1,
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device=None,
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save_results=False,
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save_path=None,
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pipelines=None):
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super(FaceKeypointsPredictor, self).__init__(
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model_path,
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config_file,
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batch_size=batch_size,
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device=device,
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save_results=save_results,
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save_path=save_path,
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pipelines=pipelines)
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self.input_size = self.cfg.IMAGE_SIZE
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self.point_number = self.cfg.POINT_NUMBER
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def preprocess(self, inputs, *args, **kwargs):
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batch_outputs = super().preprocess(inputs, *args, **kwargs)
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self.img_metas = batch_outputs['img_metas']
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return batch_outputs
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def postprocess(self, inputs, *args, **kwargs):
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results = []
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points = inputs['point'].cpu().numpy()
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poses = inputs['pose'].cpu().numpy()
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for idx, point in enumerate(points):
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h, w, c = self.img_metas[idx]['img_shape']
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scale_h = h / self.input_size
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scale_w = w / self.input_size
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point = point.reshape((self.point_number, 2))
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for index in range(len(point)):
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point[index][0] *= scale_w
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point[index][1] *= scale_h
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results.append({'point': point, 'pose': poses[idx]})
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return results
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def show_result(self, img, points, scale=4.0, save_path=None):
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"""Draw `result` over `img`.
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Args:
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img ( ndarray ): The image to be displayed.
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result (list): The face keypoints to draw over `img`.
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scale: zoom in or out scale
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save_path: path to save drawned 'img'
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Returns:
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img (ndarray): Only if not `show` or `out_file`
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"""
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image = cv2.resize(img, dsize=None, fx=scale, fy=scale)
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def draw_line(point_index, image, point):
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for i in range(len(point_index) - 1):
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cur_index = point_index[i]
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next_index = point_index[i + 1]
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cur_pt = (int(point[cur_index][0] * scale),
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int(point[cur_index][1] * scale))
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next_pt = (int(point[next_index][0] * scale),
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int(point[next_index][1] * scale))
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cv2.line(image, cur_pt, next_pt, (0, 0, 255), thickness=2)
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draw_line(face_contour_point_index, image, points)
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draw_line(left_eye_brow_point_index, image, points)
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draw_line(right_eye_brow_point_index, image, points)
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draw_line(left_eye_point_index, image, points)
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draw_line(right_eye_point_index, image, points)
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draw_line(nose_bridge_point_index, image, points)
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draw_line(nose_contour_point_index, image, points)
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draw_line(mouth_outer_point_index, image, points)
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draw_line(mouth_inter_point_index, image, points)
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size = len(points)
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for i in range(size):
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x = int(points[i][0])
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y = int(points[i][1])
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cv2.putText(image, str(i), (int(x * scale), int(y * scale)),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
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cv2.circle(image, (int(x * scale), int(y * scale)), 2, (0, 255, 0),
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cv2.FILLED)
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if save_path is not None:
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cv2.imwrite(save_path, image)
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return image
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