コード例 #1
0
ファイル: cascade.py プロジェクト: DLlearn/facefit
    def build(self, images, gt_shapes, boxes):
        self.mean_shape = util.centered_mean_shape(gt_shapes)
        self.n_landmarks = self.mean_shape.n_points
        # Generate initial shapes with perturbations.
        print_dynamic('Generating initial shapes')
        shapes = np.array([util.fit_shape_to_box(self.mean_shape, box) for box in boxes])

        print_dynamic('Perturbing initial estimates')
        if self.n_perturbations > 1:
            images, shapes, gt_shapes, boxes = util.perturb_shapes(images, shapes, gt_shapes, boxes,
                                                                   self.n_perturbations, mode='mean_shape')

        assert(len(boxes) == len(images))
        assert(len(shapes) == len(images))
        assert(len(gt_shapes) == len(images))

        print('\nSize of augmented dataset: {} images.\n'.format(len(images)))

        weak_regressors = []
        for j in xrange(self.n_stages):
            # Calculate normalized targets.
            deltas = [gt_shapes[i].points - shapes[i].points for i in xrange(len(images))]
            targets = np.array([util.transform_to_mean_shape(shapes[i], self.mean_shape).apply(deltas[i]).reshape((2*self.n_landmarks,))
                                for i in xrange(len(images))])

            weak_regressor = self.weak_builder.build(images, targets, (shapes, self.mean_shape, j))
            # Update current estimates of shapes.
            for i in xrange(len(images)):
                offset = weak_regressor.apply(images[i], shapes[i])
                shapes[i].points += offset.points
            weak_regressors.append(weak_regressor)
            print("\nBuilt outer regressor {}\n".format(j))

        return CascadedShapeRegressor(self.n_landmarks, weak_regressors, self.mean_shape)
コード例 #2
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ファイル: inner_cascade.py プロジェクト: DLlearn/facefit
 def apply(self, image, shape):
     mean_to_shape = util.transform_to_mean_shape(shape, self.mean_shape).pseudoinverse()
     shape_indexed_features = self.feature_extractor.extract_features(image, shape, mean_to_shape)
     res = PointCloud(np.zeros((self.n_landmarks, 2)), copy=False)
     for r in self.regressors:
         offset = r.apply(shape_indexed_features, self.extra)
         res.points += offset.reshape((self.n_landmarks, 2))
     return mean_to_shape.apply(res)
コード例 #3
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 def apply(self, image, shape):
     mean_to_shape = util.transform_to_mean_shape(shape, self.mean_shape).pseudoinverse()
     #feat = self.feature_extractor.apply(image, shape)
     #res = self.regression_matrix[feat == 1].sum(axis=0).reshape((self.n_landmarks, 2))
     #return mean_to_shape.apply(PointCloud(res, copy=False))
     indices = self.feature_extractor.get_indices(image, shape, mean_to_shape)
     return mean_to_shape.apply(PointCloud(self.regression_matrix[indices].sum(axis=0).reshape((self.n_landmarks, 2)),
                                           copy=False))
コード例 #4
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ファイル: linear_regression.py プロジェクト: DLlearn/facefit
 def apply(self, image, shape):
     mean_to_shape = util.transform_to_mean_shape(shape, self.mean_shape).pseudoinverse()
     #feat = self.feature_extractor.apply(image, shape)
     #res = self.regression_matrix[feat == 1].sum(axis=0).reshape((self.n_landmarks, 2))
     #return mean_to_shape.apply(PointCloud(res, copy=False))
     indices = self.feature_extractor.get_indices(image, shape, mean_to_shape)
     return mean_to_shape.apply(PointCloud(self.regression_matrix[indices].sum(axis=0).reshape((self.n_landmarks, 2)),
                                           copy=False))
コード例 #5
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 def apply(self, image, shape):
     mean_to_shape = util.transform_to_mean_shape(
         shape, self.mean_shape).pseudoinverse()
     shape_indexed_features = self.feature_extractor.extract_features(
         image, shape, mean_to_shape)
     res = PointCloud(np.zeros((self.n_landmarks, 2)), copy=False)
     for r in self.regressors:
         offset = r.apply(shape_indexed_features, self.extra)
         res.points += offset.reshape((self.n_landmarks, 2))
     return mean_to_shape.apply(res)
コード例 #6
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ファイル: feature_extractor.py プロジェクト: DLlearn/facefit
    def apply(self, img, shape):
        n_landmarks = len(self.forests)
        n_trees = len(self.forests[0].regressors)
        n_leaves = len(self.forests[0].regressors[0].leaves)
        local_binary_features = np.zeros(n_landmarks*n_trees*n_leaves)
        mean_to_shape = util.transform_to_mean_shape(shape, self.mean_shape).pseudoinverse()

        for landmark_i, f in enumerate(self.forests):
            pixels = f.feature_extractor.extract_features(img, shape, mean_to_shape)
            for tree_i, tree in enumerate(f.regressors):
                leaf = tree.get_leaf_index(pixels)
                local_binary_features[landmark_i*n_trees*n_leaves + tree_i*n_leaves + leaf] = 1
        return local_binary_features
コード例 #7
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    def build(self, images, gt_shapes, boxes):
        self.mean_shape = util.centered_mean_shape(gt_shapes)
        self.n_landmarks = self.mean_shape.n_points
        # Generate initial shapes with perturbations.
        print_dynamic('Generating initial shapes')
        shapes = np.array(
            [util.fit_shape_to_box(self.mean_shape, box) for box in boxes])

        print_dynamic('Perturbing initial estimates')
        if self.n_perturbations > 1:
            images, shapes, gt_shapes, boxes = util.perturb_shapes(
                images,
                shapes,
                gt_shapes,
                boxes,
                self.n_perturbations,
                mode='mean_shape')

        assert (len(boxes) == len(images))
        assert (len(shapes) == len(images))
        assert (len(gt_shapes) == len(images))

        print('\nSize of augmented dataset: {} images.\n'.format(len(images)))

        weak_regressors = []
        for j in xrange(self.n_stages):
            # Calculate normalized targets.
            deltas = [
                gt_shapes[i].points - shapes[i].points
                for i in xrange(len(images))
            ]
            targets = np.array([
                util.transform_to_mean_shape(shapes[i], self.mean_shape).apply(
                    deltas[i]).reshape((2 * self.n_landmarks, ))
                for i in xrange(len(images))
            ])

            weak_regressor = self.weak_builder.build(
                images, targets, (shapes, self.mean_shape, j))
            # Update current estimates of shapes.
            for i in xrange(len(images)):
                offset = weak_regressor.apply(images[i], shapes[i])
                shapes[i].points += offset.points
            weak_regressors.append(weak_regressor)
            print("\nBuilt outer regressor {}\n".format(j))

        return CascadedShapeRegressor(self.n_landmarks, weak_regressors,
                                      self.mean_shape)
コード例 #8
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    def apply(self, img, shape):
        n_landmarks = len(self.forests)
        n_trees = len(self.forests[0].regressors)
        n_leaves = len(self.forests[0].regressors[0].leaves)
        local_binary_features = np.zeros(n_landmarks * n_trees * n_leaves)
        mean_to_shape = util.transform_to_mean_shape(
            shape, self.mean_shape).pseudoinverse()

        for landmark_i, f in enumerate(self.forests):
            pixels = f.feature_extractor.extract_features(
                img, shape, mean_to_shape)
            for tree_i, tree in enumerate(f.regressors):
                leaf = tree.get_leaf_index(pixels)
                local_binary_features[landmark_i * n_trees * n_leaves +
                                      tree_i * n_leaves + leaf] = 1
        return local_binary_features
コード例 #9
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ファイル: inner_cascade.py プロジェクト: DLlearn/facefit
 def to_mean(self, shape):
     return util.transform_to_mean_shape(shape, self.mean_shape)
コード例 #10
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 def to_mean(self, shape):
     return util.transform_to_mean_shape(shape, self.mean_shape)