From 1bd909c193e25ed685e1e614c8bfe311a6736e68 Mon Sep 17 00:00:00 2001 From: wangguanzhong Date: Tue, 10 Dec 2019 17:09:43 +0800 Subject: [PATCH] fix im_info (#95) --- ppdet/data/transform/operators.py | 62 ++++++++++++++++--------------- 1 file changed, 32 insertions(+), 30 deletions(-) diff --git a/ppdet/data/transform/operators.py b/ppdet/data/transform/operators.py index c0d665746..4b9c3d4b9 100644 --- a/ppdet/data/transform/operators.py +++ b/ppdet/data/transform/operators.py @@ -287,8 +287,8 @@ class ResizeImage(BaseOperator): im_scale_x = im_scale im_scale_y = im_scale - resize_w = np.round(im_scale_x * float(im_shape[1])) - resize_h = np.round(im_scale_y * float(im_shape[0])) + resize_w = im_scale_x * float(im_shape[1]) + resize_h = im_scale_y * float(im_shape[0]) im_info = [resize_h, resize_w, im_scale] if 'im_info' in sample and sample['im_info'][2] != 1.: sample['im_info'] = np.append( @@ -311,8 +311,12 @@ class ResizeImage(BaseOperator): fy=im_scale_y, interpolation=self.interp) else: + if self.max_size != 0: + raise TypeError( + 'If you set max_size to cap the maximum size of image,' + 'please set use_cv2 to True to resize the image.') im = Image.fromarray(im) - im = im.resize((resize_w, resize_h), self.interp) + im = im.resize((int(resize_w), int(resize_h)), self.interp) im = np.array(im) sample['image'] = im @@ -1009,9 +1013,8 @@ class Resize(BaseOperator): 'random' (for randomized interpolation). default to `cv2.INTER_LINEAR`. """ - def __init__(self, - target_dim=[], - interp=cv2.INTER_LINEAR): + + def __init__(self, target_dim=[], interp=cv2.INTER_LINEAR): super(Resize, self).__init__() self.target_dim = target_dim self.interp = interp # 'random' for yolov3 @@ -1032,10 +1035,9 @@ class Resize(BaseOperator): scale_x = dim / w scale_y = dim / h if 'gt_bbox' in sample and len(sample['gt_bbox']) > 0: - scale_array = np.array([scale_x, scale_y] * 2, - dtype=np.float32) - sample['gt_bbox'] = np.clip( - sample['gt_bbox'] * scale_array, 0, dim - 1) + scale_array = np.array([scale_x, scale_y] * 2, dtype=np.float32) + sample['gt_bbox'] = np.clip(sample['gt_bbox'] * scale_array, 0, + dim - 1) sample['h'] = resize_h sample['w'] = resize_w @@ -1060,6 +1062,7 @@ class ColorDistort(BaseOperator): random_apply (bool): whether to apply in random (yolo) or fixed (SSD) order. """ + def __init__(self, hue=[-18, 18, 0.5], saturation=[0.5, 1.5, 0.5], @@ -1084,14 +1087,10 @@ class ColorDistort(BaseOperator): delta = np.random.uniform(low, high) u = np.cos(delta * np.pi) w = np.sin(delta * np.pi) - bt = np.array([[1.0, 0.0, 0.0], - [0.0, u, -w], - [0.0, w, u]]) - tyiq = np.array([[0.299, 0.587, 0.114], - [0.596, -0.274, -0.321], + bt = np.array([[1.0, 0.0, 0.0], [0.0, u, -w], [0.0, w, u]]) + tyiq = np.array([[0.299, 0.587, 0.114], [0.596, -0.274, -0.321], [0.211, -0.523, 0.311]]) - ityiq = np.array([[1.0, 0.956, 0.621], - [1.0, -0.272, -0.647], + ityiq = np.array([[1.0, 0.956, 0.621], [1.0, -0.272, -0.647], [1.0, -1.107, 1.705]]) t = np.dot(np.dot(ityiq, bt), tyiq).T img = np.dot(img, t) @@ -1135,10 +1134,8 @@ class ColorDistort(BaseOperator): img = sample['image'] if self.random_apply: distortions = np.random.permutation([ - self.apply_brightness, - self.apply_contrast, - self.apply_saturation, - self.apply_hue + self.apply_brightness, self.apply_contrast, + self.apply_saturation, self.apply_hue ]) for func in distortions: img = func(img) @@ -1167,6 +1164,7 @@ class NormalizePermute(BaseOperator): mean (list): mean values in RGB order. std (list): std values in RGB order. """ + def __init__(self, mean=[123.675, 116.28, 103.53], std=[58.395, 57.120, 57.375]): @@ -1197,7 +1195,8 @@ class RandomExpand(BaseOperator): prob (float): probability to expand. fill_value (list): color value used to fill the canvas. in RGB order. """ - def __init__(self, ratio=4., prob=0.5, fill_value=(127.5,) * 3): + + def __init__(self, ratio=4., prob=0.5, fill_value=(127.5, ) * 3): super(RandomExpand, self).__init__() assert ratio > 1.01, "expand ratio must be larger than 1.01" self.ratio = ratio @@ -1205,7 +1204,7 @@ class RandomExpand(BaseOperator): assert isinstance(fill_value, (Number, Sequence)), \ "fill value must be either float or sequence" if isinstance(fill_value, Number): - fill_value = (fill_value,) * 3 + fill_value = (fill_value, ) * 3 if not isinstance(fill_value, tuple): fill_value = tuple(fill_value) self.fill_value = fill_value @@ -1251,6 +1250,7 @@ class RandomCrop(BaseOperator): allow_no_crop (bool): allow return without actually cropping them. cover_all_box (bool): ensure all bboxes are covered in the final crop. """ + def __init__(self, aspect_ratio=[.5, 2.], thresholds=[.0, .1, .3, .5, .7, .9], @@ -1295,15 +1295,16 @@ class RandomCrop(BaseOperator): for i in range(self.num_attempts): scale = np.random.uniform(*self.scaling) min_ar, max_ar = self.aspect_ratio - aspect_ratio = np.random.uniform(max(min_ar, scale**2), - min(max_ar, scale**-2)) + aspect_ratio = np.random.uniform( + max(min_ar, scale**2), min(max_ar, scale**-2)) crop_h = int(h * scale / np.sqrt(aspect_ratio)) crop_w = int(w * scale * np.sqrt(aspect_ratio)) crop_y = np.random.randint(0, h - crop_h) crop_x = np.random.randint(0, w - crop_w) crop_box = [crop_x, crop_y, crop_x + crop_w, crop_y + crop_h] - iou = self._iou_matrix(gt_bbox, - np.array([crop_box], dtype=np.float32)) + iou = self._iou_matrix( + gt_bbox, np.array( + [crop_box], dtype=np.float32)) if iou.max() < thresh: continue @@ -1311,7 +1312,8 @@ class RandomCrop(BaseOperator): continue cropped_box, valid_ids = self._crop_box_with_center_constraint( - gt_bbox, np.array(crop_box, dtype=np.float32)) + gt_bbox, np.array( + crop_box, dtype=np.float32)) if valid_ids.size > 0: found = True break @@ -1349,8 +1351,8 @@ class RandomCrop(BaseOperator): cropped_box[:, 2:] -= crop[:2] centers = (box[:, :2] + box[:, 2:]) / 2 - valid = np.logical_and( - crop[:2] <= centers, centers < crop[2:]).all(axis=1) + valid = np.logical_and(crop[:2] <= centers, + centers < crop[2:]).all(axis=1) valid = np.logical_and( valid, (cropped_box[:, :2] < cropped_box[:, 2:]).all(axis=1)) -- GitLab