diff --git a/docs/apis/visualize.md b/docs/apis/visualize.md index 8fe45d4abb82c01c859f0be60bf6f52706eb4e52..2cdc96844758128545ffe3a1ebf815476cae1090 100755 --- a/docs/apis/visualize.md +++ b/docs/apis/visualize.md @@ -167,3 +167,20 @@ NormLIME是利用一定数量的样本来出一个全局的解释。由于NormLI ### 使用示例 > 对预测可解释性结果可视化的过程可参见[代码](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/interpret/normlime.py)。 + +## 数据预处理/增强过程可视化 +``` +paddlex.transforms.visualize(dataset, + img_count=3, + save_dir='vdl_output') +``` +对数据预处理/增强中间结果进行可视化。 +可使用VisualDL查看中间结果： +1. VisualDL启动方式: visualdl --logdir vdl_output --port 8001 +2. 浏览器打开 https://0.0.0.0:8001即可， + 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP + +### 参数 +>* **dataset** (paddlex.datasets): 数据集读取器。 +>* **img_count** (int): 需要进行数据预处理/增强的图像数目。默认为3。 +>* **save_dir** (str): 日志保存的路径。默认为'vdl_output'。 \ No newline at end of file diff --git a/paddlex/__init__.py b/paddlex/__init__.py index 6fc8aff1d3fdbc08a7474627bf38f2af17599fb3..7743882a6334e257c1a7a4b94566aff3a8a55667 100644 --- a/paddlex/__init__.py +++ b/paddlex/__init__.py @@ -48,6 +48,7 @@ if hub.version.hub_version < '1.6.2': env_info = get_environ_info() load_model = cv.models.load_model datasets = cv.datasets +transforms = cv.transforms log_level = 2 diff --git a/paddlex/cv/transforms/__init__.py b/paddlex/cv/transforms/__init__.py index 37c14e75f72f8c6b76a608116419d58437fab99e..c74b5b19e8d1e007674f6d17a30736f42dde1789 100644 --- a/paddlex/cv/transforms/__init__.py +++ b/paddlex/cv/transforms/__init__.py @@ -15,3 +15,5 @@ from . import cls_transforms from . import det_transforms from . import seg_transforms +from . import visualize +visualize = visualize.visualize diff --git a/paddlex/cv/transforms/cls_transforms.py b/paddlex/cv/transforms/cls_transforms.py index 6b11a0839d2b2bc891f8eb29dfe666a69d0c8f5d..606bb5b8d6eb4605510f734d9b737811ec22c477 100644 --- a/paddlex/cv/transforms/cls_transforms.py +++ b/paddlex/cv/transforms/cls_transforms.py @@ -32,10 +32,8 @@ class ClsTransform: class Compose(ClsTransform): """根据数据预处理/增强算子对输入数据进行操作。 所有操作的输入图像流形状均是[H, W, C]，其中H为图像高，W为图像宽，C为图像通道数。 - Args: transforms (list): 数据预处理/增强算子。 - Raises: TypeError: 形参数据类型不满足需求。 ValueError: 数据长度不匹配。 @@ -434,6 +432,7 @@ class RandomDistort(ClsTransform): params['im'] = im if np.random.uniform(0, 1) < prob: im = ops[id](**params) + im = im.astype('float32') if label is None: return (im, ) else: diff --git a/paddlex/cv/transforms/det_transforms.py b/paddlex/cv/transforms/det_transforms.py index 55b3daa85b6380068597e6de9946e6e4641216d6..dd193d2f14aac37b58b2af9ee49e8d99d51f05ad 100644 --- a/paddlex/cv/transforms/det_transforms.py +++ b/paddlex/cv/transforms/det_transforms.py @@ -41,10 +41,8 @@ class DetTransform: class Compose(DetTransform): """根据数据预处理/增强列表对输入数据进行操作。 所有操作的输入图像流形状均是[H, W, C]，其中H为图像高，W为图像宽，C为图像通道数。 - Args: transforms (list): 数据预处理/增强列表。 - Raises: TypeError: 形参数据类型不满足需求。 ValueError: 数据长度不匹配。 @@ -619,6 +617,7 @@ class RandomDistort(DetTransform): if np.random.uniform(0, 1) < prob: im = ops[id](**params) + im = im.astype('float32') if label_info is None: return (im, im_info) else: @@ -823,7 +822,7 @@ class RandomExpand(DetTransform): 'gt_class' not in label_info: raise TypeError('Cannot do RandomExpand! ' + \ 'Becasuse gt_bbox/gt_class is not in label_info!') - if np.random.uniform(0., 1.) < self.prob: + if np.random.uniform(0., 1.) > self.prob: return (im, im_info, label_info) if 'gt_class' in label_info and 0 in label_info['gt_class']: diff --git a/paddlex/cv/transforms/seg_transforms.py b/paddlex/cv/transforms/seg_transforms.py index 4932a7002983ff62c04ec0ed992efac323ee546b..f2bfb32ebeed11f84c27ab7f1d8e8920f21699a7 100644 --- a/paddlex/cv/transforms/seg_transforms.py +++ b/paddlex/cv/transforms/seg_transforms.py @@ -35,14 +35,11 @@ class SegTransform: class Compose(SegTransform): """根据数据预处理/增强算子对输入数据进行操作。 所有操作的输入图像流形状均是[H, W, C]，其中H为图像高，W为图像宽，C为图像通道数。 - Args: transforms (list): 数据预处理/增强算子。 - Raises: TypeError: transforms不是list对象 ValueError: transforms元素个数小于1。 - """ def __init__(self, transforms): @@ -71,7 +68,6 @@ class Compose(SegTransform): 图像在过resize前shape为(200, 300)， 过padding前shape为 (400, 600) label (str/np.ndarray): 标注图像路径/标注图像np.ndarray数据。 - Returns: tuple: 根据网络所需字段所组成的tuple；字段由transforms中的最后一个数据预处理操作决定。 """ @@ -1054,6 +1050,7 @@ class RandomDistort(SegTransform): params['im'] = im if np.random.uniform(0, 1) < prob: im = ops[id](**params) + im = im.astype('float32') if label is None: return (im, im_info) else: diff --git a/paddlex/cv/transforms/visualize.py b/paddlex/cv/transforms/visualize.py new file mode 100644 index 0000000000000000000000000000000000000000..514ac73b2107caade31a5e760b1ec6fa2d4d8076 --- /dev/null +++ b/paddlex/cv/transforms/visualize.py @@ -0,0 +1,306 @@ +# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import os.path as osp +import cv2 +from PIL import Image +import numpy as np +import math +from .imgaug_support import execute_imgaug +from .cls_transforms import ClsTransform +from .det_transforms import DetTransform +from .seg_transforms import SegTransform +import paddlex as pdx +from paddlex.cv.models.utils.visualize import get_color_map_list + + +def _draw_rectangle_and_cname(img, xmin, ymin, xmax, ymax, cname, color): + """ 根据提供的标注信息，给图片描绘框体和类别显示 + + Args: + img: 图片路径 + xmin: 检测框最小的x坐标 + ymin: 检测框最小的y坐标 + xmax: 检测框最大的x坐标 + ymax: 检测框最大的y坐标 + cname: 类别信息 + color: 类别与颜色的对应信息 + """ + # 描绘检测框 + line_width = math.ceil(2 * max(img.shape[0:2]) / 600) + cv2.rectangle( + img, + pt1=(xmin, ymin), + pt2=(xmax, ymax), + color=color, + thickness=line_width) + return img + +def cls_compose(im, label=None, transforms=None, vdl_writer=None, step=0): + """ + Args: + im (str/np.ndarray): 图像路径/图像np.ndarray数据。 + label (int): 每张图像所对应的类别序号。 + vdl_writer (visualdl.LogWriter): VisualDL存储器，日志信息将保存在其中。 + 当为None时，不对日志进行保存。默认为None。 + step (int): 数据预处理的轮数，当vdl_writer不为None时有效。默认为0。 + + Returns: + tuple: 根据网络所需字段所组成的tuple； + 字段由transforms中的最后一个数据预处理操作决定。 + """ + if isinstance(im, np.ndarray): + if len(im.shape) != 3: + raise Exception( + "im should be 3-dimension, but now is {}-dimensions". + format(len(im.shape))) + else: + try: + im = cv2.imread(im).astype('float32') + except: + raise TypeError('Can\'t read The image file {}!'.format(im)) + im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) + if vdl_writer is not None: + vdl_writer.add_image(tag='0. OriginalImange/' + str(step), + img=im, + step=0) + op_id = 1 + for op in transforms: + if isinstance(op, ClsTransform): + if vdl_writer is not None and hasattr(op, 'prob'): + op.prob = 1.0 + outputs = op(im, label) + im = outputs[0] + if len(outputs) == 2: + label = outputs[1] + if isinstance(op, pdx.cv.transforms.cls_transforms.Normalize): + continue + else: + import imgaug.augmenters as iaa + if isinstance(op, iaa.Augmenter): + im = execute_imgaug(op, im) + outputs = (im, ) + if label is not None: + outputs = (im, label) + if vdl_writer is not None: + tag = str(op_id) + '. ' + op.__class__.__name__ + '/' + str(step) + vdl_writer.add_image(tag=tag, + img=im, + step=0) + op_id += 1 + +def det_compose(im, im_info=None, label_info=None, transforms=None, vdl_writer=None, step=0, + labels=[], catid2color=None): + def decode_image(im_file, im_info, label_info): + if im_info is None: + im_info = dict() + if isinstance(im_file, np.ndarray): + if len(im_file.shape) != 3: + raise Exception( + "im should be 3-dimensions, but now is {}-dimensions". + format(len(im_file.shape))) + im = im_file + else: + try: + im = cv2.imread(im_file).astype('float32') + except: + raise TypeError('Can\'t read The image file {}!'.format( + im_file)) + im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) + # make default im_info with [h, w, 1] + im_info['im_resize_info'] = np.array( + [im.shape[0], im.shape[1], 1.], dtype=np.float32) + im_info['image_shape'] = np.array([im.shape[0], + im.shape[1]]).astype('int32') + use_mixup = False + for t in transforms: + if type(t).__name__ == 'MixupImage': + use_mixup = True + if not use_mixup: + if 'mixup' in im_info: + del im_info['mixup'] + # decode mixup image + if 'mixup' in im_info: + im_info['mixup'] = \ + decode_image(im_info['mixup'][0], + im_info['mixup'][1], + im_info['mixup'][2]) + if label_info is None: + return (im, im_info) + else: + return (im, im_info, label_info) + + outputs = decode_image(im, im_info, label_info) + im = outputs[0] + im_info = outputs[1] + if len(outputs) == 3: + label_info = outputs[2] + if vdl_writer is not None: + vdl_writer.add_image(tag='0. OriginalImange/' + str(step), + img=im, + step=0) + op_id = 1 + bboxes = label_info['gt_bbox'] + transforms = [None] + transforms + for op in transforms: + if im is None: + return None + if isinstance(op, DetTransform) or op is None: + if vdl_writer is not None and hasattr(op, 'prob'): + op.prob = 1.0 + if op is not None: + outputs = op(im, im_info, label_info) + else: + outputs = (im, im_info, label_info) + im = outputs[0] + vdl_im = im + if vdl_writer is not None: + if isinstance(op, pdx.cv.transforms.det_transforms.ResizeByShort): + scale = outputs[1]['im_resize_info'][2] + bboxes = bboxes * scale + elif isinstance(op, pdx.cv.transforms.det_transforms.Resize): + h = outputs[1]['image_shape'][0] + w = outputs[1]['image_shape'][1] + target_size = op.target_size + if isinstance(target_size, int): + h_scale = float(target_size) / h + w_scale = float(target_size) / w + else: + h_scale = float(target_size[0]) / h + w_scale = float(target_size[1]) / w + bboxes[:,0] = bboxes[:,0] * w_scale + bboxes[:,1] = bboxes[:,1] * h_scale + bboxes[:,2] = bboxes[:,2] * w_scale + bboxes[:,3] = bboxes[:,3] * h_scale + else: + bboxes = outputs[2]['gt_bbox'] + if not isinstance(op, pdx.cv.transforms.det_transforms.RandomHorizontalFlip): + for i in range(bboxes.shape[0]): + bbox = bboxes[i] + cname = labels[outputs[2]['gt_class'][i][0]-1] + vdl_im = _draw_rectangle_and_cname(vdl_im, + int(bbox[0]), + int(bbox[1]), + int(bbox[2]), + int(bbox[3]), + cname, + catid2color[outputs[2]['gt_class'][i][0]-1]) + if isinstance(op, pdx.cv.transforms.det_transforms.Normalize): + continue + else: + im = execute_imgaug(op, im) + if label_info is not None: + outputs = (im, im_info, label_info) + else: + outputs = (im, im_info) + vdl_im = im + if vdl_writer is not None: + tag = str(op_id) + '. ' + op.__class__.__name__ + '/' + str(step) + if op is None: + tag = str(op_id) + '. OriginalImangeWithGTBox/' + str(step) + vdl_writer.add_image(tag=tag, + img=vdl_im, + step=0) + op_id += 1 + +def seg_compose(im, im_info=None, label=None, transforms=None, vdl_writer=None, step=0): + if im_info is None: + im_info = list() + if isinstance(im, np.ndarray): + if len(im.shape) != 3: + raise Exception( + "im should be 3-dimensions, but now is {}-dimensions". + format(len(im.shape))) + else: + try: + im = cv2.imread(im).astype('float32') + except: + raise ValueError('Can\'t read The image file {}!'.format(im)) + im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) + if label is not None: + if not isinstance(label, np.ndarray): + label = np.asarray(Image.open(label)) + if vdl_writer is not None: + vdl_writer.add_image(tag='0. OriginalImange' + '/' + str(step), + img=im, + step=0) + op_id = 1 + for op in transforms: + if isinstance(op, SegTransform): + outputs = op(im, im_info, label) + im = outputs[0] + if len(outputs) >= 2: + im_info = outputs[1] + if len(outputs) == 3: + label = outputs[2] + if isinstance(op, pdx.cv.transforms.seg_transforms.Normalize): + continue + else: + im = execute_imgaug(op, im) + if label is not None: + outputs = (im, im_info, label) + else: + outputs = (im, im_info) + if vdl_writer is not None: + tag = str(op_id) + '. ' + op.__class__.__name__ + '/' + str(step) + vdl_writer.add_image(tag=tag, + img=im, + step=0) + op_id += 1 + +def visualize(dataset, img_count=3, save_dir='vdl_output'): + '''对数据预处理/增强中间结果进行可视化。 + 可使用VisualDL查看中间结果： + 1. VisualDL启动方式: visualdl --logdir vdl_output --port 8001 + 2. 浏览器打开 https://0.0.0.0:8001即可， + 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP + + Args: + dataset (paddlex.datasets): 数据集读取器。 + img_count (int): 需要进行数据预处理/增强的图像数目。默认为3。 + save_dir (str): 日志保存的路径。默认为'vdl_output'。 + ''' + if dataset.num_samples < img_count: + img_count = dataset.num_samples + transforms = dataset.transforms + if not osp.isdir(save_dir): + if osp.exists(save_dir): + os.remove(save_dir) + os.makedirs(save_dir) + from visualdl import LogWriter + vdl_save_dir = osp.join(save_dir, 'image_transforms') + vdl_writer = LogWriter(vdl_save_dir) + for i, data in enumerate(dataset.iterator()): + if i == img_count: + break + data.append(transforms.transforms) + data.append(vdl_writer) + data.append(i) + if isinstance(transforms, ClsTransform): + cls_compose(*data) + elif isinstance(transforms, DetTransform): + labels = dataset.labels + color_map = get_color_map_list(len(labels) + 1) + catid2color = {} + for catid in range(len(labels)): + catid2color[catid] = color_map[catid + 1] + data.append(labels) + data.append(catid2color) + det_compose(*data) + elif isinstance(transforms, SegTransform): + seg_compose(*data) + else: + raise Exception('The transform must the subclass of \ + ClsTransform or DetTransform or SegTransform!') \ No newline at end of file