Skip to content

P
PaddleDetection

PaddlePaddle / PaddleDetection
大约 1 年 前同步成功

通知 695
Star 11112
Fork 2696
P
PaddleDetection
未验证 提交 56cbc020 编写于 作者: G Guanghua Yu 提交者： GitHub
浏览文件
操作

add tools for voc dataset to coco (#1348)

上级 74410ff9
隐藏空白更改
内联 并排
Showing with 471 addition and 641 deletion
docs/advanced_tutorials/READER.md
浏览文件 @ 56cbc020
......@@ -34,8 +34,6 @@ PaddleDetection的数据处理模块是一个Python模块，所有代码逻辑
├── tests # 单元测试模块
│ ├── test_dataset.py # 对数据集解析、加载等进行单元测试
│ │ ...
├── tools # 一些有用的工具
│ ├── x2coco.py # 将其他数据集转换为COCO数据集格式
├── transform # 数据预处理模块
│ ├── batch_operators.py # 定义各类基于批量数据的预处理算子
│ ├── op_helper.py # 预处理算子的辅助函数
......
docs/advanced_tutorials/TRANSFER_LEARNING.md
浏览文件 @ 56cbc020
......@@ -8,7 +8,7 @@ In transfer learning, if different dataset and the number of classes is used, th
### Use custom dataset
Transfer learning needs custom dataset and annotation in COCO-format and VOC-format is supported now. The script converts the annotation from labelme or cityscape to COCO is provided in ```ppdet/data/tools/x2coco.py```. More details please refer to [READER](READER.md). After data preparation, update the data parameters in configuration file.
Transfer learning needs custom dataset and annotation in COCO-format and VOC-format is supported now. The script converts the annotation from voc, labelme or cityscape to COCO is provided in ```tools/x2coco.py```. More details please refer to [READER](READER.md). After data preparation, update the data parameters in configuration file.
1. COCO-format dataset, take [yolov3\_darknet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_darknet.yml#L66) for example, modify the COCODataSet in yolov3\_reader:
......
docs/advanced_tutorials/TRANSFER_LEARNING_cn.md
浏览文件 @ 56cbc020
......@@ -7,7 +7,7 @@
### 选择数据
迁移学习需要使用自己的数据集，目前已支持COCO和VOC的数据标注格式，在```ppdet/data/tools/x2coco.py```中给出了labelme和cityscape标注格式转换为COCO格式的脚本，具体使用方式可以参考[自定义数据源](READER.md)。数据准备完成后，在配置文件中配置数据路径，对应修改reader中的路径参数即可。
迁移学习需要使用自己的数据集，目前已支持COCO和VOC的数据标注格式，在```tools/x2coco.py```中给出了voc、labelme和cityscape标注格式转换为COCO格式的脚本，具体使用方式可以参考[自定义数据源](READER.md)。数据准备完成后，在配置文件中配置数据路径，对应修改reader中的路径参数即可。
1. COCO数据集需要修改COCODataSet中的参数，以[yolov3\_darknet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_darknet.yml#L66)为例，修改yolov3\_reader中的配置：
......
docs/tutorials/Custom_DataSet.md
浏览文件 @ 56cbc020
......@@ -15,26 +15,42 @@
### 方式一：将数据集转换为COCO格式
在`./tools/`中提供了`x2coco.py`用于将labelme标注的数据集或cityscape数据集转换为COCO数据集:
在`./tools/`中提供了`x2coco.py`用于将voc格式数据集、labelme标注的数据集或cityscape数据集转换为COCO数据集，例如:
（1）labelmes数据转换为COCO格式：
```bash
python ./ppdet/data/tools/x2coco.py \
python tools/x2coco.py \
--dataset_type labelme \
--json_input_dir ./labelme_annos/ \
--image_input_dir ./labelme_imgs/ \
--output_dir ./cocome/ \
--train_proportion 0.8 \
--val_proportion 0.2 \
--test_proportion 0.0 \
--test_proportion 0.0
```
（2）voc数据转换为COCO格式：
```bash
python tools/x2coco.py \
--dataset_type voc \
--voc_anno_dir path/to/VOCdevkit/VOC2007/Annotations/ \
--voc_anno_list path/to/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt \
--voc_label_list dataset/voc/label_list.txt \
--voc_out_name voc_train.json
```
**参数说明：**
- `--dataset_type`：需要转换的数据格式，目前支持：’labelme‘和’cityscape‘
- `--dataset_type`：需要转换的数据格式，目前支持：’voc‘、’labelme‘和’cityscape‘
- `--json_input_dir`：使用labelme标注的json文件所在文件夹
- `--image_input_dir`：图像文件所在文件夹
- `--output_dir`：转换后的COCO格式数据集存放位置
- `--train_proportion`：标注数据中用于train的比例
- `--val_proportion`：标注数据中用于validation的比例
- `--test_proportion`：标注数据中用于infer的比例
- `--voc_anno_dir`：VOC数据转换为COCO数据集时的voc数据集标注文件路径
- `--voc_anno_list`：VOC数据转换为COCO数据集时的标注列表文件，一般是`ImageSets/Main`下trainval.txt和test.txt文件
- `--voc_label_list`：VOC数据转换为COCO数据集时的类别列表文件，文件中每一行表示一种物体类别
- `--voc_out_name`：VOC数据转换为COCO数据集时的输出的COCO数据集格式json文件名
### 方式二：将数据集转换为VOC格式
......
ppdet/data/source/coco.py
浏览文件 @ 56cbc020
......@@ -137,14 +137,14 @@ class COCODataSet(DataSet):
y1 = max(0, y)
x2 = min(im_w - 1, x1 + max(0, box_w - 1))
y2 = min(im_h - 1, y1 + max(0, box_h - 1))
if inst['area'] > 0 and x2 >= x1 and y2 >= y1:
if x2 >= x1 and y2 >= y1:
inst['clean_bbox'] = [x1, y1, x2, y2]
bboxes.append(inst)
else:
logger.warn(
'Found an invalid bbox in annotations: im_id: {}, '
'area: {} x1: {}, y1: {}, x2: {}, y2: {}.'.format(
img_id, float(inst['area']), x1, y1, x2, y2))
'x1: {}, y1: {}, x2: {}, y2: {}.'.format(
img_id, x1, y1, x2, y2))
num_bbox = len(bboxes)
gt_bbox = np.zeros((num_bbox, 4), dtype=np.float32)
......
tools/cpp_infer.py 已删除 100644 → 0
浏览文件 @ 74410ff9
import os
import time
import numpy as np
from PIL import Image, ImageDraw
import paddle.fluid as fluid
import argparse
import cv2
import yaml
import copy
import logging
FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
logging.basicConfig(level=logging.INFO, format=FORMAT)
logger = logging.getLogger(__name__)
precision_map = {
'trt_int8': fluid.core.AnalysisConfig.Precision.Int8,
'trt_fp32': fluid.core.AnalysisConfig.Precision.Float32,
'trt_fp16': fluid.core.AnalysisConfig.Precision.Half
}
def create_config(model_path, mode='fluid', batch_size=1, min_subgraph_size=3):
model_file = os.path.join(model_path, '__model__')
params_file = os.path.join(model_path, '__params__')
config = fluid.core.AnalysisConfig(model_file, params_file)
config.enable_use_gpu(100, 0)
config.switch_use_feed_fetch_ops(False)
config.switch_specify_input_names(True)
logger.info('min_subgraph_size = %d.' % (min_subgraph_size))
if mode in precision_map.keys():
config.enable_tensorrt_engine(
workspace_size=1 << 30,
max_batch_size=batch_size,
min_subgraph_size=min_subgraph_size,
precision_mode=precision_map[mode],
use_static=False,
use_calib_mode=mode == 'trt_int8')
logger.info('Run inference by {}.'.format(mode))
elif mode == 'fluid':
logger.info('Run inference by Fluid FP32.')
else:
logger.fatal(
'Wrong mode, only support trt_int8, trt_fp32, trt_fp16, fluid.')
return config
def offset_to_lengths(lod):
offset = lod[0]
lengths = [offset[i + 1] - offset[i] for i in range(len(offset) - 1)]
return [lengths]
def DecodeImage(im_path):
assert os.path.exists(im_path), "Image path {} can not be found".format(
im_path)
with open(im_path, 'rb') as f:
im = f.read()
data = np.frombuffer(im, dtype='uint8')
im = cv2.imdecode(data, 1) # BGR mode, but need RGB mode
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
return im
def get_extra_info(im, arch, shape, scale):
info = []
input_shape = []
im_shape = []
logger.info('The architecture is {}'.format(arch))
if 'YOLO' in arch:
im_size = np.array([shape[:2]]).astype('int32')
logger.info('Extra info: im_size')
info.append(im_size)
elif arch in ['SSD', 'Face']:
im_shape = np.array([shape[:2]]).astype('int32')
logger.info('Extra info: im_shape')
info.append([im_shape])
elif 'RetinaNet' in arch:
input_shape.extend(im.shape[2:])
im_info = np.array([input_shape + [scale]]).astype('float32')
logger.info('Extra info: im_info')
info.append(im_info)
elif 'RCNN' in arch:
input_shape.extend(im.shape[2:])
im_shape.extend(shape[:2])
im_info = np.array([input_shape + [scale]]).astype('float32')
im_shape = np.array([im_shape + [1.]]).astype('float32')
logger.info('Extra info: im_info, im_shape')
info.append(im_info)
info.append(im_shape)
else:
logger.error(
"Unsupported arch: {}, expect YOLO, SSD, RetinaNet, RCNN and Face".
format(arch))
return info
class Resize(object):
def __init__(self,
target_size,
max_size=0,
interp=cv2.INTER_LINEAR,
use_cv2=True,
image_shape=None):
super(Resize, self).__init__()
self.target_size = target_size
self.max_size = max_size
self.interp = interp
self.use_cv2 = use_cv2
self.image_shape = image_shape
def __call__(self, im):
origin_shape = im.shape[:2]
im_c = im.shape[2]
if self.max_size != 0:
im_size_min = np.min(origin_shape[0:2])
im_size_max = np.max(origin_shape[0:2])
im_scale = float(self.target_size) / float(im_size_min)
if np.round(im_scale * im_size_max) > self.max_size:
im_scale = float(self.max_size) / float(im_size_max)
im_scale_x = im_scale
im_scale_y = im_scale
resize_w = int(im_scale_x * float(origin_shape[1]))
resize_h = int(im_scale_y * float(origin_shape[0]))
else:
im_scale_x = float(self.target_size) / float(origin_shape[1])
im_scale_y = float(self.target_size) / float(origin_shape[0])
resize_w = self.target_size
resize_h = self.target_size
if self.use_cv2:
im = cv2.resize(
im,
None,
None,
fx=im_scale_x,
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 = im.astype('uint8')
im = Image.fromarray(im)
im = im.resize((int(resize_w), int(resize_h)), self.interp)
im = np.array(im)
# padding im
if self.max_size != 0 and self.image_shape is not None:
padding_im = np.zeros(
(self.max_size, self.max_size, im_c), dtype=np.float32)
im_h, im_w = im.shape[:2]
padding_im[:im_h, :im_w, :] = im
im = padding_im
return im, im_scale_x
class Normalize(object):
def __init__(self, mean, std, is_scale=True, is_channel_first=False):
super(Normalize, self).__init__()
self.mean = mean
self.std = std
self.is_scale = is_scale
self.is_channel_first = is_channel_first
def __call__(self, im):
im = im.astype(np.float32, copy=False)
if self.is_channel_first:
mean = np.array(self.mean)[:, np.newaxis, np.newaxis]
std = np.array(self.std)[:, np.newaxis, np.newaxis]
else:
mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
std = np.array(self.std)[np.newaxis, np.newaxis, :]
if self.is_scale:
im = im / 255.0
im -= mean
im /= std
return im
class Permute(object):
def __init__(self, to_bgr=False, channel_first=True):
self.to_bgr = to_bgr
self.channel_first = channel_first
def __call__(self, im):
if self.channel_first:
im = im.transpose((2, 0, 1))
if self.to_bgr:
im = im[[2, 1, 0], :, :]
return im.copy()
class PadStride(object):
def __init__(self, stride=0):
assert stride >= 0, "Unsupported stride: {},"
" the stride in PadStride must be greater "
"or equal to 0".format(stride)
self.coarsest_stride = stride
def __call__(self, im):
coarsest_stride = self.coarsest_stride
if coarsest_stride == 0:
return im
im_c, im_h, im_w = im.shape
pad_h = int(np.ceil(float(im_h) / coarsest_stride) * coarsest_stride)
pad_w = int(np.ceil(float(im_w) / coarsest_stride) * coarsest_stride)
padding_im = np.zeros((im_c, pad_h, pad_w), dtype=np.float32)
padding_im[:, :im_h, :im_w] = im
return padding_im
def Preprocess(img_path, arch, config):
img = DecodeImage(img_path)
orig_shape = img.shape
scale = 1.
data = []
data_config = copy.deepcopy(config)
for data_aug_conf in data_config:
obj = data_aug_conf.pop('type')
preprocess = eval(obj)(**data_aug_conf)
if obj == 'Resize':
img, scale = preprocess(img)
else:
img = preprocess(img)
img = img[np.newaxis, :] # N, C, H, W
data.append(img)
extra_info = get_extra_info(img, arch, orig_shape, scale)
data += extra_info
return data
def get_category_info(with_background, label_list):
if label_list[0] != 'background' and with_background:
label_list.insert(0, 'background')
if label_list[0] == 'background' and not with_background:
label_list = label_list[1:]
clsid2catid = {i: i for i in range(len(label_list))}
catid2name = {i: name for i, name in enumerate(label_list)}
return clsid2catid, catid2name
def clip_bbox(bbox):
xmin = max(min(bbox[0], 1.), 0.)
ymin = max(min(bbox[1], 1.), 0.)
xmax = max(min(bbox[2], 1.), 0.)
ymax = max(min(bbox[3], 1.), 0.)
return xmin, ymin, xmax, ymax
def bbox2out(results, clsid2catid, is_bbox_normalized=False):
"""
Args:
results: request a dict, should include: `bbox`, `im_id`,
if is_bbox_normalized=True, also need `im_shape`.
clsid2catid: class id to category id map of COCO2017 dataset.
is_bbox_normalized: whether or not bbox is normalized.
"""
xywh_res = []
for t in results:
bboxes = t['bbox'][0]
lengths = t['bbox'][1][0]
if bboxes.shape == (1, 1) or bboxes is None:
continue
k = 0
for i in range(len(lengths)):
num = lengths[i]
for j in range(num):
dt = bboxes[k]
clsid, score, xmin, ymin, xmax, ymax = dt.tolist()
catid = (clsid2catid[int(clsid)])
if is_bbox_normalized:
xmin, ymin, xmax, ymax = \
clip_bbox([xmin, ymin, xmax, ymax])
w = xmax - xmin
h = ymax - ymin
im_shape = t['im_shape'][0][i].tolist()
im_height, im_width = int(im_shape[0]), int(im_shape[1])
xmin *= im_width
ymin *= im_height
w *= im_width
h *= im_height
else:
w = xmax - xmin + 1
h = ymax - ymin + 1
bbox = [xmin, ymin, w, h]
coco_res = {'category_id': catid, 'bbox': bbox, 'score': score}
xywh_res.append(coco_res)
k += 1
return xywh_res
def expand_boxes(boxes, scale):
"""
Expand an array of boxes by a given scale.
"""
w_half = (boxes[:, 2] - boxes[:, 0]) * .5
h_half = (boxes[:, 3] - boxes[:, 1]) * .5
x_c = (boxes[:, 2] + boxes[:, 0]) * .5
y_c = (boxes[:, 3] + boxes[:, 1]) * .5
w_half *= scale
h_half *= scale
boxes_exp = np.zeros(boxes.shape)
boxes_exp[:, 0] = x_c - w_half
boxes_exp[:, 2] = x_c + w_half
boxes_exp[:, 1] = y_c - h_half
boxes_exp[:, 3] = y_c + h_half
return boxes_exp
def mask2out(results, clsid2catid, resolution, thresh_binarize=0.5):
import pycocotools.mask as mask_util
scale = (resolution + 2.0) / resolution
segm_res = []
for t in results:
bboxes = t['bbox'][0]
lengths = t['bbox'][1][0]
if bboxes.shape == (1, 1) or bboxes is None:
continue
if len(bboxes.tolist()) == 0:
continue
masks = t['mask'][0]
s = 0
# for each sample
for i in range(len(lengths)):
num = lengths[i]
im_shape = t['im_shape'][i]
bbox = bboxes[s:s + num][:, 2:]
clsid_scores = bboxes[s:s + num][:, 0:2]
mask = masks[s:s + num]
s += num
im_h = int(im_shape[0])
im_w = int(im_shape[1])
expand_bbox = expand_boxes(bbox, scale)
expand_bbox = expand_bbox.astype(np.int32)
padded_mask = np.zeros(
(resolution + 2, resolution + 2), dtype=np.float32)
for j in range(num):
xmin, ymin, xmax, ymax = expand_bbox[j].tolist()
clsid, score = clsid_scores[j].tolist()
clsid = int(clsid)
padded_mask[1:-1, 1:-1] = mask[j, clsid, :, :]
catid = clsid2catid[clsid]
w = xmax - xmin + 1
h = ymax - ymin + 1
w = np.maximum(w, 1)
h = np.maximum(h, 1)
resized_mask = cv2.resize(padded_mask, (w, h))
resized_mask = np.array(
resized_mask > thresh_binarize, dtype=np.uint8)
im_mask = np.zeros((im_h, im_w), dtype=np.uint8)
x0 = min(max(xmin, 0), im_w)
x1 = min(max(xmax + 1, 0), im_w)
y0 = min(max(ymin, 0), im_h)
y1 = min(max(ymax + 1, 0), im_h)
im_mask[y0:y1, x0:x1] = resized_mask[(y0 - ymin):(y1 - ymin), (
x0 - xmin):(x1 - xmin)]
segm = mask_util.encode(
np.array(
im_mask[:, :, np.newaxis], order='F'))[0]
catid = clsid2catid[clsid]
segm['counts'] = segm['counts'].decode('utf8')
coco_res = {
'category_id': catid,
'segmentation': segm,
'score': score
}
segm_res.append(coco_res)
return segm_res
def color_map(num_classes):
color_map = num_classes * [0, 0, 0]
for i in range(0, num_classes):
j = 0
lab = i
while lab:
color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
j += 1
lab >>= 3
color_map = np.array(color_map).reshape(-1, 3)
return color_map
def draw_bbox(image, catid2name, bboxes, threshold, color_list):
"""
draw bbox on image
"""
draw = ImageDraw.Draw(image)
for dt in np.array(bboxes):
catid, bbox, score = dt['category_id'], dt['bbox'], dt['score']
if score < threshold:
continue
xmin, ymin, w, h = bbox
xmax = xmin + w
ymax = ymin + h
color = tuple(color_list[catid])
# draw bbox
draw.line(
[(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin),
(xmin, ymin)],
width=2,
fill=color)
# draw label
text = "{} {:.2f}".format(catid2name[catid], score)
tw, th = draw.textsize(text)
draw.rectangle(
[(xmin + 1, ymin - th), (xmin + tw + 1, ymin)], fill=color)
draw.text((xmin + 1, ymin - th), text, fill=(255, 255, 255))
return image
def draw_mask(image, masks, threshold, color_list, alpha=0.7):
"""
Draw mask on image
"""
mask_color_id = 0
w_ratio = .4
img_array = np.array(image).astype('float32')
for dt in np.array(masks):
segm, score = dt['segmentation'], dt['score']
if score < threshold:
continue
import pycocotools.mask as mask_util
mask = mask_util.decode(segm) * 255
color_mask = color_list[mask_color_id % len(color_list), 0:3]
mask_color_id += 1
for c in range(3):
color_mask[c] = color_mask[c] * (1 - w_ratio) + w_ratio * 255
idx = np.nonzero(mask)
img_array[idx[0], idx[1], :] *= 1.0 - alpha
img_array[idx[0], idx[1], :] += alpha * color_mask
return Image.fromarray(img_array.astype('uint8'))
def get_bbox_result(output, result, conf, clsid2catid):
is_bbox_normalized = True if conf['arch'] in ['SSD', 'Face'] else False
lengths = offset_to_lengths(output.lod())
np_data = np.array(output) if conf[
'use_python_inference'] else output.copy_to_cpu()
result['bbox'] = (np_data, lengths)
result['im_id'] = np.array([[0]])
bbox_results = bbox2out([result], clsid2catid, is_bbox_normalized)
return bbox_results
def get_mask_result(output, result, conf, clsid2catid):
resolution = conf['mask_resolution']
bbox_out, mask_out = output
lengths = offset_to_lengths(bbox_out.lod())
bbox = np.array(bbox_out) if conf[
'use_python_inference'] else bbox_out.copy_to_cpu()
mask = np.array(mask_out) if conf[
'use_python_inference'] else mask_out.copy_to_cpu()
result['bbox'] = (bbox, lengths)
result['mask'] = (mask, lengths)
mask_results = mask2out([result], clsid2catid, conf['mask_resolution'])
return mask_results
def visualize(bbox_results, catid2name, num_classes, mask_results=None):
image = Image.open(FLAGS.infer_img).convert('RGB')
color_list = color_map(num_classes)
image = draw_bbox(image, catid2name, bbox_results, 0.5, color_list)
if mask_results is not None:
image = draw_mask(image, mask_results, 0.5, color_list)
image_path = os.path.split(FLAGS.infer_img)[-1]
if not os.path.exists(FLAGS.output_dir):
os.makedirs(FLAGS.output_dir)
out_path = os.path.join(FLAGS.output_dir, image_path)
image.save(out_path, quality=95)
logger.info('Save visualize result to {}'.format(out_path))
def infer():
logger.info("cpp_infer.py is deprecated since release/0.3. Please use"
"deploy/python for your python deployment")
model_path = FLAGS.model_path
config_path = FLAGS.config_path
res = {}
assert model_path is not None, "Model path: {} does not exist!".format(
model_path)
assert config_path is not None, "Config path: {} does not exist!".format(
config_path)
with open(config_path) as f:
conf = yaml.safe_load(f)
use_trt = not conf['use_python_inference'] and 'trt' in conf['mode']
if use_trt:
logger.warning(
"Due to the limitation of tensorRT, the image shape needs to set in export_model"
)
img_data = Preprocess(FLAGS.infer_img, conf['arch'], conf['Preprocess'])
if conf['arch'] in ['SSD', 'Face']:
img_data, res['im_shape'] = img_data
img_data = [img_data]
if conf['use_python_inference']:
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
infer_prog, feed_var_names, fetch_targets = fluid.io.load_inference_model(
dirname=model_path,
executor=exe,
model_filename='__model__',
params_filename='__params__')
data_dict = {k: v for k, v in zip(feed_var_names, img_data)}
else:
config = create_config(
model_path,
mode=conf['mode'],
min_subgraph_size=conf['min_subgraph_size'])
predict = fluid.core.create_paddle_predictor(config)
input_names = predict.get_input_names()
for ind, d in enumerate(img_data):
input_tensor = predict.get_input_tensor(input_names[ind])
input_tensor.copy_from_cpu(d.copy())
logger.info('warmup...')
for i in range(10):
if conf['use_python_inference']:
outs = exe.run(infer_prog,
feed=data_dict,
fetch_list=fetch_targets,
return_numpy=False)
else:
predict.zero_copy_run()
cnt = 100
logger.info('run benchmark...')
t1 = time.time()
for i in range(cnt):
if conf['use_python_inference']:
outs = exe.run(infer_prog,
feed=data_dict,
fetch_list=fetch_targets,
return_numpy=False)
else:
outs = []
predict.zero_copy_run()
output_names = predict.get_output_names()
for o_name in output_names:
outs.append(predict.get_output_tensor(o_name))
t2 = time.time()
ms = (t2 - t1) * 1000.0 / float(cnt)
print("Inference: {} ms per batch image".format(ms))
clsid2catid, catid2name = get_category_info(conf['with_background'],
conf['label_list'])
bbox_result = get_bbox_result(outs[0], res, conf, clsid2catid)
mask_result = None
if 'mask_resolution' in conf:
res['im_shape'] = img_data[-1]
mask_result = get_mask_result(outs, res, conf, clsid2catid)
if FLAGS.visualize:
visualize(bbox_result, catid2name, len(conf['label_list']), mask_result)
if FLAGS.dump_result:
import json
bbox_file = os.path.join(FLAGS.output_dir, 'bbox.json')
logger.info('dump bbox to {}'.format(bbox_file))
with open(bbox_file, 'w') as f:
json.dump(bbox_result, f)
if mask_result is not None:
mask_file = os.path.join(FLAGS.output_dir, 'mask.json')
logger.info('dump mask to {}'.format(mask_file))
with open(mask_file, 'w') as f:
json.dump(mask_result, f)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--model_path", type=str, default=None, help="model path.")
parser.add_argument(
"--config_path", type=str, default=None, help="preprocess config path.")
parser.add_argument(
"--infer_img", type=str, default=None, help="Image path")
parser.add_argument(
"--visualize",
action='store_true',
default=False,
help="Whether to visualize detection output")
parser.add_argument(
"--output_dir",
type=str,
default="output",
help="Directory for storing the output visualization files.")
parser.add_argument(
"--dump_result",
action='store_true',
default=False,
help="Whether to dump result")
FLAGS = parser.parse_args()
infer()
ppdet/data/tools/x2coco.py → tools/x2coco.py
浏览文件 @ 56cbc020
......@@ -21,6 +21,9 @@ import os
import os.path as osp
import sys
import shutil
import xml.etree.ElementTree as ET
from tqdm import tqdm
import re
import numpy as np
import PIL.ImageDraw
......@@ -154,17 +157,19 @@ def deal_json(ds_type, img_path, json_path):
categories_list.append(categories(label, labels_list))
labels_list.append(label)
label_to_num[label] = len(labels_list)
points = shapes['points']
p_type = shapes['shape_type']
if p_type == 'polygon':
points = shapes['points']
annotations_list.append(
annotations_polygon(data['imageHeight'], data[
'imageWidth'], points, label, image_num,
object_num, label_to_num))
if p_type == 'rectangle':
points.append([points[0][0], points[1][1]])
points.append([points[1][0], points[0][1]])
(x1, y1), (x2, y2) = shapes['points']
x1, x2 = sorted([x1, x2])
y1, y2 = sorted([y1, y2])
points = [[x1, y1], [x2, y2], [x1, y2], [x2, y1]]
annotations_list.append(
annotations_rectangle(points, label, image_num,
object_num, label_to_num))
......@@ -187,6 +192,100 @@ def deal_json(ds_type, img_path, json_path):
return data_coco
def voc_get_label_anno(ann_dir_path, ann_ids_path, labels_path):
with open(labels_path, 'r') as f:
labels_str = f.read().split()
labels_ids = list(range(1, len(labels_str) + 1))
with open(ann_ids_path, 'r') as f:
ann_ids = f.read().split()
ann_paths = []
for aid in ann_ids:
if aid.endswith('xml'):
ann_path = os.path.join(ann_dir_path, aid)
else:
ann_path = os.path.join(ann_dir_path, aid + '.xml')
ann_paths.append(ann_path)
return dict(zip(labels_str, labels_ids)), ann_paths
def voc_get_image_info(annotation_root, im_id):
filename = annotation_root.findtext('filename')
assert filename is not None
img_name = os.path.basename(filename)
size = annotation_root.find('size')
width = int(size.findtext('width'))
height = int(size.findtext('height'))
image_info = {
'file_name': filename,
'height': height,
'width': width,
'id': im_id
}
return image_info
def voc_get_coco_annotation(obj, label2id):
label = obj.findtext('name')
assert label in label2id, "label is not in label2id."
category_id = label2id[label]
bndbox = obj.find('bndbox')
xmin = int(bndbox.findtext('xmin')) - 1
ymin = int(bndbox.findtext('ymin')) - 1
xmax = int(bndbox.findtext('xmax'))
ymax = int(bndbox.findtext('ymax'))
assert xmax > xmin and ymax > ymin, "Box size error."
o_width = xmax - xmin
o_height = ymax - ymin
anno = {
'area': o_width * o_height,
'iscrowd': 0,
'bbox': [xmin, ymin, o_width, o_height],
'category_id': category_id,
'ignore': 0,
'segmentation': [] # This script is not for segmentation
}
return anno
def voc_xmls_to_cocojson(annotation_paths, label2id, output_dir, output_file):
output_json_dict = {
"images": [],
"type": "instances",
"annotations": [],
"categories": []
}
bnd_id = 1 # bounding box start id
im_id = 0
print('Start converting !')
for a_path in tqdm(annotation_paths):
# Read annotation xml
ann_tree = ET.parse(a_path)
ann_root = ann_tree.getroot()
img_info = voc_get_image_info(ann_root, im_id)
im_id += 1
img_id = img_info['id']
output_json_dict['images'].append(img_info)
for obj in ann_root.findall('object'):
ann = voc_get_coco_annotation(obj=obj, label2id=label2id)
ann.update({'image_id': img_id, 'id': bnd_id})
output_json_dict['annotations'].append(ann)
bnd_id = bnd_id + 1
for label, label_id in label2id.items():
category_info = {'supercategory': 'none', 'id': label_id, 'name': label}
output_json_dict['categories'].append(category_info)
output_file = os.path.join(output_dir, output_file)
with open(output_file, 'w') as f:
output_json = json.dumps(output_json_dict)
f.write(output_json)
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
......@@ -194,7 +293,7 @@ def main():
parser.add_argument('--json_input_dir', help='input annotated directory')
parser.add_argument('--image_input_dir', help='image directory')
parser.add_argument(
'--output_dir', help='output dataset directory', default='../../../')
'--output_dir', help='output dataset directory', default='./')
parser.add_argument(
'--train_proportion',
help='the proportion of train dataset',
......@@ -210,96 +309,137 @@ def main():
help='the proportion of test dataset',
type=float,
default=0.0)
parser.add_argument(
'--voc_anno_dir',
help='In Voc format dataset, path to annotation files directory.',
type=str,
default=None)
parser.add_argument(
'--voc_anno_list',
help='In Voc format dataset, path to annotation files ids list.',
type=str,
default=None)
parser.add_argument(
'--voc_label_list',
help='In Voc format dataset, path to label list. The content of each line is a category.',
type=str,
default=None)
parser.add_argument(
'--voc_out_name',
type=str,
default='voc.json',
help='In Voc format dataset, path to output json file')
args = parser.parse_args()
try:
assert args.dataset_type in ['labelme', 'cityscape']
except AssertionError as e:
print('Now only support the cityscape dataset and labelme dataset!!')
os._exit(0)
try:
assert os.path.exists(args.json_input_dir)
except AssertionError as e:
print('The json folder does not exist!')
os._exit(0)
try:
assert os.path.exists(args.image_input_dir)
except AssertionError as e:
print('The image folder does not exist!')
os._exit(0)
try:
assert abs(args.train_proportion + args.val_proportion \
+ args.test_proportion - 1.0) < 1e-5
assert args.dataset_type in ['voc', 'labelme', 'cityscape']
except AssertionError as e:
print(
'The sum of pqoportion of training, validation and test datase must be 1!'
)
'Now only support the voc, cityscape dataset and labelme dataset!!')
os._exit(0)
# Allocate the dataset.
total_num = len(glob.glob(osp.join(args.json_input_dir, '*.json')))
if args.train_proportion != 0:
train_num = int(total_num * args.train_proportion)
os.makedirs(args.output_dir + '/train')
if args.dataset_type == 'voc':
assert args.voc_anno_dir and args.voc_anno_list and args.voc_label_list
label2id, ann_paths = voc_get_label_anno(
args.voc_anno_dir, args.voc_anno_list, args.voc_label_list)
voc_xmls_to_cocojson(
annotation_paths=ann_paths,
label2id=label2id,
output_dir=args.output_dir,
output_file=args.voc_out_name)
else:
train_num = 0
if args.val_proportion == 0.0:
val_num = 0
test_num = total_num - train_num
if args.test_proportion != 0.0:
os.makedirs(args.output_dir + '/test')
else:
val_num = int(total_num * args.val_proportion)
test_num = total_num - train_num - val_num
os.makedirs(args.output_dir + '/val')
if args.test_proportion != 0.0:
os.makedirs(args.output_dir + '/test')
count = 1
for img_name in os.listdir(args.image_input_dir):
if count <= train_num:
if osp.exists(args.output_dir + '/train/'):
shutil.copyfile(
osp.join(args.image_input_dir, img_name),
osp.join(args.output_dir + '/train/', img_name))
try:
assert os.path.exists(args.json_input_dir)
except AssertionError as e:
print('The json folder does not exist!')
os._exit(0)
try:
assert os.path.exists(args.image_input_dir)
except AssertionError as e:
print('The image folder does not exist!')
os._exit(0)
try:
assert abs(args.train_proportion + args.val_proportion \
+ args.test_proportion - 1.0) < 1e-5
except AssertionError as e:
print(
'The sum of pqoportion of training, validation and test datase must be 1!'
)
os._exit(0)
# Allocate the dataset.
total_num = len(glob.glob(osp.join(args.json_input_dir, '*.json')))
if args.train_proportion != 0:
train_num = int(total_num * args.train_proportion)
os.makedirs(args.output_dir + '/train')
else:
if count <= train_num + val_num:
if osp.exists(args.output_dir + '/val/'):
train_num = 0
if args.val_proportion == 0.0:
val_num = 0
test_num = total_num - train_num
if args.test_proportion != 0.0:
os.makedirs(args.output_dir + '/test')
else:
val_num = int(total_num * args.val_proportion)
test_num = total_num - train_num - val_num
os.makedirs(args.output_dir + '/val')
if args.test_proportion != 0.0:
os.makedirs(args.output_dir + '/test')
count = 1
for img_name in os.listdir(args.image_input_dir):
if count <= train_num:
if osp.exists(args.output_dir + '/train/'):
shutil.copyfile(
osp.join(args.image_input_dir, img_name),
osp.join(args.output_dir + '/val/', img_name))
osp.join(args.output_dir + '/train/', img_name))
else:
if osp.exists(args.output_dir + '/test/'):
shutil.copyfile(
osp.join(args.image_input_dir, img_name),
osp.join(args.output_dir + '/test/', img_name))
count = count + 1
# Deal with the json files.
if not os.path.exists(args.output_dir + '/annotations'):
os.makedirs(args.output_dir + '/annotations')
if args.train_proportion != 0:
train_data_coco = deal_json(
args.dataset_type, args.output_dir + '/train', args.json_input_dir)
train_json_path = osp.join(args.output_dir + '/annotations',
'instance_train.json')
json.dump(
train_data_coco,
open(train_json_path, 'w'),
indent=4,
cls=MyEncoder)
if args.val_proportion != 0:
val_data_coco = deal_json(args.dataset_type, args.output_dir + '/val',
args.json_input_dir)
val_json_path = osp.join(args.output_dir + '/annotations',
'instance_val.json')
json.dump(
val_data_coco, open(val_json_path, 'w'), indent=4, cls=MyEncoder)
if args.test_proportion != 0:
test_data_coco = deal_json(args.dataset_type, args.output_dir + '/test',
args.json_input_dir)
test_json_path = osp.join(args.output_dir + '/annotations',
'instance_test.json')
json.dump(
test_data_coco, open(test_json_path, 'w'), indent=4, cls=MyEncoder)
if count <= train_num + val_num:
if osp.exists(args.output_dir + '/val/'):
shutil.copyfile(
osp.join(args.image_input_dir, img_name),
osp.join(args.output_dir + '/val/', img_name))
else:
if osp.exists(args.output_dir + '/test/'):
shutil.copyfile(
osp.join(args.image_input_dir, img_name),
osp.join(args.output_dir + '/test/', img_name))
count = count + 1
# Deal with the json files.
if not os.path.exists(args.output_dir + '/annotations'):
os.makedirs(args.output_dir + '/annotations')
if args.train_proportion != 0:
train_data_coco = deal_json(args.dataset_type,
args.output_dir + '/train',
args.json_input_dir)
train_json_path = osp.join(args.output_dir + '/annotations',
'instance_train.json')
json.dump(
train_data_coco,
open(train_json_path, 'w'),
indent=4,
cls=MyEncoder)
if args.val_proportion != 0:
val_data_coco = deal_json(args.dataset_type,
args.output_dir + '/val',
args.json_input_dir)
val_json_path = osp.join(args.output_dir + '/annotations',
'instance_val.json')
json.dump(
val_data_coco,
open(val_json_path, 'w'),
indent=4,
cls=MyEncoder)
if args.test_proportion != 0:
test_data_coco = deal_json(args.dataset_type,
args.output_dir + '/test',
args.json_input_dir)
test_json_path = osp.join(args.output_dir + '/annotations',
'instance_test.json')
json.dump(
test_data_coco,
open(test_json_path, 'w'),
indent=4,
cls=MyEncoder)
if __name__ == '__main__':
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或