@@ -39,7 +39,7 @@
- ```shell
$ hub install humanseg_mobile
```
-
+
- In case of any problems during installation, please refer to:[Windows_Quickstart](../../../../docs/docs_en/get_start/windows_quickstart.md)
| [Linux_Quickstart](../../../../docs/docs_en/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_en/get_start/mac_quickstart.md)
@@ -49,11 +49,11 @@
- ```
hub run humanseg_mobile --input_path "/PATH/TO/IMAGE"
-
+
```
- If you want to call the Hub module through the command line, please refer to: [PaddleHub Command Line Instruction](../../../../docs/docs_en/tutorial/cmd_usage.rst)
-
+
- ### 2、Prediction Code Example
- Image segmentation and video segmentation example：
```python
@@ -122,9 +122,9 @@
- **Return**
- * res (list\[dict\]): The list of recognition results, where each element is dict and each field is:
+ * res (list\[dict\]): The list of recognition results, where each element is dict and each field is:
* save\_path (str, optional): Save path of the result.
- * data (numpy.ndarray): The result of portrait segmentation.
+ * data (numpy.ndarray): The result of portrait segmentation.
```python
def video_stream_segment(self,
@@ -231,6 +231,8 @@
cv2.imwrite("segment_human_mobile.png", rgba)
```
+- ### Gradio APP support
+ Starting with PaddleHub 2.3.1, the Gradio APP for humanseg_mobile is supported to be accessed in the browser using the link http://127.0.0.1:8866/gradio/humanseg_mobile.
## V. Release Note
@@ -239,7 +241,7 @@
First release
- 1.1.0
-
+
Added video portrait split interface
Added video stream portrait segmentation interface
@@ -252,7 +254,10 @@
Remove Fluid API
+* 1.3.0
+
+ Add Gradio APP support.
+
```shell
- $ hub install humanseg_mobile == 1.2.0
+ $ hub install humanseg_mobile == 1.3.0
```
-
diff --git a/modules/image/semantic_segmentation/humanseg_mobile/module.py b/modules/image/semantic_segmentation/humanseg_mobile/module.py
index 413386a4581c2f4f1bd99111ff2add10ad2016fe..e4f9397960a567d460009e327342dded2863e15b 100644
--- a/modules/image/semantic_segmentation/humanseg_mobile/module.py
+++ b/modules/image/semantic_segmentation/humanseg_mobile/module.py
@@ -12,43 +12,49 @@
# 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 argparse
import ast
import os
import os.path as osp
-import argparse
import cv2
import numpy as np
-import paddle
import paddle.jit
import paddle.static
-from paddle.inference import Config, create_predictor
-from paddlehub.module.module import moduleinfo, runnable, serving
-
-from .processor import postprocess, base64_to_cv2, cv2_to_base64, check_dir
-from .data_feed import reader, preprocess_v
-from .optimal import postprocess_v, threshold_mask
-
-
-@moduleinfo(
- name="humanseg_mobile",
- type="CV/semantic_segmentation",
- author="paddlepaddle",
- author_email="",
- summary="HRNet_w18_samll_v1 is a semantic segmentation model.",
- version="1.2.0")
+from paddle.inference import Config
+from paddle.inference import create_predictor
+
+from .data_feed import preprocess_v
+from .data_feed import reader
+from .optimal import postprocess_v
+from .optimal import threshold_mask
+from .processor import base64_to_cv2
+from .processor import check_dir
+from .processor import cv2_to_base64
+from .processor import postprocess
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.module import runnable
+from paddlehub.module.module import serving
+
+
+@moduleinfo(name="humanseg_mobile",
+ type="CV/semantic_segmentation",
+ author="paddlepaddle",
+ author_email="",
+ summary="HRNet_w18_samll_v1 is a semantic segmentation model.",
+ version="1.3.0")
class HRNetw18samllv1humanseg:
+
def __init__(self):
- self.default_pretrained_model_path = os.path.join(
- self.directory, "humanseg_mobile_inference", "model")
+ self.default_pretrained_model_path = os.path.join(self.directory, "humanseg_mobile_inference", "model")
self._set_config()
def _set_config(self):
"""
predictor config setting
"""
- model = self.default_pretrained_model_path+'.pdmodel'
- params = self.default_pretrained_model_path+'.pdiparams'
+ model = self.default_pretrained_model_path + '.pdmodel'
+ params = self.default_pretrained_model_path + '.pdiparams'
cpu_config = Config(model, params)
cpu_config.disable_glog_info()
cpu_config.disable_gpu()
@@ -63,7 +69,7 @@ class HRNetw18samllv1humanseg:
gpu_config = Config(model, params)
gpu_config.disable_glog_info()
gpu_config.enable_use_gpu(memory_pool_init_size_mb=1000, device_id=0)
-
+
if paddle.get_cudnn_version() == 8004:
gpu_config.delete_pass('conv_elementwise_add_act_fuse_pass')
gpu_config.delete_pass('conv_elementwise_add2_act_fuse_pass')
@@ -131,13 +137,12 @@ class HRNetw18samllv1humanseg:
output = np.expand_dims(output[:, 1, :, :], axis=1)
# postprocess one by one
for i in range(len(batch_data)):
- out = postprocess(
- data_out=output[i],
- org_im=batch_data[i]['org_im'],
- org_im_shape=batch_data[i]['org_im_shape'],
- org_im_path=batch_data[i]['org_im_path'],
- output_dir=output_dir,
- visualization=visualization)
+ out = postprocess(data_out=output[i],
+ org_im=batch_data[i]['org_im'],
+ org_im_shape=batch_data[i]['org_im_shape'],
+ org_im_path=batch_data[i]['org_im_path'],
+ output_dir=output_dir,
+ visualization=visualization)
res.append(out)
return res
@@ -317,23 +322,21 @@ class HRNetw18samllv1humanseg:
"""
Run as a command.
"""
- self.parser = argparse.ArgumentParser(
- description="Run the {} module.".format(self.name),
- prog='hub run {}'.format(self.name),
- usage='%(prog)s',
- add_help=True)
+ self.parser = argparse.ArgumentParser(description="Run the {} module.".format(self.name),
+ prog='hub run {}'.format(self.name),
+ usage='%(prog)s',
+ add_help=True)
self.arg_input_group = self.parser.add_argument_group(title="Input options", description="Input data. Required")
self.arg_config_group = self.parser.add_argument_group(
title="Config options", description="Run configuration for controlling module behavior, not required.")
self.add_module_config_arg()
self.add_module_input_arg()
args = self.parser.parse_args(argvs)
- results = self.segment(
- paths=[args.input_path],
- batch_size=args.batch_size,
- use_gpu=args.use_gpu,
- output_dir=args.output_dir,
- visualization=args.visualization)
+ results = self.segment(paths=[args.input_path],
+ batch_size=args.batch_size,
+ use_gpu=args.use_gpu,
+ output_dir=args.output_dir,
+ visualization=args.visualization)
if args.save_dir is not None:
check_dir(args.save_dir)
self.save_inference_model(args.save_dir)
@@ -344,14 +347,22 @@ class HRNetw18samllv1humanseg:
"""
Add the command config options.
"""
- self.arg_config_group.add_argument(
- '--use_gpu', type=ast.literal_eval, default=False, help="whether use GPU or not")
- self.arg_config_group.add_argument(
- '--output_dir', type=str, default='humanseg_mobile_output', help="The directory to save output images.")
- self.arg_config_group.add_argument(
- '--save_dir', type=str, default='humanseg_mobile_model', help="The directory to save model.")
- self.arg_config_group.add_argument(
- '--visualization', type=ast.literal_eval, default=False, help="whether to save output as images.")
+ self.arg_config_group.add_argument('--use_gpu',
+ type=ast.literal_eval,
+ default=False,
+ help="whether use GPU or not")
+ self.arg_config_group.add_argument('--output_dir',
+ type=str,
+ default='humanseg_mobile_output',
+ help="The directory to save output images.")
+ self.arg_config_group.add_argument('--save_dir',
+ type=str,
+ default='humanseg_mobile_model',
+ help="The directory to save model.")
+ self.arg_config_group.add_argument('--visualization',
+ type=ast.literal_eval,
+ default=False,
+ help="whether to save output as images.")
self.arg_config_group.add_argument('--batch_size', type=ast.literal_eval, default=1, help="batch size.")
def add_module_input_arg(self):
@@ -360,31 +371,20 @@ class HRNetw18samllv1humanseg:
"""
self.arg_input_group.add_argument('--input_path', type=str, help="path to image.")
-
-if __name__ == "__main__":
- m = HRNetw18samllv1humanseg()
- img = cv2.imread('photo.jpg')
- #res = m.segment(images=[img], visualization=True)
- #print(res[0]['data'])
- #m.video_segment('')
- cap_video = cv2.VideoCapture('video_test.mp4')
- fps = cap_video.get(cv2.CAP_PROP_FPS)
- save_path = 'result_frame.avi'
- width = int(cap_video.get(cv2.CAP_PROP_FRAME_WIDTH))
- height = int(cap_video.get(cv2.CAP_PROP_FRAME_HEIGHT))
- cap_out = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), fps, (width, height))
- prev_gray = None
- prev_cfd = None
- while cap_video.isOpened():
- ret, frame_org = cap_video.read()
- if ret:
- [img_matting, prev_gray, prev_cfd] = m.video_stream_segment(
- frame_org=frame_org, frame_id=cap_video.get(1), prev_gray=prev_gray, prev_cfd=prev_cfd)
- img_matting = np.repeat(img_matting[:, :, np.newaxis], 3, axis=2)
- bg_im = np.ones_like(img_matting) * 255
- comb = (img_matting * frame_org + (1 - img_matting) * bg_im).astype(np.uint8)
- cap_out.write(comb)
- else:
- break
- cap_video.release()
- cap_out.release()
+ def create_gradio_app(self):
+ import gradio as gr
+ import tempfile
+ import os
+ from PIL import Image
+
+ def inference(image, use_gpu=False):
+ with tempfile.TemporaryDirectory() as temp_dir:
+ self.segment(paths=[image], use_gpu=use_gpu, visualization=True, output_dir=temp_dir)
+ return Image.open(os.path.join(temp_dir, os.listdir(temp_dir)[0]))
+
+ interface = gr.Interface(
+ inference,
+ [gr.inputs.Image(type="filepath"), gr.Checkbox(label='use_gpu')],
+ gr.outputs.Image(type="ndarray"),
+ title='humanseg_mobile')
+ return interface
diff --git a/modules/image/semantic_segmentation/humanseg_mobile/optimal.py b/modules/image/semantic_segmentation/humanseg_mobile/optimal.py
index fbdc750f2f7fd2e1bd0cc8392544d998bef5d8aa..6271726cf8fa7d8af9011f2a294105f168bfe620 100644
--- a/modules/image/semantic_segmentation/humanseg_mobile/optimal.py
+++ b/modules/image/semantic_segmentation/humanseg_mobile/optimal.py
@@ -1,5 +1,4 @@
# -*- coding:utf-8 -*-
-
import numpy as np
@@ -33,8 +32,8 @@ def human_seg_tracking(pre_gray, cur_gray, prev_cfd, dl_weights, disflow):
# 超出边界不跟踪
not_track = (cur_x < 0) + (cur_x >= w) + (cur_y < 0) + (cur_y >= h)
flow_bw[~not_track] = flow_bw[cur_y[~not_track], cur_x[~not_track]]
- not_track += (
- np.square(flow_fw[:, :, 0] + flow_bw[:, :, 0]) + np.square(flow_fw[:, :, 1] + flow_bw[:, :, 1])) >= check_thres
+ not_track += (np.square(flow_fw[:, :, 0] + flow_bw[:, :, 0]) +
+ np.square(flow_fw[:, :, 1] + flow_bw[:, :, 1])) >= check_thres
track_cfd[cur_y[~not_track], cur_x[~not_track]] = prev_cfd[~not_track]
is_track[cur_y[~not_track], cur_x[~not_track]] = 1
diff --git a/modules/image/semantic_segmentation/humanseg_mobile/processor.py b/modules/image/semantic_segmentation/humanseg_mobile/processor.py
index 6a81997cd3c2fe4b15b72584c72f9cb8e5660f0c..af664695dcafa4273e1e936f2af1a1ba9b3e6abf 100644
--- a/modules/image/semantic_segmentation/humanseg_mobile/processor.py
+++ b/modules/image/semantic_segmentation/humanseg_mobile/processor.py
@@ -1,7 +1,7 @@
# -*- coding:utf-8 -*-
+import base64
import os
import time
-import base64
import cv2
import numpy as np
diff --git a/modules/image/semantic_segmentation/humanseg_mobile/test.py b/modules/image/semantic_segmentation/humanseg_mobile/test.py
index c38977f77bb1d67ba936f25d9546f6dde3569854..3ca8df523db41072de5a0e2e165f0bce640e9a04 100644
--- a/modules/image/semantic_segmentation/humanseg_mobile/test.py
+++ b/modules/image/semantic_segmentation/humanseg_mobile/test.py
@@ -3,15 +3,16 @@ import shutil
import unittest
import cv2
-import requests
import numpy as np
-import paddlehub as hub
+import requests
+import paddlehub as hub
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
class TestHubModule(unittest.TestCase):
+
@classmethod
def setUpClass(cls) -> None:
img_url = 'https://unsplash.com/photos/pg_WCHWSdT8/download?ixid=MnwxMjA3fDB8MXxhbGx8fHx8fHx8fHwxNjYyNDM2ODI4&force=true&w=640'
@@ -23,8 +24,7 @@ class TestHubModule(unittest.TestCase):
f.write(response.content)
fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
img = cv2.imread('tests/test.jpg')
- video = cv2.VideoWriter('tests/test.avi', fourcc,
- 20.0, tuple(img.shape[:2]))
+ video = cv2.VideoWriter('tests/test.avi', fourcc, 20.0, tuple(img.shape[:2]))
for i in range(40):
video.write(img)
video.release()
@@ -38,100 +38,65 @@ class TestHubModule(unittest.TestCase):
shutil.rmtree('humanseg_mobile_video_result')
def test_segment1(self):
- results = self.module.segment(
- paths=['tests/test.jpg'],
- use_gpu=False,
- visualization=False
- )
+ results = self.module.segment(paths=['tests/test.jpg'], use_gpu=False, visualization=False)
self.assertIsInstance(results[0]['data'], np.ndarray)
def test_segment2(self):
- results = self.module.segment(
- images=[cv2.imread('tests/test.jpg')],
- use_gpu=False,
- visualization=False
- )
+ results = self.module.segment(images=[cv2.imread('tests/test.jpg')], use_gpu=False, visualization=False)
self.assertIsInstance(results[0]['data'], np.ndarray)
def test_segment3(self):
- results = self.module.segment(
- images=[cv2.imread('tests/test.jpg')],
- use_gpu=False,
- visualization=True
- )
+ results = self.module.segment(images=[cv2.imread('tests/test.jpg')], use_gpu=False, visualization=True)
self.assertIsInstance(results[0]['data'], np.ndarray)
def test_segment4(self):
- results = self.module.segment(
- images=[cv2.imread('tests/test.jpg')],
- use_gpu=True,
- visualization=False
- )
+ results = self.module.segment(images=[cv2.imread('tests/test.jpg')], use_gpu=True, visualization=False)
self.assertIsInstance(results[0]['data'], np.ndarray)
def test_segment5(self):
- self.assertRaises(
- AssertionError,
- self.module.segment,
- paths=['no.jpg']
- )
+ self.assertRaises(AssertionError, self.module.segment, paths=['no.jpg'])
def test_segment6(self):
- self.assertRaises(
- AttributeError,
- self.module.segment,
- images=['test.jpg']
- )
+ self.assertRaises(AttributeError, self.module.segment, images=['test.jpg'])
def test_video_stream_segment1(self):
- img_matting, cur_gray, optflow_map = self.module.video_stream_segment(
- frame_org=cv2.imread('tests/test.jpg'),
- frame_id=1,
- prev_gray=None,
- prev_cfd=None,
- use_gpu=False
- )
+ img_matting, cur_gray, optflow_map = self.module.video_stream_segment(frame_org=cv2.imread('tests/test.jpg'),
+ frame_id=1,
+ prev_gray=None,
+ prev_cfd=None,
+ use_gpu=False)
self.assertIsInstance(img_matting, np.ndarray)
self.assertIsInstance(cur_gray, np.ndarray)
self.assertIsInstance(optflow_map, np.ndarray)
- img_matting, cur_gray, optflow_map = self.module.video_stream_segment(
- frame_org=cv2.imread('tests/test.jpg'),
- frame_id=2,
- prev_gray=cur_gray,
- prev_cfd=optflow_map,
- use_gpu=False
- )
+ img_matting, cur_gray, optflow_map = self.module.video_stream_segment(frame_org=cv2.imread('tests/test.jpg'),
+ frame_id=2,
+ prev_gray=cur_gray,
+ prev_cfd=optflow_map,
+ use_gpu=False)
self.assertIsInstance(img_matting, np.ndarray)
self.assertIsInstance(cur_gray, np.ndarray)
self.assertIsInstance(optflow_map, np.ndarray)
def test_video_stream_segment2(self):
- img_matting, cur_gray, optflow_map = self.module.video_stream_segment(
- frame_org=cv2.imread('tests/test.jpg'),
- frame_id=1,
- prev_gray=None,
- prev_cfd=None,
- use_gpu=True
- )
+ img_matting, cur_gray, optflow_map = self.module.video_stream_segment(frame_org=cv2.imread('tests/test.jpg'),
+ frame_id=1,
+ prev_gray=None,
+ prev_cfd=None,
+ use_gpu=True)
self.assertIsInstance(img_matting, np.ndarray)
self.assertIsInstance(cur_gray, np.ndarray)
self.assertIsInstance(optflow_map, np.ndarray)
- img_matting, cur_gray, optflow_map = self.module.video_stream_segment(
- frame_org=cv2.imread('tests/test.jpg'),
- frame_id=2,
- prev_gray=cur_gray,
- prev_cfd=optflow_map,
- use_gpu=True
- )
+ img_matting, cur_gray, optflow_map = self.module.video_stream_segment(frame_org=cv2.imread('tests/test.jpg'),
+ frame_id=2,
+ prev_gray=cur_gray,
+ prev_cfd=optflow_map,
+ use_gpu=True)
self.assertIsInstance(img_matting, np.ndarray)
self.assertIsInstance(cur_gray, np.ndarray)
self.assertIsInstance(optflow_map, np.ndarray)
def test_video_segment1(self):
- self.module.video_segment(
- video_path="tests/test.avi",
- use_gpu=False
- )
+ self.module.video_segment(video_path="tests/test.avi", use_gpu=False)
def test_save_inference_model(self):
self.module.save_inference_model('./inference/model')