update pyramidbox_lite_server_mask (#2180)

modules/image/face_detection/pyramidbox_lite_server_mask/README.md

@@ -179,6 +179,11 @@
     # 打印预测结果
     print(r.json()["results"])
     ```
+
+- ### gradio app 支持
+
+  从 PaddleHub 2.3.1 开始支持使用链接 http://127.0.0.1:8866/gradio/pyramidbox_lite_server_mask 在浏览器中访问 pyramidbox_lite_server_mask 的 Gradio APP。
+
 ## 五、Paddle Lite部署
 - ### 通过python执行以下代码，保存模型
   - ```python
@@ -209,6 +214,10 @@
 
   修复无法导出推理模型的问题
 
+* 1.5.0
+
+  添加 Gradio APP 的支持
+
   - ```shell
-    $ hub install pyramidbox_lite_server_mask==1.4.0
+    $ hub install pyramidbox_lite_server_mask==1.5.0
     ```

modules/image/face_detection/pyramidbox_lite_server_mask/README_en.md

@@ -155,6 +155,10 @@
     # print prediction results
     print(r.json()["results"])
     ```
+
+- ### Gradio APP support
+  Starting with PaddleHub 2.3.1, the Gradio APP for pyramidbox_lite_server_mask is supported to be accessed in the browser using the link http://127.0.0.1:8866/gradio/pyramidbox_lite_server_mask.
+
 ## V.Paddle Lite Deployment
 - ### Save model demo
   - ```python
@@ -187,6 +191,10 @@
 
   Fix a bug of save_inference_model
 
+* 1.5.0
+
+  Add Gradio APP support.
+
   - ```shell
-    $ hub install pyramidbox_lite_server_mask==1.4.0
+    $ hub install pyramidbox_lite_server_mask==1.5.0
     ```

modules/image/face_detection/pyramidbox_lite_server_mask/data_feed.py

 # coding=utf-8
-import os
 import math
+import os
 import time
 from collections import OrderedDict
 
@@ -43,8 +43,7 @@ def bbox_vote(det):
         det_accu[:, 0:4] = det_accu[:, 0:4] * np.tile(det_accu[:, -1:], (1, 4))
         max_score = np.max(det_accu[:, 4])
         det_accu_sum = np.zeros((1, 5))
-        det_accu_sum[:, 0:4] = np.sum(
-            det_accu[:, 0:4], axis=0) / np.sum(det_accu[:, -1:])
+        det_accu_sum[:, 0:4] = np.sum(det_accu[:, 0:4], axis=0) / np.sum(det_accu[:, -1:])
         det_accu_sum[:, 4] = max_score
         try:
             dets = np.row_stack((dets, det_accu_sum))
@@ -54,38 +53,26 @@ def bbox_vote(det):
     return dets
 
 
-def crop(image,
-         pts,
-         shift=0,
-         scale=1.5,
-         rotate=0,
-         res_width=128,
-         res_height=128):
+def crop(image, pts, shift=0, scale=1.5, rotate=0, res_width=128, res_height=128):
     res = (res_width, res_height)
     idx1 = 0
     idx2 = 1
     # angle
     alpha = 0
-    if pts[idx2, 0] != -1 and pts[idx2, 1] != -1 and pts[idx1, 0] != -1 and pts[
-            idx1, 1] != -1:
-        alpha = math.atan2(pts[idx2, 1] - pts[idx1, 1],
-                           pts[idx2, 0] - pts[idx1, 0]) * 180 / math.pi
+    if pts[idx2, 0] != -1 and pts[idx2, 1] != -1 and pts[idx1, 0] != -1 and pts[idx1, 1] != -1:
+        alpha = math.atan2(pts[idx2, 1] - pts[idx1, 1], pts[idx2, 0] - pts[idx1, 0]) * 180 / math.pi
     pts[pts == -1] = np.inf
     coord_min = np.min(pts, 0)
     pts[pts == np.inf] = -1
     coord_max = np.max(pts, 0)
     # coordinates of center point
-    c = np.array([
-        coord_max[0] - (coord_max[0] - coord_min[0]) / 2,
-        coord_max[1] - (coord_max[1] - coord_min[1]) / 2
-    ])  # center
-    max_wh = max((coord_max[0] - coord_min[0]) / 2,
-                 (coord_max[1] - coord_min[1]) / 2)
+    c = np.array([coord_max[0] - (coord_max[0] - coord_min[0]) / 2,
+                  coord_max[1] - (coord_max[1] - coord_min[1]) / 2])  # center
+    max_wh = max((coord_max[0] - coord_min[0]) / 2, (coord_max[1] - coord_min[1]) / 2)
     # Shift the center point, rot add eyes angle
     c = c + shift * max_wh
     rotate = rotate + alpha
-    M = cv2.getRotationMatrix2D((c[0], c[1]), rotate,
-                                res[0] / (2 * max_wh * scale))
+    M = cv2.getRotationMatrix2D((c[0], c[1]), rotate, res[0] / (2 * max_wh * scale))
     M[0, 2] = M[0, 2] - (c[0] - res[0] / 2.0)
     M[1, 2] = M[1, 2] - (c[1] - res[0] / 2.0)
     image_out = cv2.warpAffine(image, M, res)
@@ -97,27 +84,24 @@ def color_normalize(image, mean, std=None):
         image = np.repeat(image, axis=2)
     h, w, c = image.shape
     image = np.transpose(image, (2, 0, 1))
-    image = np.subtract(image.reshape(c, -1), mean[:, np.newaxis]).reshape(
-        -1, h, w)
+    image = np.subtract(image.reshape(c, -1), mean[:, np.newaxis]).reshape(-1, h, w)
     image = np.transpose(image, (1, 2, 0))
     return image
 
 
 def process_image(org_im, face):
     pts = np.array([
-        face['left'], face['top'], face['right'], face['top'], face['left'],
-        face['bottom'], face['right'], face['bottom']
+        face['left'], face['top'], face['right'], face['top'], face['left'], face['bottom'], face['right'],
+        face['bottom']
     ]).reshape(4, 2).astype(np.float32)
     image_in, M = crop(org_im, pts)
     image_in = image_in / 256.0
     image_in = color_normalize(image_in, mean=np.array([0.5, 0.5, 0.5]))
-    image_in = image_in.astype(np.float32).transpose([2, 0, 1]).reshape(
-        -1, 3, 128, 128)
+    image_in = image_in.astype(np.float32).transpose([2, 0, 1]).reshape(-1, 3, 128, 128)
     return image_in
 
 
-def reader(face_detector, shrink, confs_threshold, images, paths, use_gpu,
-           use_multi_scale):
+def reader(face_detector, shrink, confs_threshold, images, paths, use_gpu, use_multi_scale):
     """
     Preprocess to yield image.
 
@@ -142,8 +126,7 @@ def reader(face_detector, shrink, confs_threshold, images, paths, use_gpu,
         assert type(paths) is list, "paths should be a list."
         for im_path in paths:
             each = OrderedDict()
-            assert os.path.isfile(
-                im_path), "The {} isn't a valid file path.".format(im_path)
+            assert os.path.isfile(im_path), "The {} isn't a valid file path.".format(im_path)
             im = cv2.imread(im_path)
             each['org_im'] = im
             each['org_im_path'] = im_path
@@ -153,8 +136,7 @@ def reader(face_detector, shrink, confs_threshold, images, paths, use_gpu,
         for im in images:
             each = OrderedDict()
             each['org_im'] = im
-            each['org_im_path'] = 'ndarray_time={}'.format(
-                round(time.time(), 6) * 1e6)
+            each['org_im_path'] = 'ndarray_time={}'.format(round(time.time(), 6) * 1e6)
             component.append(each)
 
     for element in component:
@@ -162,19 +144,15 @@ def reader(face_detector, shrink, confs_threshold, images, paths, use_gpu,
             scale_res = list()
             detect_faces = list()
             for scale in multi_scales:
-                _detect_res = face_detector.face_detection(
-                    images=[element['org_im']],
-                    use_gpu=use_gpu,
-                    visualization=False,
-                    shrink=scale,
-                    confs_threshold=confs_threshold)
+                _detect_res = face_detector.face_detection(images=[element['org_im']],
+                                                           use_gpu=use_gpu,
+                                                           visualization=False,
+                                                           shrink=scale,
+                                                           confs_threshold=confs_threshold)
 
                 _s = list()
                 for _face in _detect_res[0]['data']:
-                    _face_list = [
-                        _face['left'], _face['top'], _face['right'],
-                        _face['bottom'], _face['confidence']
-                    ]
+                    _face_list = [_face['left'], _face['top'], _face['right'], _face['bottom'], _face['confidence']]
                     _s.append(_face_list)
 
                 if _s:
@@ -185,23 +163,16 @@ def reader(face_detector, shrink, confs_threshold, images, paths, use_gpu,
                 keep_index = np.where(scale_res[:, 4] >= confs_threshold)[0]
                 scale_res = scale_res[keep_index, :]
                 for data in scale_res:
-                    face = {
-                        'left': data[0],
-                        'top': data[1],
-                        'right': data[2],
-                        'bottom': data[3],
-                        'confidence': data[4]
-                    }
+                    face = {'left': data[0], 'top': data[1], 'right': data[2], 'bottom': data[3], 'confidence': data[4]}
                     detect_faces.append(face)
             else:
                 detect_faces = []
         else:
-            _detect_res = face_detector.face_detection(
-                images=[element['org_im']],
-                use_gpu=use_gpu,
-                visualization=False,
-                shrink=shrink,
-                confs_threshold=confs_threshold)
+            _detect_res = face_detector.face_detection(images=[element['org_im']],
+                                                       use_gpu=use_gpu,
+                                                       visualization=False,
+                                                       shrink=shrink,
+                                                       confs_threshold=confs_threshold)
             detect_faces = _detect_res[0]['data']
 
         element['preprocessed'] = list()

modules/image/face_detection/pyramidbox_lite_server_mask/module.py

@@ -10,11 +10,11 @@ import numpy as np
 import paddle
 from paddle.inference import Config
 from paddle.inference import create_predictor
+
+import paddlehub as hub
 from .data_feed import reader
 from .processor import base64_to_cv2
 from .processor import postprocess
-
-import paddlehub as hub
 from paddlehub.module.module import moduleinfo
 from paddlehub.module.module import runnable
 from paddlehub.module.module import serving
@@ -29,6 +29,7 @@ from paddlehub.module.module import serving
     "PyramidBox-Lite-Server-Mask is a high-performance face detection model used to detect whether people wear masks.",
     version="1.4.0")
 class PyramidBoxLiteServerMask:
+
     def __init__(self, face_detector_module=None):
         """
         Args:
@@ -46,8 +47,8 @@ class PyramidBoxLiteServerMask:
         """
         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()
@@ -243,3 +244,28 @@ class PyramidBoxLiteServerMask:
                                           type=ast.literal_eval,
                                           default=0.6,
                                           help="confidence threshold.")
+
+    def create_gradio_app(self):
+        import gradio as gr
+        import tempfile
+        import os
+        from PIL import Image
+
+        def inference(image, shrink, confs_threshold):
+            with tempfile.TemporaryDirectory() as temp_dir:
+                self.face_detection(paths=[image],
+                                    use_gpu=False,
+                                    visualization=True,
+                                    output_dir=temp_dir,
+                                    shrink=shrink,
+                                    confs_threshold=confs_threshold)
+                return Image.open(os.path.join(temp_dir, os.listdir(temp_dir)[0]))
+
+        interface = gr.Interface(inference, [
+            gr.inputs.Image(type="filepath"),
+            gr.Slider(0.0, 1.0, 0.5, step=0.01),
+            gr.Slider(0.0, 1.0, 0.6, step=0.01)
+        ],
+                                 gr.outputs.Image(type="ndarray"),
+                                 title='pyramidbox_lite_server_mask')
+        return interface

modules/image/face_detection/pyramidbox_lite_server_mask/processor.py

@@ -3,13 +3,14 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import base64
 import os
 import time
 
-import base64
 import cv2
 import numpy as np
-from PIL import Image, ImageDraw
+from PIL import Image
+from PIL import ImageDraw
 
 __all__ = ['base64_to_cv2', 'postprocess']
 
@@ -56,8 +57,7 @@ def get_save_image_name(org_im, org_im_path, output_dir):
     # save image path
     save_im_path = os.path.join(output_dir, im_prefix + ext)
     if os.path.exists(save_im_path):
-        save_im_path = os.path.join(
-            output_dir, im_prefix + 'time={}'.format(int(time.time())) + ext)
+        save_im_path = os.path.join(output_dir, im_prefix + 'time={}'.format(int(time.time())) + ext)
 
     return save_im_path
 
@@ -68,19 +68,13 @@ def draw_bounding_box_on_image(save_im_path, output_data):
     for bbox in output_data:
         # draw bouding box
         if bbox['label'] == "MASK":
-            draw.line([(bbox['left'], bbox['top']),
-                       (bbox['left'], bbox['bottom']),
-                       (bbox['right'], bbox['bottom']),
-                       (bbox['right'], bbox['top']),
-                       (bbox['left'], bbox['top'])],
+            draw.line([(bbox['left'], bbox['top']), (bbox['left'], bbox['bottom']), (bbox['right'], bbox['bottom']),
+                       (bbox['right'], bbox['top']), (bbox['left'], bbox['top'])],
                       width=2,
                       fill='green')
         else:
-            draw.line([(bbox['left'], bbox['top']),
-                       (bbox['left'], bbox['bottom']),
-                       (bbox['right'], bbox['bottom']),
-                       (bbox['right'], bbox['top']),
-                       (bbox['left'], bbox['top'])],
+            draw.line([(bbox['left'], bbox['top']), (bbox['left'], bbox['bottom']), (bbox['right'], bbox['bottom']),
+                       (bbox['right'], bbox['top']), (bbox['left'], bbox['top'])],
                       width=2,
                       fill='red')
         # draw label
@@ -93,17 +87,14 @@ def draw_bounding_box_on_image(save_im_path, output_data):
             box_fill = (255)
             text_fill = (0)
 
-        draw.rectangle(
-            xy=(bbox['left'], bbox['top'] - (textsize_height + 5),
-                bbox['left'] + textsize_width + 10, bbox['top'] - 3),
-            fill=box_fill)
-        draw.text(
-            xy=(bbox['left'], bbox['top'] - 15), text=text, fill=text_fill)
+        draw.rectangle(xy=(bbox['left'], bbox['top'] - (textsize_height + 5), bbox['left'] + textsize_width + 10,
+                           bbox['top'] - 3),
+                       fill=box_fill)
+        draw.text(xy=(bbox['left'], bbox['top'] - 15), text=text, fill=text_fill)
     image.save(save_im_path)
 
 
-def postprocess(confidence_out, org_im, org_im_path, detected_faces, output_dir,
-                visualization):
+def postprocess(confidence_out, org_im, org_im_path, detected_faces, output_dir, visualization):
     """
     Postprocess output of network. one element at a time.
 

modules/image/face_detection/pyramidbox_lite_server_mask/test.py

@@ -4,13 +4,14 @@ import unittest
 
 import cv2
 import requests
-import paddlehub as hub
 
+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/iFgRcqHznqg/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MXx8ZmFjZXxlbnwwfHx8fDE2NjE5ODAyMTc&force=true&w=640'
@@ -29,11 +30,7 @@ class TestHubModule(unittest.TestCase):
         shutil.rmtree('detection_result')
 
     def test_face_detection1(self):
-        results = self.module.face_detection(
-            paths=['tests/test.jpg'],
-            use_gpu=False,
-            visualization=False
-        )
+        results = self.module.face_detection(paths=['tests/test.jpg'], use_gpu=False, visualization=False)
         bbox = results[0]['data'][0]
 
         label = bbox['label']
@@ -42,7 +39,7 @@ class TestHubModule(unittest.TestCase):
         right = bbox['right']
         top = bbox['top']
         bottom = bbox['bottom']
-        
+
         self.assertEqual(label, 'NO MASK')
         self.assertTrue(confidence > 0.5)
         self.assertTrue(0 < left < 2000)
@@ -51,11 +48,7 @@ class TestHubModule(unittest.TestCase):
         self.assertTrue(0 < bottom < 2000)
 
     def test_face_detection2(self):
-        results = self.module.face_detection(
-            images=[cv2.imread('tests/test.jpg')],
-            use_gpu=False,
-            visualization=False
-        )
+        results = self.module.face_detection(images=[cv2.imread('tests/test.jpg')], use_gpu=False, visualization=False)
         bbox = results[0]['data'][0]
 
         label = bbox['label']
@@ -64,7 +57,7 @@ class TestHubModule(unittest.TestCase):
         right = bbox['right']
         top = bbox['top']
         bottom = bbox['bottom']
-        
+
         self.assertEqual(label, 'NO MASK')
         self.assertTrue(confidence > 0.5)
         self.assertTrue(0 < left < 2000)
@@ -73,11 +66,7 @@ class TestHubModule(unittest.TestCase):
         self.assertTrue(0 < bottom < 2000)
 
     def test_face_detection3(self):
-        results = self.module.face_detection(
-            images=[cv2.imread('tests/test.jpg')],
-            use_gpu=False,
-            visualization=True
-        )
+        results = self.module.face_detection(images=[cv2.imread('tests/test.jpg')], use_gpu=False, visualization=True)
         bbox = results[0]['data'][0]
 
         label = bbox['label']
@@ -86,7 +75,7 @@ class TestHubModule(unittest.TestCase):
         right = bbox['right']
         top = bbox['top']
         bottom = bbox['bottom']
-        
+
         self.assertEqual(label, 'NO MASK')
         self.assertTrue(confidence > 0.5)
         self.assertTrue(0 < left < 2000)
@@ -95,11 +84,7 @@ class TestHubModule(unittest.TestCase):
         self.assertTrue(0 < bottom < 2000)
 
     def test_face_detection4(self):
-        results = self.module.face_detection(
-            images=[cv2.imread('tests/test.jpg')],
-            use_gpu=True,
-            visualization=False
-        )
+        results = self.module.face_detection(images=[cv2.imread('tests/test.jpg')], use_gpu=True, visualization=False)
         bbox = results[0]['data'][0]
 
         label = bbox['label']
@@ -108,7 +93,7 @@ class TestHubModule(unittest.TestCase):
         right = bbox['right']
         top = bbox['top']
         bottom = bbox['bottom']
-        
+
         self.assertEqual(label, 'NO MASK')
         self.assertTrue(confidence > 0.5)
         self.assertTrue(0 < left < 2000)
@@ -117,18 +102,10 @@ class TestHubModule(unittest.TestCase):
         self.assertTrue(0 < bottom < 2000)
 
     def test_face_detection5(self):
-        self.assertRaises(
-            AssertionError,
-            self.module.face_detection,
-            paths=['no.jpg']
-        )
+        self.assertRaises(AssertionError, self.module.face_detection, paths=['no.jpg'])
 
     def test_face_detection6(self):
-        self.assertRaises(
-            AttributeError,
-            self.module.face_detection,
-            images=['test.jpg']
-        )
+        self.assertRaises(AttributeError, self.module.face_detection, images=['test.jpg'])
 
     def test_save_inference_model(self):
         self.module.save_inference_model('./inference/model')