adapted nlpreader to lac module v2 (#418)

* adapted nlpreader to lac module v2

* fix code style of mask demo

demo/mask_detection/mask_detection.py

@@ -8,6 +8,7 @@ import os
 
 module = hub.Module(name="pyramidbox_lite_server_mask", version='1.1.0')
 
+
 # opencv输出中文
 def paint_chinese(im, chinese, position, fontsize, color_bgr):
     # 图像从OpenCV格式转换成PIL格式
@@ -88,7 +89,8 @@ while True:
             label_cn = "无口罩"
 
         cv2.rectangle(frame_copy, (left, top), (right, bottom), color, 3)
-        cv2.putText(frame_copy, label, (left, top-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 2)
+        cv2.putText(frame_copy, label, (left, top - 10),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 2)
         #origin_point = (left, top - 36)
         #frame_copy = paint_chinese(frame_copy, label_cn, origin_point, 24,
         #                           color)

demo/mask_detection/python/infer.py

@@ -33,30 +33,27 @@ import argparse
 
 def parse_args():
     parser = argparse.ArgumentParser('mask detection.')
-    parser.add_argument('--models_dir',
-                        type=str,
-                        default='',
-                        help='path of models.')
-    parser.add_argument('--img_paths',
-                        type=str,
-                        default='',
-                        help='path of images')
-    parser.add_argument('--video_path',
-                        type=str,
-                        default='',
-                        help='path of video.')
-    parser.add_argument('--use_camera',
-                        type=bool,
-                        default=False,
-                        help='switch detect video or camera, default:video.')
-    parser.add_argument('--open_imshow',
-                        type=bool,
-                        default=False,
-                        help='visualize video detection results in real time.')
-    parser.add_argument('--use_gpu',
-                        type=bool,
-                        default=False,
-                        help='switch cpu/gpu, default:cpu.')
+    parser.add_argument(
+        '--models_dir', type=str, default='', help='path of models.')
+    parser.add_argument(
+        '--img_paths', type=str, default='', help='path of images')
+    parser.add_argument(
+        '--video_path', type=str, default='', help='path of video.')
+    parser.add_argument(
+        '--use_camera',
+        type=bool,
+        default=False,
+        help='switch detect video or camera, default:video.')
+    parser.add_argument(
+        '--open_imshow',
+        type=bool,
+        default=False,
+        help='visualize video detection results in real time.')
+    parser.add_argument(
+        '--use_gpu',
+        type=bool,
+        default=False,
+        help='switch cpu/gpu, default:cpu.')
     args = parser.parse_args()
     return args
 
@@ -108,10 +105,11 @@ class MaskClassifier:
         h, w = self.EVAL_SIZE[1], self.EVAL_SIZE[0]
         inputs = []
         for face in faces:
-            im = cv2.resize(face.rect_data, (128, 128),
-                            fx=0,
-                            fy=0,
-                            interpolation=cv2.INTER_CUBIC)
+            im = cv2.resize(
+                face.rect_data, (128, 128),
+                fx=0,
+                fy=0,
+                interpolation=cv2.INTER_CUBIC)
             # HWC -> CHW
             im = im.swapaxes(1, 2)
             im = im.swapaxes(0, 1)
@@ -151,10 +149,8 @@ class FaceDetector:
 
     def Preprocess(self, image, shrink):
         h, w = int(image.shape[1] * shrink), int(image.shape[0] * shrink)
-        im = cv2.resize(image, (h, w),
-                        fx=0,
-                        fy=0,
-                        interpolation=cv2.INTER_CUBIC)
+        im = cv2.resize(
+            image, (h, w), fx=0, fy=0, interpolation=cv2.INTER_CUBIC)
         # HWC -> CHW
         im = im.swapaxes(1, 2)
         im = im.swapaxes(0, 1)
@@ -194,16 +190,18 @@ class FaceDetector:
 
 
 def predict_images(args):
-    detector = FaceDetector(model_dir=args.models_dir + '/pyramidbox_lite/',
-                            mean=[104.0, 177.0, 123.0],
-                            scale=[0.007843, 0.007843, 0.007843],
-                            use_gpu=args.use_gpu,
-                            threshold=0.7)
-
-    classifier = MaskClassifier(model_dir=args.models_dir + '/mask_detector/',
-                                mean=[0.5, 0.5, 0.5],
-                                scale=[1.0, 1.0, 1.0],
-                                use_gpu=args.use_gpu)
+    detector = FaceDetector(
+        model_dir=args.models_dir + '/pyramidbox_lite/',
+        mean=[104.0, 177.0, 123.0],
+        scale=[0.007843, 0.007843, 0.007843],
+        use_gpu=args.use_gpu,
+        threshold=0.7)
+
+    classifier = MaskClassifier(
+        model_dir=args.models_dir + '/mask_detector/',
+        mean=[0.5, 0.5, 0.5],
+        scale=[1.0, 1.0, 1.0],
+        use_gpu=args.use_gpu)
     names = []
     image_paths = []
     for name in os.listdir(args.img_paths):
@@ -229,16 +227,18 @@ def predict_video(args, im_shape=(1920, 1080), use_camera=False):
         capture = cv2.VideoCapture(0)
     else:
         capture = cv2.VideoCapture(args.video_path)
-    detector = FaceDetector(model_dir=args.models_dir + '/pyramidbox_lite/',
-                            mean=[104.0, 177.0, 123.0],
-                            scale=[0.007843, 0.007843, 0.007843],
-                            use_gpu=args.use_gpu,
-                            threshold=0.7)
-
-    classifier = MaskClassifier(model_dir=args.models_dir + '/mask_detector/',
-                                mean=[0.5, 0.5, 0.5],
-                                scale=[1.0, 1.0, 1.0],
-                                use_gpu=args.use_gpu)
+    detector = FaceDetector(
+        model_dir=args.models_dir + '/pyramidbox_lite/',
+        mean=[104.0, 177.0, 123.0],
+        scale=[0.007843, 0.007843, 0.007843],
+        use_gpu=args.use_gpu,
+        threshold=0.7)
+
+    classifier = MaskClassifier(
+        model_dir=args.models_dir + '/mask_detector/',
+        mean=[0.5, 0.5, 0.5],
+        scale=[1.0, 1.0, 1.0],
+        use_gpu=args.use_gpu)
 
     path = './result'
     isExists = os.path.exists(path)
@@ -248,8 +248,8 @@ def predict_video(args, im_shape=(1920, 1080), use_camera=False):
     width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
     height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
     fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    writer = cv2.VideoWriter(os.path.join(path, 'result.mp4'), fourcc, fps,
-                             (width, height))
+    writer = cv2.VideoWriter(
+        os.path.join(path, 'result.mp4'), fourcc, fps, (width, height))
     import time
     start_time = time.time()
     index = 0

paddlehub/reader/nlp_reader.py

@@ -1162,9 +1162,6 @@ class LACClassifyReader(BaseReader):
         self.tokenizer = tokenization.FullTokenizer(
             vocab_file=vocab_path, do_lower_case=False)
         self.vocab = self.tokenizer.vocab
-        self.feed_key = list(
-            self.lac.processor.data_format(
-                sign_name="lexical_analysis").keys())[0]
         self.has_processed = {
             "train": False,
             "dev": False,
@@ -1200,7 +1197,7 @@ class LACClassifyReader(BaseReader):
                 "Unknown phase, which should be in ['train', 'dev', 'test'].")
 
         def preprocess(text):
-            data_dict = {self.feed_key: [text]}
+            data_dict = {'text': [text]}
             processed = self.lac.lexical_analysis(data=data_dict)
             processed = [
                 self.vocab[word] for word in processed[0]['word']

paddlehub/version.py

@@ -13,5 +13,5 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """ PaddleHub version string """
-hub_version = "1.5.2"
+hub_version = "1.6.0"
 module_proto_version = "1.0.0"