add python infer

contrib/RealTimeHumanSeg/python/infer.py

+# coding: utf8
+# copyright (c) 2019 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 sys
+import ast
+import time
+import json
+import argparse
+
+import numpy as np
+import cv2
+
+import paddle.fluid as fluid
+
+
+def LoadModel(model_dir, use_gpu=False):
+    prog_file = os.path.join(model_dir, '__model__')
+    params_file = os.path.join(model_dir, '__params__')
+    config = fluid.core.AnalysisConfig(prog_file, params_file)
+    if use_gpu:
+        config.enable_use_gpu(100, 0)
+        config.switch_ir_optim(True)
+    else:
+        config.disable_gpu()
+    config.disable_glog_info()
+    config.switch_specify_input_names(True)
+    config.enable_memory_optim()
+    return fluid.core.create_paddle_predictor(config)
+
+
+class HumanSeg:
+    def __init__(self, model_dir, mean, scale, eval_size, use_gpu=False):
+        self.mean = np.array(mean).reshape((3, 1, 1))
+        self.scale = np.array(scale).reshape((3, 1, 1))
+        self.eval_size = eval_size
+        self.predictor = LoadModel(model_dir, use_gpu)
+
+    def Preprocess(self, image):
+        im = cv2.resize(image,
+                        self.eval_size,
+                        fx=0,
+                        fy=0,
+                        interpolation=cv2.INTER_CUBIC)
+        # HWC -> CHW
+        im = im.swapaxes(1, 2)
+        im = im.swapaxes(0, 1)
+        # Convert to float
+        im = im[:, :, :].astype('float32')
+        # im  = (im - mean) * scale
+        im = im - self.mean
+        im = im * self.scale
+        im = im[np.newaxis, :, :, :]
+        return im
+
+    def Postprocess(self, image, output_data):
+        mask = output_data[0, 1, :, :]
+        mask = cv2.resize(mask, (image.shape[1], image.shape[0]))
+        scoremap = np.repeat(mask[:, :, np.newaxis], 3, axis=2)
+        bg = np.ones_like(scoremap) * 255
+        merge_im = (scoremap * image + (1 - scoremap) * bg).astype(np.uint8)
+        return merge_im
+
+    def Predict(self, image):
+        ori_im = image.copy()
+        im = self.Preprocess(image)
+        im_tensor = fluid.core.PaddleTensor(im.copy().astype('float32'))
+        output_data = self.predictor.run([im_tensor])[0]
+        output_data = output_data.as_ndarray()
+        return self.Postprocess(image, output_data)
+
+# Do Predicting on a image
+def PredictImage(seg, image_path):
+    im = cv2.imread(input_path)
+    im = seg.Predict(im)
+    cv2.imwrite('result.jpeg', im)
+
+# Do Predicting on a video
+def PredictVideo(seg, video_path):
+
+
+if __name__ == "__main__":
+    if len(sys.argv) < 3:
+        print('Usage: python infer.py /path/to/model/ /path/to/video')
+        exit(0)
+
+    model_dir = sys.argv[1]
+    input_path = sys.argv[2]
+    use_gpu = int(sys.argv[3]) if len(sys.argv) >= 4 else 0
+    # Init model 
+    mean = [104.008, 116.669, 122.675]
+    scale = [1.0, 1.0, 1.0]
+    eval_size = (192, 192)
+    seg = HumanSeg(model_dir, mean, scale, eval_size, use_gpu)