add 3d pose visualize

Author: hanjr

examples/app_tutorials/model/pose_config.txt

@@ -0,0 +1,44 @@
+linear_model/w1
+linear_model/b1
+linear_model/batch_normalization/beta
+linear_model/batch_normalization/gamma
+linear_model/batch_normalization/moving_mean
+linear_model/batch_normalization/moving_variance
+linear_model/two_linear_0/w2_0
+linear_model/two_linear_0/b2_0
+linear_model/two_linear_0/batch_normalization10/beta
+linear_model/two_linear_0/batch_normalization10/gamma
+linear_model/two_linear_0/batch_normalization10/moving_mean
+linear_model/two_linear_0/batch_normalization10/moving_variance
+linear_model/two_linear_0/w3_0
+linear_model/two_linear_0/b3_0
+linear_model/two_linear_0/batch_normalization20/beta
+linear_model/two_linear_0/batch_normalization20/gamma
+linear_model/two_linear_0/batch_normalization20/moving_mean
+linear_model/two_linear_0/batch_normalization20/moving_variance
+linear_model/two_linear_1/w2_1
+linear_model/two_linear_1/b2_1
+linear_model/two_linear_1/batch_normalization11/beta
+linear_model/two_linear_1/batch_normalization11/gamma
+linear_model/two_linear_1/batch_normalization11/moving_mean
+linear_model/two_linear_1/batch_normalization11/moving_variance
+linear_model/two_linear_1/w3_1
+linear_model/two_linear_1/b3_1
+linear_model/two_linear_1/batch_normalization21/beta
+linear_model/two_linear_1/batch_normalization21/gamma
+linear_model/two_linear_1/batch_normalization21/moving_mean
+linear_model/two_linear_1/batch_normalization21/moving_variance
+linear_model/two_linear_2/w2_2
+linear_model/two_linear_2/b2_2
+linear_model/two_linear_2/batch_normalization12/beta
+linear_model/two_linear_2/batch_normalization12/gamma
+linear_model/two_linear_2/batch_normalization12/moving_mean
+linear_model/two_linear_2/batch_normalization12/moving_variance
+linear_model/two_linear_2/w3_2
+linear_model/two_linear_2/b3_2
+linear_model/two_linear_2/batch_normalization22/beta
+linear_model/two_linear_2/batch_normalization22/gamma
+linear_model/two_linear_2/batch_normalization22/moving_mean
+linear_model/two_linear_2/batch_normalization22/moving_variance
+linear_model/w4
+linear_model/b4

examples/app_tutorials/tutorial_human_3dpose_estimation_LCN.py

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+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+from tensorlayer.app.human_pose_estimation.common import DataReader, visualize_3D_pose, flip_data
+from tensorlayer.app.human_pose_estimation.LCN import CGCNN
+import numpy as np
+
+datareader = DataReader()
+train_data, test_data = datareader.read_2d(which='scale', mode='gt', read_confidence=False)
+train_labels, test_labels = datareader.read_3d(which='scale', mode='gt')
+network = CGCNN(pretrained=True)
+test_data = flip_data(test_data)
+result = network(test_data, is_train=False)
+result = datareader.denormalize3D(np.asarray(result), which='scale')
+test_data = datareader.denormalize2D(test_data, which='scale')
+test_labels = datareader.denormalize3D(test_labels, which='scale')
+visualize_3D_pose(
+    test_data, test_labels, result
+)  # We plot 4 examples. You can modify this function according to your own needs.

examples/app_tutorials/tutorial_object_detection_yolov4_image.py

@@ -15,7 +15,6 @@
 image = cv2.cvtColor(np.array(original_image), cv2.COLOR_BGR2RGB)
 net = computer_vision.object_detection('yolo4-mscoco')
 json_result = net(original_image)
-print(type(json_result))
 image = visualize.draw_boxes_and_labels_to_image_with_json(image, json_result, class_names)
 image = Image.fromarray(image.astype(np.uint8))
 image.show()