modified test lanenet

tools/test_lanenet.py

@@ -50,6 +50,20 @@ def init_args():
     return parser.parse_args()
 
 
+def minmax_scale(input_arr):
+    """
+
+    :param input_arr:
+    :return:
+    """
+    min_val = np.min(input_arr)
+    max_val = np.max(input_arr)
+
+    output_arr = (input_arr - min_val) * 255.0 / (max_val - min_val)
+
+    return output_arr
+
+
 def test_lanenet(image_path, weights_path, use_gpu):
     """
 
@@ -69,10 +83,10 @@ def test_lanenet(image_path, weights_path, use_gpu):
     log.info('图像读取完毕, 耗时: {:.5f}s'.format(time.time() - t_start))
 
     input_tensor = tf.placeholder(dtype=tf.float32, shape=[1, 256, 512, 3], name='input_tensor')
-    phase_tensor = tf.constant('train', tf.string)
+    phase_tensor = tf.constant('test', tf.string)
 
     net = lanenet_merge_model.LaneNet(phase=phase_tensor, net_flag='vgg')
-    binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor, name='lanenet_loss')
+    binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor, name='lanenet_model')
 
     cluster = lanenet_cluster.LaneNetCluster()
     postprocessor = lanenet_postprocess.LaneNetPoseProcessor()
@@ -103,26 +117,10 @@ def test_lanenet(image_path, weights_path, use_gpu):
         binary_seg_image[0] = postprocessor.postprocess(binary_seg_image[0])
         mask_image = cluster.get_lane_mask(binary_seg_ret=binary_seg_image[0],
                                            instance_seg_ret=instance_seg_image[0])
-        # mask_image = cluster.get_lane_mask_v2(instance_seg_ret=instance_seg_image[0])
-        # mask_image = cv2.resize(mask_image, (image_vis.shape[1], image_vis.shape[0]),
-        #                         interpolation=cv2.INTER_LINEAR)
-
-        ele_mex = np.max(instance_seg_image[0], axis=(0, 1))
-        for i in range(3):
-            if ele_mex[i] == 0:
-                scale = 1
-            else:
-                scale = 255 / ele_mex[i]
-            instance_seg_image[0][:, :, i] *= int(scale)
-        embedding_image = np.array(instance_seg_image[0], np.uint8)
-        # cv2.imwrite('embedding_mask.png', embedding_image)
 
-        # mask_image = cluster.get_lane_mask_v2(instance_seg_ret=embedding_image)
-        # mask_image = cv2.resize(mask_image, (image_vis.shape[1], image_vis.shape[0]),
-        #                         interpolation=cv2.INTER_LINEAR)
-
-        cv2.imwrite('binary_ret.png', binary_seg_image[0] * 255)
-        cv2.imwrite('instance_ret.png', embedding_image)
+        for i in range(4):
+            instance_seg_image[0][:, :, i] = minmax_scale(instance_seg_image[0][:, :, i])
+        embedding_image = np.array(instance_seg_image[0], np.uint8)
 
         plt.figure('mask_image')
         plt.imshow(mask_image[:, :, (2, 1, 0)])
@@ -157,10 +155,10 @@ def test_lanenet_batch(image_dir, weights_path, batch_size, use_gpu, save_dir=No
                       glob.glob('{:s}/**/*.jpeg'.format(image_dir), recursive=True)
 
     input_tensor = tf.placeholder(dtype=tf.float32, shape=[None, 256, 512, 3], name='input_tensor')
-    phase_tensor = tf.constant('train', tf.string)
+    phase_tensor = tf.constant('test', tf.string)
 
     net = lanenet_merge_model.LaneNet(phase=phase_tensor, net_flag='vgg')
-    binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor, name='lanenet_loss')
+    binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor, name='lanenet_model')
 
     cluster = lanenet_cluster.LaneNetCluster()
     postprocessor = lanenet_postprocess.LaneNetPoseProcessor()
@@ -254,3 +252,8 @@ def test_lanenet_batch(image_dir, weights_path, batch_size, use_gpu, save_dir=No
         # test hnet model on a batch of image
         test_lanenet_batch(image_dir=args.image_path, weights_path=args.weights_path,
                            save_dir=args.save_dir, use_gpu=args.use_gpu, batch_size=args.batch_size)
+
+    # test_net_hdmap('/media/baidu/Data/高精图像质检/20180716t162624_hb/20180716T164118',
+    #                '/home/baidu/Silly_Project/ICode/baidu/beec/lanenet-lane-detection/model/'
+    #                'tusimple_lanenet/tusimple_lanenet_vgg_2018-05-21-11-11-03.ckpt-94000',
+    #                '/media/baidu/Data/高精图像质检/20180716t162624_hb/lanenet_mask_ret')