Merge pull request #485 from tensorlayer/api-rename

rename APIs of obj detection and pose

Author: wagamamaz

docs/modules/prepro.rst

@@ -81,14 +81,12 @@ Some of the code in this package are borrowed from Keras.
    parse_darknet_ann_str_to_list
    parse_darknet_ann_list_to_cls_box
 
-   obj_box_left_right_flip
+   obj_box_horizontal_flip
    obj_box_imresize
    obj_box_crop
    obj_box_shift
    obj_box_zoom
 
-
-
    pad_sequences
    remove_pad_sequences
    process_sequences
@@ -270,7 +268,7 @@ Hi, here is an example for image augmentation on VOC dataset.
        ann_list[idx][1], [], classes, True, save_name='_im_original.png')
 
   # left right flip
-  im_flip, coords = tl.prepro.obj_box_left_right_flip(image,
+  im_flip, coords = tl.prepro.obj_box_horizontal_flip(image,
           ann_list[idx][1], is_rescale=True, is_center=True, is_random=False)
   tl.vis.draw_boxes_and_labels_to_image(im_flip, ann_list[idx][0],
           coords, [], classes, True, save_name='_im_flip.png')
@@ -321,7 +319,7 @@ In practice, you may want to use threading method to process a batch of images a
       im = tl.prepro.illumination(im, gamma=(0.5, 1.5),
                contrast=(0.5, 1.5), saturation=(0.5, 1.5), is_random=True)
       ## flip randomly
-      im, coords = tl.prepro.obj_box_left_right_flip(im, coords,
+      im, coords = tl.prepro.obj_box_horizontal_flip(im, coords,
                is_rescale=True, is_center=True, is_random=True)
       ## randomly resize and crop image, it can have same effect as random zoom
       tmp0 = random.randint(1, int(im_size[0]*jitter))
@@ -400,7 +398,7 @@ Darknet format split class and coordinate
 
 Image Aug - Flip
 ^^^^^^^^^^^^^^^^^^^^^^^^^
-.. autofunction:: obj_box_left_right_flip
+.. autofunction:: obj_box_horizontal_flip
 
 Image Aug - Resize
 ^^^^^^^^^^^^^^^^^^^^^^^^^

docs/modules/visualize.rst

@@ -13,7 +13,7 @@ to visualize the model, activations etc. Here we provide more functions for data
    save_image
    save_images
    draw_boxes_and_labels_to_image
-   draw_mpii_people_to_image
+   draw_mpii_pose_to_image
    draw_weights
    CNN2d
    frame
@@ -47,7 +47,7 @@ Save image for object detection
 
 Save image for pose estimation (MPII)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-.. autofunction:: draw_mpii_people_to_image
+.. autofunction:: draw_mpii_pose_to_image
 
 Visualize model parameters
 ------------------------------

tensorlayer/prepro.py

@@ -2277,7 +2277,7 @@ def parse_darknet_ann_list_to_cls_box(annotations):
     return class_list, bbox_list
 
 
-def obj_box_left_right_flip(im, coords=None, is_rescale=False, is_center=False, is_random=False):
+def obj_box_horizontal_flip(im, coords=None, is_rescale=False, is_center=False, is_random=False):
     """Left-right flip the image and coordinates for object detection.
 
     Parameters
@@ -2354,6 +2354,8 @@ def _flip(im, coords):
         return _flip(im, coords)
 
 
+obj_box_left_right_flip = obj_box_horizontal_flip
+
 # im = np.zeros([80, 100])    # as an image with shape width=100, height=80
 # im, coords = obj_box_left_right_flip(im, coords=[[0.2, 0.4, 0.3, 0.3], [0.1, 0.5, 0.2, 0.3]], is_rescale=True, is_center=True, is_random=False)
 # logging.info(coords)

tensorlayer/visualize.py

@@ -222,7 +222,7 @@ def draw_boxes_and_labels_to_image(image, classes, coords, scores, classes_list,
     return image
 
 
-def draw_mpii_people_to_image(image, peoples, save_name='image.png'):
+def draw_mpii_pose_to_image(image, poses, save_name='image.png'):
     """Draw people(s) into image using MPII dataset format as input, return or save the result image.
 
     This is an experimental API, can be changed in the future.
@@ -231,7 +231,7 @@ def draw_mpii_people_to_image(image, peoples, save_name='image.png'):
     -----------
     image : numpy.array
         The RGB image [height, width, channel].
-    people : list of dict
+    poses : list of dict
         The people(s) annotation in MPII format, see ``tl.files.load_mpii_pose_dataset``.
     save_name : None or str
         The name of image file (i.e. image.png), if None, not to save image.
@@ -267,7 +267,7 @@ def draw_mpii_people_to_image(image, peoples, save_name='image.png'):
     if image.max() < 1:
         image = image * 255
 
-    for people in peoples:
+    for people in poses:
         ### Pose Keyponts
         joint_pos = people['joint_pos']
         # draw sketch
@@ -340,6 +340,9 @@ def draw_mpii_people_to_image(image, peoples, save_name='image.png'):
     return image
 
 
+draw_mpii_people_to_image = draw_mpii_pose_to_image
+
+
 def frame(I=None, second=5, saveable=True, name='frame', cmap=None, fig_idx=12836):
     """Display a frame(image). Make sure OpenAI Gym render() is disable before using it.