simplify augmentation

* update show data

Author: Borda

keras_yolo3/model.py

@@ -7,7 +7,7 @@
 from functools import wraps
 
 import tensorflow as tf
-from keras import backend as K
+import keras.backend as K
 from keras.layers import Conv2D, Add, ZeroPadding2D, UpSampling2D, Concatenate, MaxPooling2D
 from keras.layers import Input, Lambda
 from keras.layers.advanced_activations import LeakyReLU

keras_yolo3/utils.py

@@ -460,35 +460,37 @@ def get_augmented_data(annotation_line, input_shape, augment=True, max_boxes=20,
     boxes = np.array([list(map(float, box.split(',')))
                       for box in line_split[1:]]).astype(int)
 
-    if not augment:
-        # resize image
-        img_data, adj_box_data = normalize_image_bboxes(
-            image, boxes, input_shape, resize_img=resize_img,
-            allow_rnd_shift=allow_rnd_shift, interp=interp)
-        box_data = _copy_bboxes(boxes, adj_box_data, max_boxes, check_dropped=False)
-        # yolo3.visual.show_augment_data(image_open(line_split[0]), boxes, img_data, box_data)
-        return img_data, box_data
+    # if not augment:
+    #     img_data, adj_box_data = normalize_image_bboxes(
+    #         image, boxes, input_shape, resize_img=resize_img,
+    #         allow_rnd_shift=allow_rnd_shift, interp=interp)
+    #     box_data = _copy_bboxes(boxes, adj_box_data, max_boxes, check_dropped=False)
+    #     return img_data, box_data
 
     # resize image
     # new_ar = cnn_w / cnn_h * _rand(1 - jitter, 1 + jitter) / _rand(1 - jitter, 1 + jitter)
-    scaling = _rand(1. / img_scaling, img_scaling)
     if resize_img:
+        scaling = _rand(1. / img_scaling, img_scaling) if augment else 1.
         img_data, scaling, (dx, dy) = _scale_image_to_cnn(
             image, input_shape, scaling, allow_rnd_shift=allow_rnd_shift, interp=interp)
     else:
         img_data, scaling, (dx, dy) = _crop_image_to_cnn(image, input_shape, allow_rnd_shift)
     image = Image.fromarray(np.round(img_data * 255).astype(np.uint8))
 
-    # flip image or not
-    flip_horizontal = np.random.random() < 0.5 and flip_horizontal
-    if flip_horizontal:
-        image = image.transpose(Image.FLIP_LEFT_RIGHT)
-    flip_vertical = np.random.random() < 0.5 and flip_vertical
-    if flip_vertical:
-        image = image.transpose(Image.FLIP_TOP_BOTTOM)
-
-    # distort image
-    img_data = augment_image_color(image, hue, sat, val)
+    if augment:
+        # flip image or not
+        flip_horizontal = np.random.random() < 0.5 and flip_horizontal
+        if flip_horizontal:
+            image = image.transpose(Image.FLIP_LEFT_RIGHT)
+        flip_vertical = np.random.random() < 0.5 and flip_vertical
+        if flip_vertical:
+            image = image.transpose(Image.FLIP_TOP_BOTTOM)
+
+        # distort image
+        img_data = augment_image_color(image, hue, sat, val)
+    else:
+        flip_horizontal = False
+        flip_vertical = False
 
     if len(boxes) == 0:
         max_boxes = max_boxes if max_boxes else 1
@@ -499,7 +501,7 @@ def get_augmented_data(annotation_line, input_shape, augment=True, max_boxes=20,
     adj_box_data = adjust_bboxes(boxes, input_shape, flip_horizontal, flip_vertical,
                                  scaling, scaling, dx, dy)
     box_data = _copy_bboxes(boxes, adj_box_data, max_boxes)
-    # yolo3.visual.show_augment_data(image_open(line_split[0]), boxes, img_data, box_data)
+    # yolo3.visual.show_augment_data(image_open(line_split[0]), boxes, img_data, box_data, title=line_split[0])
     return img_data, box_data
 
 

keras_yolo3/visual.py

@@ -67,7 +67,7 @@ def _draw_bbox(ax, bbox, color='r'):
     ax.plot(x, y, color=color)
 
 
-def show_augment_data(image_in, bboxes, img_data, box_data):
+def show_augment_data(image_in, bboxes, img_data, box_data, title=''):
     """visualise image_data and box_data
 
     :param ndaaray image_in: original image
@@ -81,13 +81,15 @@ def show_augment_data(image_in, bboxes, img_data, box_data):
     >>> show_augment_data(img, [box], img, [box, [0] * 5])  # doctest: +ELLIPSIS
     <...>
     """
-    fig, axarr = plt.subplots(ncols=2, figsize=(12, 5))
+    fig, axarr = plt.subplots(ncols=2, figsize=(14, 8), constrained_layout=True)
     axarr[0].set_title('Original image with annotation')
     axarr[0].imshow(np.array(image_in))
     for box in bboxes:
         _draw_bbox(axarr[0], box)
-    axarr[1].set_title('Augmented image with adjusted bboxes')
-    axarr[1].imshow(np.array(img_data))
+    axarr[1].set_title('Augmented image with adjusted bounding boxes')
+    axarr[1].imshow(np.array(img_data), vmin=0, vmax=1)
     for box in box_data:
         _draw_bbox(axarr[1], box)
+    fig.suptitle(title)
+    fig.tight_layout()
     return fig

keras_yolo3/yolo.py

@@ -9,7 +9,7 @@
 import colorsys
 
 import numpy as np
-from keras import backend as K
+import keras.backend as K
 from keras.models import load_model
 from keras.layers import Input
 from keras.utils import multi_gpu_model

requirements.txt

@@ -1,11 +1,13 @@
 numpy
 matplotlib<3.0.0  # dropped support for py3.5 and lower
 Pillow
-keras>=2.2.0, <2.2.5 # missing control_flow_ops in keras.backend
+# keras!=2.2.3,2.2.4 # crash multi GPU training, see https://github.com/keras-team/keras/issues/11348
+# keras!=2.2.5 # missing control_flow_ops in keras.backend
+keras>=2.2.0, <2.2.5
 pydot
 pydot-ng
 graphviz
-tensorflow>=1.4.0, <2.0.0
+tensorflow>=1.4.0, <2.0.0  # breaking compatibility
 pandas
 opencv-python
 tqdm

scripts/convert_weights.py

@@ -31,7 +31,7 @@
 
 import tqdm
 import numpy as np
-from keras import backend as K
+import keras.backend as K
 from keras.layers import (Conv2D, Input, ZeroPadding2D, Add,
                           UpSampling2D, MaxPooling2D, Concatenate)
 from keras.layers.advanced_activations import LeakyReLU

tox.ini

@@ -16,7 +16,7 @@ deps =
 commands =
     check-manifest --ignore tox.ini,tests*
     python setup.py check -m -s
-    coverage run --source keras_yolo3 -m py.test keras_yolo3 scripts -v --doctest-modules
+    coverage run --source keras_yolo3 -m py.test keras_yolo3 -v --doctest-modules
     coverage report
     flake8 .