def main(img):
"""Applies static transformations to img and plots for visual comparison"""
# set up augmentation parameters
hw = (img.shape[0], img.shape[1])
crop_percentage = 0.10
crop_hw = (hw[0] - int(hw[0] * crop_percentage),
hw[1] - int(hw[1] * crop_percentage))
augmentation_params = dict(
rotation=45,
zoom=(0.5, 2.0), # zoom (x, y) = (col, row)
shear=15,
translation=(int(hw[0] * 0.1), -int(hw[1] * 0.2)),
flip_lr=True,
flip_ud=True)
nAugmentations = len(augmentation_params)
# set up plotting.
# nPlots = original image + len(augs) + all_augment + all_augment_crop
nPlots = nAugmentations + 3
nrows = int(np.ceil(np.sqrt(nPlots)))
ncols = int(np.ceil(nPlots / float(nrows)))
fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(16, 12))
axes = axes.flatten()
# Plot the original image
axes[0].imshow(img)
axes[0].set_title("Original Image")
# Plot each augmentation parameter, isolated
for it, (key, param) in enumerate(augmentation_params.iteritems()):
ax = axes[it + 1]
augmentation_param = {key: param}
# transform image
img_wf = dtf.transform_image(img, **augmentation_param)
# plot image
ax.imshow(img_wf.astype(np.uint8))
ax.set_title("Augmentation Param: %s = %s" % (key, param))
ax.set_xticks([])
ax.set_yticks([])
# plot image with all augmentations at once
ax = axes[it + 2]
img_wf_og = dtf.transform_image(img, **augmentation_params)
ax.imshow(img_wf_og.astype(np.uint8))
ax.set_title("All Augmentations")
ax.set_xticks([])
ax.set_yticks([])
# plot image with all augmentations & center crop
ax = axes[it + 3]
img_wf = dtf.transform_image(img,
output_shape=crop_hw,
**augmentation_params)
ax.imshow(img_wf.astype(np.uint8))
ax.set_title("All Augmentation + Crop")
def main(img):
"""Applies static transformations to img and plots for visual comparison"""
# set up augmentation parameters
hw = (img.shape[0], img.shape[1])
crop_percentage = 0.10
crop_hw = (hw[0] - int(hw[0]*crop_percentage),
hw[1] - int(hw[1]*crop_percentage))
augmentation_params = dict(rotation=45,
zoom=(0.5, 2.0), # zoom (x, y) = (col, row)
shear=15,
translation=(int(hw[0]*0.1), -int(hw[1]*0.2)),
flip_lr=True,
flip_ud=True)
nAugmentations = len(augmentation_params)
# set up plotting.
# nPlots = original image + len(augs) + all_augment + all_augment_crop
nPlots = nAugmentations + 3
nrows = int(np.ceil(np.sqrt(nPlots)))
ncols = int(np.ceil(nPlots/float(nrows)))
fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(16, 12))
axes = axes.flatten()
# Plot the original image
axes[0].imshow(img)
axes[0].set_title("Original Image")
# Plot each augmentation parameter, isolated
for it, (key, param) in enumerate(augmentation_params.iteritems()):
ax = axes[it + 1]
augmentation_param = {key: param}
# transform image
img_wf = dtf.transform_image(img, **augmentation_param)
# plot image
ax.imshow(img_wf.astype(np.uint8))
ax.set_title("Augmentation Param: %s = %s" % (key, param))
ax.set_xticks([])
ax.set_yticks([])
# plot image with all augmentations at once
ax = axes[it + 2]
img_wf_og = dtf.transform_image(img, **augmentation_params)
ax.imshow(img_wf_og.astype(np.uint8))
ax.set_title("All Augmentations")
ax.set_xticks([])
ax.set_yticks([])
# plot image with all augmentations & center crop
ax = axes[it + 3]
img_wf = dtf.transform_image(img, output_shape=crop_hw,
**augmentation_params)
ax.imshow(img_wf.astype(np.uint8))
ax.set_title("All Augmentation + Crop")
if title is None:
title = ''
ax.imshow(img, **imshow_kwargs)
ax.set_title(title, fontsize=20)
if axis_off:
ax.set_xticks([])
ax.set_yticks([])
# load test img
img = np.array(Image.open('../tests/test_data/cat.jpg', 'r'))
# rotate image by 20 deg CCW
img_rot = dtf.transform_image(img, rotation=20)
img_rot = img_rot.astype(np.uint8) # by default, transform returns float32
# zoom in the center of image by 2x (not upscale). zoom = (col, row)
img_zoom = dtf.transform_image(img, zoom=(2.0, 2.0)).astype(np.uint8)
# translate x,y: -50 in x direction and 200 in y direction
img_txy = dtf.transform_image(img, translation=(-50, 200)).astype(np.uint8)
# Take a 200x200 center-crop of image
img_crop = dtf.transform_image(img, output_shape=(600, 600)).astype(np.uint8)
# rotate, translate, and zoom all at once
img_all = dtf.transform_image(img,
rotation=20,
zoom=(2.0, 2.0),
if title is None:
title = ''
ax.imshow(img, **imshow_kwargs)
ax.set_title(title, fontsize=20)
if axis_off:
ax.set_xticks([])
ax.set_yticks([])
# load test img
img = np.array(Image.open('../tests/test_data/cat.jpg', 'r'))
# rotate image by 20 deg CCW
img_rot = dtf.transform_image(img, rotation=20)
img_rot = img_rot.astype(np.uint8) # by default, transform returns float32
# zoom in the center of image by 2x (not upscale). zoom = (col, row)
img_zoom = dtf.transform_image(img, zoom=(2.0, 2.0)).astype(np.uint8)
# translate x,y: -50 in x direction and 200 in y direction
img_txy = dtf.transform_image(img, translation=(-50, 200)).astype(np.uint8)
# Take a 200x200 center-crop of image
img_crop = dtf.transform_image(img, output_shape=(600, 600)).astype(np.uint8)
# rotate, translate, and zoom all at once
img_all = dtf.transform_image(img, rotation=20, zoom=(2.0, 2.0),
translation=(-50, 200)).astype(np.uint8)
