def preprocess(img, bbox_labels, mode, settings):
img_width, img_height = img.size
sampled_labels = bbox_labels
if mode == 'train':
if settings._apply_distort:
img = image_util.distort_image(img, settings)
if settings._apply_expand:
img, bbox_labels, img_width, img_height = image_util.expand_image(
img, bbox_labels, img_width, img_height, settings)
# sampling
batch_sampler = []
# hard-code here
batch_sampler.append(
image_util.sampler(1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0))
batch_sampler.append(
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 0.0))
batch_sampler.append(
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 0.0))
batch_sampler.append(
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 0.0))
batch_sampler.append(
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 0.0))
batch_sampler.append(
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0))
batch_sampler.append(
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0))
sampled_bbox = image_util.generate_batch_samples(
batch_sampler, bbox_labels)
img = np.array(img)
if len(sampled_bbox) > 0:
idx = int(np.random.uniform(0, len(sampled_bbox)))
img, sampled_labels = image_util.crop_image(
img, bbox_labels, sampled_bbox[idx], img_width, img_height)
img = Image.fromarray(img)
img = img.resize((settings.resize_w, settings.resize_h), Image.ANTIALIAS)
img = np.array(img)
if mode == 'train':
mirror = int(np.random.uniform(0, 2))
if mirror == 1:
img = img[:, ::-1, :]
for i in six.moves.xrange(len(sampled_labels)):
tmp = sampled_labels[i][1]
sampled_labels[i][1] = 1 - sampled_labels[i][3]
sampled_labels[i][3] = 1 - tmp
# HWC to CHW
if len(img.shape) == 3:
img = np.swapaxes(img, 1, 2)
img = np.swapaxes(img, 1, 0)
# RBG to BGR
img = img[[2, 1, 0], :, :]
img = img.astype('float32')
img -= settings.img_mean
img = img * 0.007843
return img, sampled_labels
def preprocess(img, bbox_labels, mode, settings, image_path):
img_width, img_height = img.size
sampled_labels = bbox_labels
if mode == 'train':
if settings.apply_distort:
img = image_util.distort_image(img, settings)
if settings.apply_expand:
img, bbox_labels, img_width, img_height = image_util.expand_image(
img, bbox_labels, img_width, img_height, settings)
# sampling
batch_sampler = []
# used for continuous evaluation
if 'ce_mode' in os.environ:
random.seed(0)
np.random.seed(0)
prob = np.random.uniform(0., 1.)
if prob > settings.data_anchor_sampling_prob:
scale_array = np.array([16, 32, 64, 128, 256, 512])
batch_sampler.append(
image_util.sampler(1, 10, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.2,
0.0, True))
sampled_bbox = image_util.generate_batch_random_samples(
batch_sampler, bbox_labels, img_width, img_height, scale_array,
settings.resize_width, settings.resize_height)
img = np.array(img)
if len(sampled_bbox) > 0:
idx = int(np.random.uniform(0, len(sampled_bbox)))
img, sampled_labels = image_util.crop_image_sampling(
img, bbox_labels, sampled_bbox[idx], img_width, img_height,
settings.resize_width, settings.resize_height,
settings.min_face_size)
img = img.astype('uint8')
img = Image.fromarray(img)
else:
# hard-code here
batch_sampler.append(
image_util.sampler(1, 50, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0,
0.0, True))
batch_sampler.append(
image_util.sampler(1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0,
0.0, True))
batch_sampler.append(
image_util.sampler(1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0,
0.0, True))
batch_sampler.append(
image_util.sampler(1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0,
0.0, True))
batch_sampler.append(
image_util.sampler(1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0,
0.0, True))
sampled_bbox = image_util.generate_batch_samples(
batch_sampler, bbox_labels, img_width, img_height)
img = np.array(img)
if len(sampled_bbox) > 0:
idx = int(np.random.uniform(0, len(sampled_bbox)))
img, sampled_labels = image_util.crop_image(
img, bbox_labels, sampled_bbox[idx], img_width, img_height,
settings.resize_width, settings.resize_height,
settings.min_face_size)
img = Image.fromarray(img)
interp_mode = [
Image.BILINEAR, Image.HAMMING, Image.NEAREST, Image.BICUBIC,
Image.LANCZOS
]
interp_indx = np.random.randint(0, 5)
img = img.resize((settings.resize_width, settings.resize_height),
resample=interp_mode[interp_indx])
img = np.array(img)
if mode == 'train':
mirror = int(np.random.uniform(0, 2))
if mirror == 1:
img = img[:, ::-1, :]
for i in six.moves.xrange(len(sampled_labels)):
tmp = sampled_labels[i][1]
sampled_labels[i][1] = 1 - sampled_labels[i][3]
sampled_labels[i][3] = 1 - tmp
img = to_chw_bgr(img)
img = img.astype('float32')
img -= settings.img_mean
img = img * settings.scale
return img, sampled_labels