GPEN / retinaface /data /data_augment.py
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import cv2
import numpy as np
import random
from utils.box_utils import matrix_iof
def _crop(image, boxes, labels, landm, img_dim):
height, width, _ = image.shape
pad_image_flag = True
for _ in range(250):
"""
if random.uniform(0, 1) <= 0.2:
scale = 1.0
else:
scale = random.uniform(0.3, 1.0)
"""
PRE_SCALES = [0.3, 0.45, 0.6, 0.8, 1.0]
scale = random.choice(PRE_SCALES)
short_side = min(width, height)
w = int(scale * short_side)
h = w
if width == w:
l = 0
else:
l = random.randrange(width - w)
if height == h:
t = 0
else:
t = random.randrange(height - h)
roi = np.array((l, t, l + w, t + h))
value = matrix_iof(boxes, roi[np.newaxis])
flag = (value >= 1)
if not flag.any():
continue
centers = (boxes[:, :2] + boxes[:, 2:]) / 2
mask_a = np.logical_and(roi[:2] < centers, centers < roi[2:]).all(axis=1)
boxes_t = boxes[mask_a].copy()
labels_t = labels[mask_a].copy()
landms_t = landm[mask_a].copy()
landms_t = landms_t.reshape([-1, 5, 2])
if boxes_t.shape[0] == 0:
continue
image_t = image[roi[1]:roi[3], roi[0]:roi[2]]
boxes_t[:, :2] = np.maximum(boxes_t[:, :2], roi[:2])
boxes_t[:, :2] -= roi[:2]
boxes_t[:, 2:] = np.minimum(boxes_t[:, 2:], roi[2:])
boxes_t[:, 2:] -= roi[:2]
# landm
landms_t[:, :, :2] = landms_t[:, :, :2] - roi[:2]
landms_t[:, :, :2] = np.maximum(landms_t[:, :, :2], np.array([0, 0]))
landms_t[:, :, :2] = np.minimum(landms_t[:, :, :2], roi[2:] - roi[:2])
landms_t = landms_t.reshape([-1, 10])
# make sure that the cropped image contains at least one face > 16 pixel at training image scale
b_w_t = (boxes_t[:, 2] - boxes_t[:, 0] + 1) / w * img_dim
b_h_t = (boxes_t[:, 3] - boxes_t[:, 1] + 1) / h * img_dim
mask_b = np.minimum(b_w_t, b_h_t) > 0.0
boxes_t = boxes_t[mask_b]
labels_t = labels_t[mask_b]
landms_t = landms_t[mask_b]
if boxes_t.shape[0] == 0:
continue
pad_image_flag = False
return image_t, boxes_t, labels_t, landms_t, pad_image_flag
return image, boxes, labels, landm, pad_image_flag
def _distort(image):
def _convert(image, alpha=1, beta=0):
tmp = image.astype(float) * alpha + beta
tmp[tmp < 0] = 0
tmp[tmp > 255] = 255
image[:] = tmp
image = image.copy()
if random.randrange(2):
#brightness distortion
if random.randrange(2):
_convert(image, beta=random.uniform(-32, 32))
#contrast distortion
if random.randrange(2):
_convert(image, alpha=random.uniform(0.5, 1.5))
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
#saturation distortion
if random.randrange(2):
_convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5))
#hue distortion
if random.randrange(2):
tmp = image[:, :, 0].astype(int) + random.randint(-18, 18)
tmp %= 180
image[:, :, 0] = tmp
image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)
else:
#brightness distortion
if random.randrange(2):
_convert(image, beta=random.uniform(-32, 32))
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
#saturation distortion
if random.randrange(2):
_convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5))
#hue distortion
if random.randrange(2):
tmp = image[:, :, 0].astype(int) + random.randint(-18, 18)
tmp %= 180
image[:, :, 0] = tmp
image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)
#contrast distortion
if random.randrange(2):
_convert(image, alpha=random.uniform(0.5, 1.5))
return image
def _expand(image, boxes, fill, p):
if random.randrange(2):
return image, boxes
height, width, depth = image.shape
scale = random.uniform(1, p)
w = int(scale * width)
h = int(scale * height)
left = random.randint(0, w - width)
top = random.randint(0, h - height)
boxes_t = boxes.copy()
boxes_t[:, :2] += (left, top)
boxes_t[:, 2:] += (left, top)
expand_image = np.empty(
(h, w, depth),
dtype=image.dtype)
expand_image[:, :] = fill
expand_image[top:top + height, left:left + width] = image
image = expand_image
return image, boxes_t
def _mirror(image, boxes, landms):
_, width, _ = image.shape
if random.randrange(2):
image = image[:, ::-1]
boxes = boxes.copy()
boxes[:, 0::2] = width - boxes[:, 2::-2]
# landm
landms = landms.copy()
landms = landms.reshape([-1, 5, 2])
landms[:, :, 0] = width - landms[:, :, 0]
tmp = landms[:, 1, :].copy()
landms[:, 1, :] = landms[:, 0, :]
landms[:, 0, :] = tmp
tmp1 = landms[:, 4, :].copy()
landms[:, 4, :] = landms[:, 3, :]
landms[:, 3, :] = tmp1
landms = landms.reshape([-1, 10])
return image, boxes, landms
def _pad_to_square(image, rgb_mean, pad_image_flag):
if not pad_image_flag:
return image
height, width, _ = image.shape
long_side = max(width, height)
image_t = np.empty((long_side, long_side, 3), dtype=image.dtype)
image_t[:, :] = rgb_mean
image_t[0:0 + height, 0:0 + width] = image
return image_t
def _resize_subtract_mean(image, insize, rgb_mean):
interp_methods = [cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_NEAREST, cv2.INTER_LANCZOS4]
interp_method = interp_methods[random.randrange(5)]
image = cv2.resize(image, (insize, insize), interpolation=interp_method)
image = image.astype(np.float32)
image -= rgb_mean
return image.transpose(2, 0, 1)
class preproc(object):
def __init__(self, img_dim, rgb_means):
self.img_dim = img_dim
self.rgb_means = rgb_means
def __call__(self, image, targets):
assert targets.shape[0] > 0, "this image does not have gt"
boxes = targets[:, :4].copy()
labels = targets[:, -1].copy()
landm = targets[:, 4:-1].copy()
image_t, boxes_t, labels_t, landm_t, pad_image_flag = _crop(image, boxes, labels, landm, self.img_dim)
image_t = _distort(image_t)
image_t = _pad_to_square(image_t,self.rgb_means, pad_image_flag)
image_t, boxes_t, landm_t = _mirror(image_t, boxes_t, landm_t)
height, width, _ = image_t.shape
image_t = _resize_subtract_mean(image_t, self.img_dim, self.rgb_means)
boxes_t[:, 0::2] /= width
boxes_t[:, 1::2] /= height
landm_t[:, 0::2] /= width
landm_t[:, 1::2] /= height
labels_t = np.expand_dims(labels_t, 1)
targets_t = np.hstack((boxes_t, landm_t, labels_t))
return image_t, targets_t