import cv2
import numpy as np
import copy
def _rand_scale(scale):
scale = np.random.uniform(1, scale)
return scale if np.random.randint(2) == 0 else 1./scale
def _constrain(min_v, max_v, value):
if value < min_v:
return min_v
if value > max_v:
return max_v
return value
def random_flip(image, flip):
if flip == 1:
return cv2.flip(image, 1)
return image
def correct_bounding_boxes(boxes, new_w, new_h, net_w, net_h, dx, dy, flip, image_w, image_h):
boxes = copy.deepcopy(boxes)
# randomize boxes' order
np.random.shuffle(boxes)
# correct sizes and positions
sx, sy = float(new_w)/image_w, float(new_h)/image_h
zero_boxes = []
for i in range(len(boxes)):
boxes[i]['xmin'] = int(_constrain(0, net_w, boxes[i]['xmin']*sx + dx))
boxes[i]['xmax'] = int(_constrain(0, net_w, boxes[i]['xmax']*sx + dx))
boxes[i]['ymin'] = int(_constrain(0, net_h, boxes[i]['ymin']*sy + dy))
boxes[i]['ymax'] = int(_constrain(0, net_h, boxes[i]['ymax']*sy + dy))
if boxes[i]['xmax'] <= boxes[i]['xmin'] or boxes[i]['ymax'] <= boxes[i]['ymin']:
zero_boxes += [i]
continue
if flip == 1:
swap = boxes[i]['xmin']
boxes[i]['xmin'] = net_w - boxes[i]['xmax']
boxes[i]['xmax'] = net_w - swap
boxes = [boxes[i] for i in range(len(boxes)) if i not in zero_boxes]
return boxes
def random_distort_image(image, hue=18, saturation=1.5, exposure=1.5):
# determine scale factors
dhue = np.random.uniform(-hue, hue)
dsat = _rand_scale(saturation)
dexp = _rand_scale(exposure)
# convert RGB space to HSV space
image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV).astype('float')
# change satuation and exposure
image[:, :, 1] *= dsat
image[:, :, 2] *= dexp
# change hue
image[:, :, 0] += dhue
image[:, :, 0] -= (image[:, :, 0] > 180) * 180
image[:, :, 0] += (image[:, :, 0] < 0) * 180
# convert back to RGB from HSV
return cv2.cvtColor(image.astype('uint8'), cv2.COLOR_HSV2RGB)
def apply_random_scale_and_crop(image, new_w, new_h, net_w, net_h, dx, dy):
im_sized = cv2.resize(image, (new_w, new_h))
if dx > 0:
im_sized = np.pad(im_sized, ((0, 0), (dx, 0), (0, 0)), mode='constant', constant_values=127)
else:
im_sized = im_sized[:, -dx:, :]
if (new_w + dx) < net_w:
im_sized = np.pad(im_sized, ((0, 0), (0, net_w - (new_w+dx)), (0, 0)), mode='constant', constant_values=127)
if dy > 0:
im_sized = np.pad(im_sized, ((dy, 0), (0, 0), (0, 0)), mode='constant', constant_values=127)
else:
im_sized = im_sized[-dy:, :, :]
if (new_h + dy) < net_h:
im_sized = np.pad(im_sized, ((0, net_h - (new_h+dy)), (0, 0), (0, 0)), mode='constant', constant_values=127)
return im_sized[:net_h, :net_w, :]
