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import cv2
import numpy as np
from matplotlib.colors import rgb_to_hsv, hsv_to_rgb
def rand(a=0, b=1):
return np.random.rand()*(b-a) + a
def get_random_data (
annotation_line,
input_shape,
max_boxes=25,
scale=.3,
hue=.1,
sat=1.5,
val=1.5,
random=True
):
'''
random preprocessing for real-time data augmentation
'''
line = annotation_line.split('\t')
h, w = input_shape
box = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]])
image = cv2.imread(line[0])
ih, iw, ic = image.shape
if not random:
resize_scale = min(h/ih, w/iw)
nw = int(iw * resize_scale)
nh = int(ih * resize_scale)
max_offx = w - nw
max_offy = h - nh
dx = max_offx//2
dy = max_offy//2
to_x0, to_y0 = max(0, dx), max(0, dy)
from_x0, from_y0 = max(0, -dx), max(0, -dy)
wx, hy = min(w, dx+nw) - to_x0, min(h, dy+nh) - to_y0
# place image
image_data = np.zeros((*input_shape,ic), dtype='uint8') + 128
image_data[to_y0:to_y0+hy, to_x0:to_x0+wx, :] = cv2.resize(image, (nw, nh))[from_y0:from_y0+hy, from_x0:from_x0+wx, :]
flip = False
image_data = image_data/255.
else:
if np.random.uniform() >= 0.5:
# scale Up
resize_scale = 1. + scale * np.random.uniform()
resize_scale = max( h*resize_scale/ih, w*resize_scale/iw)
nw = int(iw * resize_scale)
nh = int(ih * resize_scale)
max_offx = nw - w
max_offy = nh - h
dx = int(np.random.uniform() * max_offx)
dy = int(np.random.uniform() * max_offy)
# resize and crop
image = cv2.resize(image, (nw, nh))
image_data = image[dy : (dy + h), dx : (dx + w), :]
dx, dy = (-dx, -dy)
else:
# scale down
mul = 1 if np.random.uniform() >= 0.5 else -1
resize_scale = 1. + mul * scale * np.random.uniform()
resize_scale = min( h*resize_scale/ih, w*resize_scale/iw)
nw = int(iw * resize_scale)
nh = int(ih * resize_scale)
max_offx = w - nw
max_offy = h - nh
dx = int(np.random.uniform() * max_offx)
dy = int(np.random.uniform() * max_offy)
to_x0, to_y0 = max(0, dx), max(0, dy)
from_x0, from_y0 = max(0, -dx), max(0, -dy)
wx, hy = min(w, dx+nw) - to_x0, min(h, dy+nh) - to_y0
# place image
image_data = np.zeros((*input_shape,ic), dtype='uint8') + 128
image_data[to_y0:to_y0+hy, to_x0:to_x0+wx, :] = cv2.resize(image, (nw, nh))[from_y0:from_y0+hy, from_x0:from_x0+wx, :]
flip = np.random.uniform() >= 0.5
if flip: image_data = image_data[:,::-1,:]
# distort color of the image
hue = rand(-hue, hue)
sat = rand(1, sat) if rand()<.5 else 1/rand(1, sat)
val = rand(1, val) if rand()<.5 else 1/rand(1, val)
x = rgb_to_hsv(np.array(image_data)/255.)
x[..., 0] += hue
x[..., 0][x[..., 0]>1] -= 1
x[..., 0][x[..., 0]<0] += 1
x[..., 1] *= sat
x[..., 2] *= val
x[x>1] = 1
x[x<0] = 0
image_data = hsv_to_rgb(x) # numpy array, 0 to 1
# correct boxes
box_data = np.zeros((max_boxes,5))
if len(box)>0:
np.random.shuffle(box)
box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx
box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy
if flip: box[:, [0,2]] = w - box[:, [2,0]]
box[:, 0:2][box[:, 0:2]<0] = 0
box[:, 2][box[:, 2]>w] = w
box[:, 3][box[:, 3]>h] = h
box_w = box[:, 2] - box[:, 0]
box_h = box[:, 3] - box[:, 1]
box = box[np.logical_and(box_w>1, box_h>1)] # discard invalid box
if len(box)>max_boxes: box = box[:max_boxes]
box_data[:len(box)] = box
return image_data, box_data
def data_generator(annotation_lines, batch_size, input_shape, random):
'''
data generator for fit_generator
'''
n = len(annotation_lines)
i = 0
while True:
image_data = []
box_data = []
for _ in range(batch_size):
image, box = get_random_data(annotation_lines[i], input_shape, max_boxes=50, random=random)
image_data.append(image)
box = box[np.sum(box, axis=1) != 0, :]
box_data.append(box)
i = (i+1) % n
image_data = np.array(image_data)
box_data = np.array(box_data)
yield image_data, box_data
def data_generator_wrapper(annotation_lines, batch_size, input_shape, random):
n = len(annotation_lines)
if n==0 or batch_size<=0: return None
return data_generator(annotation_lines, batch_size, input_shape, random) |