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Transform.py
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# encoding: utf-8
'''
mirror:probably is 0.5
distort:color jitter
expand:origin image expand to 4*4
crop:random crop
'''
from jaccard import *
# import random
#from PIL import Image, ImageEnhance, ImageOps, ImageFile
#import matplotlib.pyplot as plt
from scipy import misc
# import numpy as np
# import copy
def mirrot_anno(image, objects):
'''mirror the annoated info at the same time'''
height, width, channel = image.shape
coord = ['xmin', 'xmax', 'ymin', 'ymax']
for i in objects.keys():
if not (type(objects[i]) == dict and objects[i].has_key('bndbox')):
continue
xmin, xmax, ymin, ymax = [int(objects[i]['bndbox'][k]) for k in coord]
new_xmin = width - xmax
new_xmax = width - xmin
objects[i]['bndbox']['xmin'] = new_xmin
objects[i]['bndbox']['xmax'] = new_xmax
def jitter(data):
image, min_max = transfer(data)
image = Image.fromarray(image)
random_factor = np.random.randint(0, 31) / 10.
image = ImageEnhance.Color(image).enhance(random_factor)
random_factor = np.random.randint(5, 11) / 10.
image = ImageEnhance.Brightness(image).enhance(random_factor)
random_factor = np.random.randint(10, 21) / 10.
image = ImageEnhance.Contrast(image).enhance(random_factor)
'''
plt.subplot(121)
plt.imshow(image)
plt.subplot(122)
plt.imshow(contrast_image)
'''
image = np.array(image)
image = re_transfer(image, min_max)
return image
def expand(image, objects, sz, ratio=3):
"""
zoom out,but be careful:
if whiter will be applied, the function(whiter) should be carried out after expand()
ranther than before this function.for matters of Mean_value.
:param image:
:param objects:
:param sz:
:param ratio:
:return:
"""
height, width, channel = image.shape
aug_sz = (sz[0] * ratio, sz[1] * ratio)
mean_value = [104, 117, 123]
# build a canvus
canvus = np.zeros((aug_sz[0], aug_sz[0], channel), dtype="uint8")
# canvus_origin = np.zeros((aug_sz[0], aug_sz[0], channel), dtype="uint8") # origin image for show
for i in range(channel):
canvus[:, :, i] = mean_value[i]
# canvus_origin[:, :, i] = mean_value[i] # origin image for show
# insert the image
h_off = random.randint(0, aug_sz[0] - height)
w_off = random.randint(0, aug_sz[1] - width)
canvus[h_off:h_off + height, w_off:w_off + width, :] = image
# canvus_origin[h_off:h_off + height, w_off:w_off + width, :] = origin_image # origin image for show
# adjust the labels
new_objects = copy.deepcopy(objects)
coord = ['xmin', 'xmax', 'ymin', 'ymax']
for i in new_objects.keys():
if not (type(new_objects[i]) == dict and new_objects[i].has_key('bndbox')):
continue
coor = new_objects[i]['bndbox']
xmin, xmax, ymin, ymax = [int(coor[k]) for k in coord] # coor's coordination is num now,not a string
newCoor = [xmin + w_off, xmax + w_off, ymin + h_off, ymax + h_off]
for k, key in enumerate(coord):
coor[key] = newCoor[k]
canvus, new_objects = resize_imgAnno(sz, canvus, new_objects)
# canvus_origin, _ = resize_imgAnno(sz, canvus_origin, new_objects)
return [canvus, new_objects] # , canvus_origin] # image,lables,origin_image
def whiter(image):
"""
中心化和标准化处理，得到0均值、标准差为1的服从标准正态分布的数据
:param image: image.shape=[300,300,3];height,width,channel
:return: the data after whitering
"""
im=image.astype('float64')
data = np.zeros(image.shape).astype('float16')
w, h, c = image.shape
for i in range(c):
data[:, :, i] = (im[:, :, i] - np.mean(im[:, :, i])) / np.std(im[:, :, i])
return data
def transform(origin_image, objects):
"""
data augment
:param origin_image:
:param objects: annoated dict
:return:
"""
# whitening
image = whiter(origin_image)
whiter_image = image
#misc.imsave('save_whiter.jpg',image)
# image=origin_image
trans_dict = {}
objects = objects
# 1.distort$$
prob = random.randint(0, 1)
if prob > 0.5:
global image
image = jitter(image)
print 'distort'
#trans_dict['distort'] = [image.copy(),copy.deepcopy(objects)]
sz = (300, 300) # width & height -- order
# 2.expand image$$
prob = random.randint(0, 1)
if prob > 0.5:
global image
image, objects = expand(origin_image, objects, sz)
#misc.imsave('save_expand.jpg',image_expand)
#print 'expand_label'
#print lables_expand
global whiter_image
whiter_image=whiter(image)
print 'expand'
# trans_dict['expand'] = [whiter_image, objects, image]
# 3.origin image -- resize$$
prob = random.randint(1, 2)
if prob > 0:
global image
global whiter_image
image, objects = resize_imgAnno(sz, whiter_image, objects)
#misc.imsave('save_resize.jpg',image)
#print 'resize_label'
#print objects
origin_image, _ = resize_imgAnno(sz, origin_image, objects)
print 'resize'
# trans_dict['origin'] = [image.copy(), copy.deepcopy(objects), origin_image.copy()]
# 4.mirror$$
prob = random.randint(0, 1)
if prob > 0.5:
global image
image = image[:, ::-1]
origin_image = origin_image[:, ::-1]
mirrot_anno(image, objects)
print 'mirror'
trans_dict['mirror'] = [image.copy(), copy.deepcopy(objects), origin_image.copy()]
'''
# 5.jaccard$$
try:
dict_jaccard = corp_image(image, objects, sz, origin_image)
trans_dict = dict(trans_dict, **dict_jaccard)
print 'jaccard'
except Exception, e:
print "what's wrong:", e
'''
trans_dict['mirror'] = [image.copy(), copy.deepcopy(objects), origin_image.copy()]
return trans_dict