def randomcrop(img, side):
# crop sidexside randomly from given image.
h, w = img.shape[0:2]
minside = min(h, w)
scale = minside / side
# assert scale > 1
cs = chosen_scale = np.random.uniform(scale * .45, scale * .95)
img = vis.resize_perfect(img, h / cs, w / cs, cubic=True)
h, w = img.shape[0:2]
sh, sw = np.random.choice(h - side), np.random.choice(w - side)
cropped = img[sh:sh + side, sw:sw + side, :]
if np.random.uniform() > 0.5:
cropped = np.flip(cropped, axis=1)
# random brightness contrast
cropped = cropped.astype(np.float32)
cropped = cropped * (np.random.uniform(0.6, 1.7, size=(1, )) +
np.random.uniform(-.4, .4, size=(3, )))
cropped = cropped + np.random.uniform(-0.4, 0.4, size=(3, )) * 255
cropped = np.random.normal(loc=cropped, scale=np.random.uniform(3, 25))
cropped = np.clip(cropped, 0, 255)
cropped = cropped.astype(np.uint8)
return cropped
def load_image(self, path, is_filename=True, scale=1.0, target_width=None):
orig = load_image(path, is_filename)
# crop to face region
# from facial import facecrop
# orig = facecrop(orig)
if target_width is not None:
scale = target_width / orig.shape[1]
self.orig = orig
self.scale = scale
# set expected output h and w
self.orig_h, self.orig_w = orig.shape[0:2]
self.target_h, self.target_w = int(self.orig_h * scale), int(
self.orig_w * scale)
self.orig_hw = [self.orig_h, self.orig_w]
self.target_hw = [self.target_h, self.target_w]
# resize
target = vis.resize_perfect(self.orig,
self.target_h,
self.target_w,
cubic=True,
a=3)
# log
print(
'loading image {}, scale:{:2.2f}, orig[{}x{}], now[{}x{}]'.format(
path, scale, self.orig_w, self.orig_h, self.target_w,
self.target_h))
# floatify
target = (target / 255.).astype('float32')
target = self.preprocess(target)
if self.grayscale:
# b/w
target = np.einsum('ijk,kt->ijt', target,
np.array([[.3], [.6], [.1]]))
# clip to normal range
target = np.clip(target, 0, 1)
self.target = target
self.set_metric()
def load_image(self, path, is_filename=True, scale=1.0, target_width=None):
if is_filename:
# read image from file
orig = cv2.imread(path)
assert orig is not None
else:
# use in memory image
orig = path
if target_width is not None:
scale = target_width / orig.shape[1]
self.orig = orig
self.scale = scale
# set expected output h and w
self.orig_h, self.orig_w = orig.shape[0:2]
self.target_h, self.target_w = int(self.orig_h * scale), int(
self.orig_w * scale)
self.orig_hw = [self.orig_h, self.orig_w]
self.target_hw = [self.target_h, self.target_w]
# resize
target = vis.resize_perfect(self.orig,
self.target_h,
self.target_w,
cubic=True,
a=3)
# log
print(
'loading image {}, scale:{:2.2f}, orig[{}x{}], now[{}x{}]'.format(
path, scale, self.orig_w, self.orig_h, self.target_w,
self.target_h))
# floatify
target = (target / 255.).astype('float32')
# b/w
target = (target[:, :, 1:2] + target[:, :, 2:3]) * .5
# clip to normal range
target = np.clip(target * 1 + 0.1, 0, 1)
self.target = target
# loss metric precomputation
self.precomputated = self.loss_metric.prepare(self.target)
def jeffdemo():
jeff = load_image('../jeff.jpg')
h, w = jeff.shape[0:2]
jeff = vis.resize_perfect(jeff, 192, 192)
print(jeff.shape)
vis.show_autoscaled(jeff)
jefff = np.divide(jeff, 255., dtype=np.float32)
jeffff = forenet_infer(jefff)
print(jeffff.shape)
jeffff = np.transpose(jeffff, [2, 0, 1])
jeffff.shape += (1, )
vis.show_batch_autoscaled(jeffff * 0.5 + 0.5)
cv2.waitKey(0)
def resize(image, maxwidth):
cmw = max(image.shape[0:2])
return vis.resize_perfect(image, maxwidth, maxwidth, cubic=True, a=3)
# canvas width
w = 256
def newcanvas():
return np.ones((w, w, 1), dtype='uint8') * 255
mc = ManyConnected(w=w)
v = mc.to_vec()
print(type(v), v.shape)
mc.from_vec(v)
target = cv2.imread('hjt.jpg')
target = vis.resize_perfect(target, w, w, cubic=True, a=3)
target = (target / 255.).astype('float32')
target = (target[:, :, 1:2] + target[:, :, 2:3]) * .5
target = np.clip(target * 1 + 0.1, 0, 1)
def pyramid(img):
a = [img]
for i in range(5):
a.append(cv2.pyrDown(a[-1]))
return a
target_pyr = pyramid(target)
def update():
sess = get_session()
_, l = sess.run([updateop, loss])
return l
def getcanvas():
sess = get_session()
c = sess.run([canvas])[0]
return c[0]
return settarg, update, getcanvas
jeff = load_image('../jeff.jpg')
h,w = jeff.shape[0:2]
jeff = vis.resize_perfect(jeff, 192, 192)
print(jeff.shape)
settarg, update, getcanvas = feed_gen(jeff)
get_session().run(gvi())
settarg(jeff)
def show():
vis.show_autoscaled(jeff,name='jeff')
canvas = getcanvas()
vis.show_autoscaled(canvas)
cv2.waitKey(1)
show()