def print_histogram_plt_x_y(k, type, landmarks_arr):
import matplotlib.ticker as ticker
import random
image_utility = ImageUtility()
num_of_bins = 20
plt.figure()
for lndm in landmarks_arr:
landmark_arr_xy, landmark_arr_x, landmark_arr_y = image_utility.create_landmarks(landmarks=lndm,
scale_factor_x=1,
scale_factor_y=1)
data = landmark_arr_x
data = landmark_arr_y
# plt.plot(landmark_arr_x[0:32], landmark_arr_y[0:32], '-ok', c=color)
plt.hist(data, bins=num_of_bins, color='blue', edgecolor='blue', alpha=0.3)
# plt.hist2d(landmark_arr_x, landmark_arr_y, bins=num_of_bins, color='blue', edgecolor='black', alpha=0.5)
# plt.axis.set_major_formatter(ticker.PercentFormatter(xmax=len(landmark_arr_x)))
plt.savefig('histo_X_' + str(type) + '_' + str(k) + '.png', bbox_inches='tight')
plt.clf()
'''for Y'''
plt.figure()
for lndm in landmarks_arr:
landmark_arr_xy, landmark_arr_x, landmark_arr_y = image_utility.create_landmarks(landmarks=lndm,
scale_factor_x=1,
scale_factor_y=1)
data = landmark_arr_y
# plt.plot(landmark_arr_x[0:32], landmark_arr_y[0:32], '-ok', c=color)
plt.hist(data, bins=num_of_bins, color='green', edgecolor='green', alpha=0.3)
# plt.axis.set_major_formatter(ticker.PercentFormatter(xmax=len(data)))
plt.savefig('histo_Y_' + str(type) + '_' + str(k) + '.png', bbox_inches='tight')
plt.clf()
def print_arr(k, type, landmarks_arr):
import random
image_utility = ImageUtility()
plt.figure()
for lndm in landmarks_arr:
color = "#"+''.join([random.choice('0123456789ABCDEF') for j in range(6)])
landmark_arr_xy, landmark_arr_x, landmark_arr_y = image_utility.create_landmarks(landmarks=lndm,
scale_factor_x=1,
scale_factor_y=1)
if type == 'full':
plt.scatter(x=landmark_arr_x[:], y=landmark_arr_y[:], c=color, s=2)
elif type == 'face':
plt.scatter(x=landmark_arr_x[0:32], y=landmark_arr_y[0:32], c=color, s=5)
plt.plot(landmark_arr_x[0:32], landmark_arr_y[0:32], '-ok', c=color)
elif type == 'eyes':
plt.scatter(x=landmark_arr_x[60:75], y=landmark_arr_y[60:75], c=color, s=5)
plt.plot(landmark_arr_x[60:75], landmark_arr_y[60:75], '-ok', c=color)
elif type == 'nose':
plt.scatter(x=landmark_arr_x[51:59], y=landmark_arr_y[51:59], c=color, s=5)
plt.plot(landmark_arr_x[51:59], landmark_arr_y[51:59], '-ok', c=color)
elif type == 'mouth':
plt.scatter(x=landmark_arr_x[76:95], y=landmark_arr_y[76:95], c=color, s=5)
plt.plot(landmark_arr_x[76:95], landmark_arr_y[76:95], '-ok', c=color)
elif type == 'eyebrow':
plt.scatter(x=landmark_arr_x[33:50], y=landmark_arr_y[33:50], c=color, s=5)
plt.plot(landmark_arr_x[33:50], landmark_arr_y[33:50], '-ok', c=color)
plt.axis('off')
plt.savefig('name_' + str(type) + '_' + str(k) + '.png', bbox_inches='tight')
# plt.show()
plt.clf()
points_x_arr,
points_y_arr,
no_padding=False)
xx_s = new_xy_s[0::2]
yy_s = new_xy_s[1::2]
#image_utility.print_image_arr(i, crop, xx_s, yy_s)
resized_img = transform.resize(crop, (224, 224, 3), anti_aliasing=True)
dims = crop.shape
height = dims[0]
width = dims[1]
scale_factor_y = 224 / height
scale_factor_x = 224 / width
'''rescale and retrieve landmarks'''
landmark_arr_xy, landmark_arr_x, landmark_arr_y = \
image_utility.create_landmarks(landmarks=new_xy_s,
scale_factor_x=scale_factor_x,
scale_factor_y=scale_factor_y)
if not (min(landmark_arr_x) < 0.0 or min(landmark_arr_y) < 0.0
or max(landmark_arr_x) > 224 or max(landmark_arr_y) > 224):
#image_utility.print_image_arr(i, resized_img, landmark_arr_x, landmark_arr_y)
im = Image.fromarray((resized_img * 255).astype(np.uint8))
im.save(str(file_name_image) + png_file_name[i])
width = len(resized_img[0])
height = len(resized_img[1])
x_center = width / 2
y_center = height / 2
landmark_arr_flat_normalized = []
points_arr.append(x)
points_arr.append(y)
points_x_arr.append(x)
points_y_arr.append(y)
line = fp.readline()
cnt += 1
img = Image.open(img_file)
'''normalize image'''
resized_img = np.array(img) / 255.0
#resized_img = transform.resize(resized_img, (224, 224))
'''crop data: we add a small margin to the images'''
landmark_arr_xy, landmark_arr_x, landmark_arr_y = image_utility.create_landmarks(landmarks=points_arr,
scale_factor_x=1,
scale_factor_y=1)
'''augment the images, then normalize the landmarks based on the hyperface method'''
for k in range(IbugConf.augmentation_factor):
'''save the origin image as well'''
#print(k)
if k == 0:
landmark_arr_flat_aug = landmark_arr_xy
img_aug = resized_img
else:
'''save the augmented images'''
if k % 2 == 0:
#print(np.shape(resized_img))
landmark_arr_flat_aug, img_aug = image_utility.random_augmentation(landmark_arr_xy, resized_img)