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testing.py
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from build_model import *
from load_data import *
from train import *
import numpy as np
import matplotlib.pyplot as plt
from random import *
import cv2
from CK_work import *
if __name__ == '__main__':
condition = 13
relight_cycle_generator = build_relight()
relight_cycle_generator.load_weights('weight_ck/ck_generator_part2_weights_13')
if condition == 0: #testing data random test
path = '/home/pomelo96/Desktop/datasets/Yaleb/test'
_, _, _, train_roots, train_id, train_light = load_YaleB()
input_image_roots, reference_image_roots, GT_image_roots, id_class \
= set_data_for_cycleGAN(train_roots, train_light, is_pretrain=False)
for i in range(10):
source_sampling = load_image(get_batch_data(input_image_roots, i, 10))
reference_sampling = load_image(get_batch_data(reference_image_roots, i, 10))
gt_sampling = load_image(get_batch_data(GT_image_roots, i, 10))
label = get_batch_data(id_class, i, 10)
gen_imgs_1 = relight_cycle_generator.predict(tf.concat([source_sampling, reference_sampling], axis=-1))
gen_imgs_2 = relight_cycle_generator.predict(tf.concat([gen_imgs_1, source_sampling], axis=-1))
gen_imgs_1 = 0.5 * (gen_imgs_1 + 1)
gen_imgs_2 = 0.5 * (gen_imgs_2 + 1)
reference_sampling = 0.5 * (reference_sampling+1)
source_sampling = 0.5 * (source_sampling+1)
gt_sampling = 0.5 * (gt_sampling+1)
r, c = 5, 10
fig, axs = plt.subplots(r, c, sharex='col', sharey='row', figsize=(25, 25))
plt.subplots_adjust(hspace=0.2)
cnt = 0
for k in range(c):
axs[0, k].imshow(source_sampling[cnt], cmap='gray')
axs[0, k].axis('off')
axs[1, k].imshow(gen_imgs_1[cnt], cmap='gray')
axs[1, k].axis('off')
axs[2, k].imshow(gt_sampling[cnt], cmap='gray')
axs[2, k].axis('off')
axs[3, k].imshow(reference_sampling[cnt], cmap='gray')
axs[3, k].axis('off')
axs[4, k].imshow(gen_imgs_2[cnt], cmap='gray')
axs[4, k].axis('off')
cnt += 1
fig.savefig('picture/cond0/test_{}.jpg'.format(i))
plt.close()
#-------------------------------------------------------------------------------------#
elif condition==1: #Input: ID any light any -> Output ID any light same
for target_light in range(6, 11):
source, reference, gt = load_img_cond1(target_light_type=target_light, train=False)
for i in range(10):
source_ = source[6 * i:6 * (i + 1)]
start = randint(1, 5)
reference_index = [(start + i) % 6 for i in range(6)]
reference_ = reference[reference_index]
gen_imgs = relight_cycle_generator.predict(tf.concat([source_, reference_], axis=-1))
gen_imgs = 0.5 * (gen_imgs + 1)
source_ = 0.5 * (source_ + 1)
reference_ = 0.5 * (reference_ + 1)
gt_ = 0.5 * (gt + 1)
#for j in range(4):
r, c = 4, 6
fig, axs = plt.subplots(r, c, sharex='col', sharey='row', figsize=(25, 25))
plt.subplots_adjust(hspace=0.2)
cnt = 0
for k in range(c):
axs[0, k].imshow(source_[cnt], cmap='gray')
axs[0, k].axis('off')
axs[1, k].imshow(gen_imgs[cnt], cmap='gray')
axs[1, k].axis('off')
axs[2, k].imshow(gt_[cnt], cmap='gray')
axs[2, k].axis('off')
axs[3, k].imshow(reference_[cnt], cmap='gray')
axs[3, k].axis('off')
cnt += 1
fig.savefig('picture/cond1/test_T{}_S{}.png'.format(target_light, i))
plt.close()
elif condition == 2: #source light = reference light => predict must same with source
for target_light_type in range(11):
source = load_light_type(target_light_type, train=False)
start = randint(1, 5)
reference_index = [(start+i) % 6 for i in range(6)]
reference = source[reference_index]
gen_imgs = relight_cycle_generator.predict(tf.concat([source, reference], axis=-1))
gen_imgs = 0.5 * (gen_imgs + 1)
source_ = 0.5 * (source + 1)
reference = 0.5 * (reference + 1)
r, c = 3, 6
fig, axs = plt.subplots(r, c, sharex='col', sharey='row', figsize=(25, 25))
plt.subplots_adjust(hspace=0.2)
cnt = 0
for k in range(c):
axs[0, k].imshow(source[cnt], cmap='gray')
axs[0, k].axis('off')
axs[1, k].imshow(gen_imgs[cnt], cmap='gray')
axs[1, k].axis('off')
axs[2, k].imshow(reference[cnt], cmap='gray')
axs[2, k].axis('off')
cnt += 1
fig.savefig('picture/cond2/test_T{}.png'.format(target_light_type))
plt.close()
elif condition == 3: #same id with all lights -> same id same 1light
for id_source in range(6):
for target_light in range(11):
source = load_id(id_source, train=False)
reference = load_light_type(target_light, train=True)
gt = reference[id_source]
reference = np.delete(reference, [id_source], axis=0)
start = randint(1, 5)
reference_index = [(start + i) % 6 for i in range(11)]
reference_ = reference[reference_index]
gen_imgs = relight_cycle_generator.predict(tf.concat([source, reference_], axis=-1))
gen_imgs = 0.5 * (gen_imgs + 1)
source = 0.5 * (source + 1)
reference_ = 0.5 * (reference_ + 1)
r, c = 4, 6
fig, axs = plt.subplots(r, c, sharex='col', sharey='row', figsize=(25, 25))
plt.subplots_adjust(hspace=0.2)
cnt = 0
for k in range(c):
axs[0, k].imshow(source[cnt], cmap='gray')
axs[0, k].axis('off')
axs[1, k].imshow(gen_imgs[cnt], cmap='gray')
axs[1, k].axis('off')
axs[2, k].imshow(gt, cmap='gray')
axs[2, k].axis('off')
axs[3, k].imshow(reference_[cnt] , cmap='gray')
axs[3, k].axis('off')
cnt += 1
fig.savefig('picture/cond3/test_ID{}_light{}.png'.format(id_source, target_light))
plt.close()
elif condition == 4: #reference all black or all white
for id_source in range(6):
source = load_id(id_source, train=False)
reference = np.ones_like(source)
inputs = tf.concat([source, reference], axis=-1)
gen_imgs = relight_cycle_generator.predict(tf.concat([source, reference], axis=-1))
gen_imgs = 0.5 * (gen_imgs + 1)
source = 0.5 * (source + 1)
r, c = 2, 11
fig, axs = plt.subplots(r, c, sharex='col', sharey='row', figsize=(25, 25))
plt.subplots_adjust(hspace=0.2)
cnt = 0
for k in range(c):
axs[0, k].imshow(source[cnt], cmap='gray')
axs[0, k].axis('off')
axs[1, k].imshow(gen_imgs[cnt], cmap='gray')
axs[1, k].axis('off')
cnt += 1
fig.savefig('picture/cond4/test_white_ID{}.png'.format(id_source))
plt.close()
elif condition == 5: #model work in CK dataset
for target_light in range(11):
source = load_ck()
reference = load_light_type(target_light, train=True)
reference_index = [i for i in range(reference.shape[0])]
shuffle(reference_index)
reference_index = reference_index[:5]
reference = reference[reference_index]
inputs = tf.concat([source, reference], axis=-1)
gen_imgs = relight_cycle_generator.predict(tf.concat([source, reference], axis=-1))
gen_imgs = 0.5 * (gen_imgs + 1)
source = 0.5 * (source + 1)
reference = 0.5 * (reference + 1)
r, c = 3, 5
fig, axs = plt.subplots(r, c, sharex='col', sharey='row', figsize=(25, 25))
plt.subplots_adjust(hspace=0.2)
cnt = 0
for k in range(c):
axs[0, k].imshow(source[cnt], cmap='gray')
axs[0, k].axis('off')
axs[1, k].imshow(gen_imgs[cnt], cmap='gray')
axs[1, k].axis('off')
axs[2, k].imshow(reference[cnt], cmap='gray')
axs[2, k].axis('off')
cnt += 1
fig.savefig('picture/cond5/target_light{}.png'.format(target_light))
plt.close()
elif condition == 6: #visualize saliency map
vsn = build_vsn()
vsn.load_weights('weight/vsn_weights_21')
for i in range(32):
source = load_id(i, train=True)
source = 0.5 * (source + 1)
att, _ = vsn.predict(source)
att = np.mean(att, axis=-1)
r, c = 2, 11
fig, axs = plt.subplots(r, c, sharex='col', sharey='row', figsize=(30, 30))
plt.subplots_adjust(hspace=0.2)
cnt = 0
for k in range(c):
axs[0, k].imshow(source[cnt], cmap='gray')
axs[0, k].axis('off')
img = np.reshape(att[cnt], (32,32,1))
axs[1, k].imshow(cv2.resize(img, (128,128)), cmap='gray')
axs[1, k].axis('off')
cnt += 1
fig.savefig('picture/cond6/ID_{}.png'.format(i))
plt.close()
elif condition == 7:
img_path = os.listdir('affine_lab_face')
source = []
for name in img_path:
img = cv2.imread('affine_lab_face/' + name)
img = cv2.resize(img, (128,128))
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = np.expand_dims(img, axis=-1)
source.append(img)
source = np.array(source)
source = source/127.5 - 1
natural_roots, _, expression_roots, _ = load_CK(train=True)
natural_reference = random.sample(natural_roots, 4)
expression_referencce = random.sample(expression_roots, 4)
natural_reference_img = load_image(natural_reference)
expression_reference_img = load_image(expression_referencce)
inputs_natural = tf.concat([source, natural_reference_img], axis=-1)
gen_imgs_natural = relight_cycle_generator.predict(inputs_natural)
inputs_expression = tf.concat([source, expression_reference_img], axis=-1)
gen_imgs_expression = relight_cycle_generator.predict(inputs_expression)
source = 0.5 * (source + 1)
natural_reference_img = 0.5 * (natural_reference_img + 1)
gen_imgs_natural = 0.5 * (gen_imgs_natural + 1)
expression_reference_img = 0.5 * (expression_reference_img + 1)
gen_imgs_expression = 0.5 * (gen_imgs_expression + 1)
r, c = 3, 4
fig, axs = plt.subplots(r, c, sharex='col', sharey='row', figsize=(30, 30))
plt.subplots_adjust(hspace=0.2)
cnt = 0
for k in range(c):
axs[0, k].imshow(source[cnt], cmap='gray')
axs[0, k].axis('off')
axs[1, k].imshow(gen_imgs_natural[cnt], cmap='gray')
axs[1, k].axis('off')
axs[2, k].imshow(natural_reference_img[cnt], cmap='gray')
axs[2, k].axis('off')
cnt += 1
fig.savefig('picture/cond7/lab_natural.jpg')
plt.close(fig)
r, c = 3, 4
fig, axs = plt.subplots(r, c, sharex='col', sharey='row', figsize=(30, 30))
plt.subplots_adjust(hspace=0.2)
cnt = 0
for k in range(c):
axs[0, k].imshow(source[cnt], cmap='gray')
axs[0, k].axis('off')
axs[1, k].imshow(gen_imgs_expression[cnt], cmap='gray')
axs[1, k].axis('off')
axs[2, k].imshow(expression_reference_img[cnt], cmap='gray')
axs[2, k].axis('off')
cnt += 1
fig.savefig('picture/cond7/lab_expression.jpg')
elif condition == 8: #random show ck work on testing data
test_natural_roots, test_id_label_natural, \
test_expression_roots, test_id_label_expression = load_CK(train=False)
test_input_image_roots, test_reference_image_roots, test_GT_image_roots, test_id_class \
= cycle_dataset_ck(test_natural_roots, test_id_label_natural,
test_expression_roots, test_id_label_expression)
for i in range(10):
source = load_image(get_batch_data(test_input_image_roots, i, 10))
reference = load_image(get_batch_data(test_reference_image_roots, i, 10))
gt = load_image(get_batch_data(test_GT_image_roots, i, 10))
inputs_1 = tf.concat([source, reference], -1)
gen_imgs_1 = relight_cycle_generator.predict(inputs_1)
inputs_2 = tf.concat([gen_imgs_1, source], -1)
gen_imgs_2 = relight_cycle_generator.predict(inputs_2)
# Rescale images 0 - 1
source = 0.5 * (source + 1)
reference = 0.5 * (reference + 1)
gt = 0.5 * (gt + 1)
gen_imgs_1 = 0.5 * (gen_imgs_1 + 1)
gen_imgs_2 = 0.5 * (gen_imgs_2 + 1)
r, c = 5, 10
fig, axs = plt.subplots(r, c, sharex='col', sharey='row', figsize=(25, 25))
plt.subplots_adjust(hspace=0.2)
cnt = 0
for j in range(c):
axs[0, j].imshow(source[cnt], cmap='gray')
axs[0, j].axis('off')
axs[1, j].imshow(gen_imgs_2[cnt], cmap='gray')
axs[1, j].axis('off')
axs[2, j].imshow(gen_imgs_1[cnt], cmap='gray')
axs[2, j].axis('off')
axs[3, j].imshow(gt[cnt], cmap='gray')
axs[3, j].axis('off')
axs[4, j].imshow(reference[cnt], cmap='gray')
axs[4, j].axis('off')
cnt += 1
fig.savefig('picture/cond8/total_test_{}'.format(i))
plt.close()
elif condition == 9: #light transform total result on testing
for id_source in range(6):
for target_light in range(11):
source = load_id(id_source, train=False)
reference = load_light_type(target_light, train=False)
gt = reference[id_source]
reference = np.delete(reference, [id_source], axis=0)
start = randint(1, 4)
reference_index = [(start + i) % 5 for i in range(11)]
reference_ = reference[reference_index]
gen_imgs = relight_cycle_generator.predict(tf.concat([source, reference_], axis=-1))
gen_imgs_2 = relight_cycle_generator.predict(tf.concat([gen_imgs, source], axis=-1))
gen_imgs = 0.5 * (gen_imgs + 1)
gen_imgs_2 = 0.5 * (gen_imgs_2 + 1)
source = 0.5 * (source + 1)
reference_ = 0.5 * (reference_ + 1)
r, c = 5, 6
fig, axs = plt.subplots(r, c, sharex='col', sharey='row', figsize=(25, 25))
plt.subplots_adjust(hspace=0.2)
cnt = 0
for k in range(c):
axs[0, k].imshow(source[cnt], cmap='gray')
axs[0, k].axis('off')
axs[1, k].imshow(gen_imgs_2[cnt], cmap='gray')
axs[1, k].axis('off')
axs[2, k].imshow(gen_imgs[cnt], cmap='gray')
axs[2, k].axis('off')
axs[3, k].imshow(gt, cmap='gray')
axs[3, k].axis('off')
axs[4, k].imshow(reference_[cnt] , cmap='gray')
axs[4, k].axis('off')
cnt += 1
fig.savefig('picture/cond9/20_final_test_ID{}_light{}.png'.format(id_source, target_light))
plt.close()
elif condition == 10: #final test result on lab photo
for light_type in range(11):
img_path = os.listdir('affine_lab_face')
source = []
for name in img_path:
img = cv2.imread('affine_lab_face/' + name)
img = cv2.resize(img, (128, 128))
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = np.expand_dims(img, axis=-1)
source.append(img)
source = np.array(source)
source = source / 127.5 - 1
reference = load_light_type(light_type, train=True)
reference_index = [i for i in range(len(reference))]
random.shuffle(reference_index)
reference = reference[reference_index[:4]]
gen_imgs = relight_cycle_generator.predict(tf.concat([source, reference], axis=-1))
gen_imgs_2 = relight_cycle_generator.predict(tf.concat([gen_imgs, source], axis=-1))
source = 0.5 * (source + 1)
reference = 0.5 * (reference + 1)
gen_imgs = 0.5 * (gen_imgs + 1)
gen_imgs_2 = 0.5 * (gen_imgs_2 + 1)
r, c = 4, 4
fig, axs = plt.subplots(r, c, sharex='col', sharey='row', figsize=(25, 25))
plt.subplots_adjust(hspace=0.2)
cnt = 0
for k in range(c):
axs[0, k].imshow(source[cnt], cmap='gray')
axs[0, k].axis('off')
axs[1, k].imshow(gen_imgs_2[cnt], cmap='gray')
axs[1, k].axis('off')
axs[2, k].imshow(gen_imgs[cnt], cmap='gray')
axs[2, k].axis('off')
axs[3, k].imshow(reference[cnt], cmap='gray')
axs[3, k].axis('off')
cnt += 1
fig.savefig('picture/cond10/lab_light{}.png'.format(light_type))
plt.close()
elif condition == 11: #1st: source:ID1 L1, reference: ID2~6 L2~11
id = 0
source = load_id(id, train=False)
for light_type in range(11):
reference = load_light_type(light_type, train=False)
reference = np.delete(reference, [id], axis=0)
source_ = source[light_type]
source__ = np.zeros_like(reference)
for i in range(source__.shape[0]):
source__[i] = source_
gen_img = relight_cycle_generator.predict(tf.concat([source__, reference], axis=-1))
r, c = 3, 5
fig, axs = plt.subplots(r, c, sharex='col', sharey='row', figsize=(25, 25))
plt.subplots_adjust(hspace=0.2)
cnt = 0
source__ = 0.5 * (source__ + 1)
reference = 0.5 * (reference + 1)
gen_img = 0.5 * (gen_img + 1)
for k in range(c):
axs[0, k].imshow(source__[cnt], cmap='gray')
axs[0, k].axis('off')
axs[1, k].imshow(gen_img[cnt], cmap='gray')
axs[1, k].axis('off')
axs[2, k].imshow(reference[cnt], cmap='gray')
axs[2, k].axis('off')
cnt += 1
fig.savefig('picture/cond11/1_ID{}_light{}.png'.format(id, light_type))
plt.close()
#2nd source: ID1 L1, reference:ID1 L2~11
reference = np.delete(source, [light_type], axis=0)
source_ = np.zeros_like(reference)
for i in range(source_.shape[0]):
source_[i] = source[light_type]
gen_img = relight_cycle_generator.predict(tf.concat([source_, reference], axis=-1))
source_ = 0.5 * (source_ + 1)
reference = 0.5 * (reference + 1)
gen_img = 0.5 * (gen_img + 1)
r, c = 3, 5
fig, axs = plt.subplots(r, c, sharex='col', sharey='row', figsize=(25, 25))
plt.subplots_adjust(hspace=0.2)
cnt = 0
source__ = 0.5 * (source__ + 1)
reference = 0.5 * (reference + 1)
gen_img = 0.5 * (gen_img + 1)
for k in range(c):
axs[0, k].imshow(source_[cnt], cmap='gray')
axs[0, k].axis('off')
axs[1, k].imshow(gen_img[cnt], cmap='gray')
axs[1, k].axis('off')
axs[2, k].imshow(reference[cnt], cmap='gray')
axs[2, k].axis('off')
cnt += 1
fig.savefig('picture/cond11/2_ID{}_light{}.png'.format(id, light_type))
plt.close()
elif condition == 12: #all id in ck testing data N2E
path = '/home/pomelo96/Desktop/datasets/classifier_alignment_CK/test'
#N -> E
for id_ in range(33):
source_root = []
reference_root = []
natural_path = path + '/Natural image'
natural_file = os.listdir(natural_path) #S001, S002, ...
natural_file.sort()
natural_id_file = natural_path + '/' + natural_file[id_] #id_th ppl od natural expression
natural_id_file = natural_id_file + '/' + os.listdir(natural_id_file)[0]
natural_id_img_name = os.listdir(natural_id_file)
for img_name in natural_id_img_name:
source_root.append(natural_id_file + '/' + img_name)
expression_path = path + '/Expression image'
expression_file = os.listdir(expression_path)
expression_file.sort()
del expression_file[id_]
for other_id in expression_file:
expression_id_file = expression_path + '/' + other_id
expression_id_file = expression_id_file + '/' + os.listdir(expression_id_file)[0]
expression_id_img_name = os.listdir(expression_id_file)
for img_name in expression_id_img_name:
reference_root.append(expression_id_file + '/' + img_name)
random.shuffle(reference_root)
reference_root = reference_root[:len(source_root)]
source = load_image(source_root)
reference = load_image(reference_root)
gen_img1 = relight_cycle_generator.predict(tf.concat([source, reference], -1))
gen_img2 = relight_cycle_generator.predict(tf.concat([gen_img1, source], -1))
source = 0.5 * (source + 1)
reference = 0.5 * (reference + 1)
gen_img1 = 0.5 * (gen_img1 + 1)
gen_img2 = 0.5 * (gen_img2 + 1)
r, c = 4, source.shape[0]
fig, axs = plt.subplots(r, c, sharex='col', sharey='row', figsize=(25, 25))
plt.subplots_adjust(hspace=0.2)
cnt = 0
for k in range(c):
axs[0, k].imshow(source[cnt], cmap='gray')
axs[0, k].axis('off')
axs[1, k].imshow(gen_img2[cnt], cmap='gray')
axs[1, k].axis('off')
axs[2, k].imshow(gen_img1[cnt], cmap='gray')
axs[2, k].axis('off')
axs[3, k].imshow(reference[cnt], cmap='gray')
axs[3, k].axis('off')
cnt += 1
fig.savefig('picture/cond12/{}_N2E.png'.format(natural_file[id_]))
plt.close()
elif condition == 13: #all id in ck testing data E2N
path = '/home/pomelo96/Desktop/datasets/classifier_alignment_CK/test'
#N -> E
for id_ in range(33):
source_root = []
reference_root = []
expression_path = path + '/Expression image'
expression_file = os.listdir(expression_path) #S001, S002, ...
expression_file.sort()
expression_id_file = expression_path + '/' + expression_file[id_] #id_th ppl od natural expression
expression_id_file = expression_id_file + '/' + os.listdir(expression_id_file)[0]
expression_id_img_name = os.listdir(expression_id_file)
for img_name in expression_id_img_name:
source_root.append(expression_id_file + '/' + img_name)
natural_path = path + '/Natural image'
natural_file = os.listdir(natural_path)
natural_file.sort()
del natural_file[id_]
for other_id in natural_file:
natural_id_file = natural_path + '/' + other_id
natural_id_file = natural_id_file + '/' + os.listdir(natural_id_file)[0]
natural_id_img_name = os.listdir(natural_id_file)
for img_name in natural_id_img_name:
reference_root.append(natural_id_file + '/' + img_name)
random.shuffle(reference_root)
reference_root = reference_root[:len(source_root)]
source = load_image(source_root)
reference = load_image(reference_root)
gen_img1 = relight_cycle_generator.predict(tf.concat([source, reference], -1))
gen_img2 = relight_cycle_generator.predict(tf.concat([gen_img1, source], -1))
source = 0.5 * (source + 1)
reference = 0.5 * (reference + 1)
gen_img1 = 0.5 * (gen_img1 + 1)
gen_img2 = 0.5 * (gen_img2 + 1)
r, c = 4, source.shape[0]
fig, axs = plt.subplots(r, c, sharex='col', sharey='row', figsize=(25, 25))
plt.subplots_adjust(hspace=0.2)
cnt = 0
for k in range(c):
axs[0, k].imshow(source[cnt], cmap='gray')
axs[0, k].axis('off')
axs[1, k].imshow(gen_img2[cnt], cmap='gray')
axs[1, k].axis('off')
axs[2, k].imshow(gen_img1[cnt], cmap='gray')
axs[2, k].axis('off')
axs[3, k].imshow(reference[cnt], cmap='gray')
axs[3, k].axis('off')
cnt += 1
fig.savefig('picture/cond13/{}_E2N.png'.format(expression_file[id_]))
plt.close()