def _get_one_triplet(input_data, input_labels):
input_labels = np.array(input_labels)
index = np.random.choice(n_labels, 2, replace=False)
label_positive = index[0]
label_negative = index[1]
indexes = utils.get_index(input_labels, index[0])
np.random.shuffle(indexes)
# print(indexes[0])
data_anchor = input_data[indexes[0], :, :, :]
data_anchor = utils.prewhiten(data_anchor)
data_anchor = utils.flip(data_anchor, random_flip=True)
data_anchor = utils.random_crop(data_anchor, image_size=299)
data_anchor = utils.random_rotate_image(data_anchor)
data_positive = input_data[indexes[1], :, :, :]
data_positive = utils.prewhiten(data_positive)
data_positive = utils.flip(data_positive, random_flip=True)
data_positive = utils.random_crop(data_positive, image_size=299)
data_positive = utils.random_rotate_image(data_positive)
indexes = utils.get_index(input_labels, index[1])
# print(indexes)
np.random.shuffle(indexes)
data_negative = input_data[indexes[0], :, :, :]
data_negative = utils.prewhiten(data_negative)
data_negative = utils.flip(data_negative, random_flip=True)
data_negative = utils.random_crop(data_negative, image_size=299)
data_negative = utils.random_rotate_image(data_negative)
# print(np.shape(data_negative))
return data_anchor, data_positive, data_negative, \
label_positive, label_positive, label_negative
def correct_img_torch(x_s, scale, r, s, device, for_dag = True, eps = 1e-9, pad='circular'):
conv_shape = (s.shape[2] + r.shape[2] - 1, s.shape[3] + r.shape[3] - 1)
S = utils.fft2(s/s.sum(), conv_shape[1], conv_shape[0])
R = utils.fft2(utils.flip(r)/r.sum(), conv_shape[1], conv_shape[0])
Q_unscaled = utils.mul_complex(R, S)
q_unscaled = torch.irfft(Q_unscaled, signal_ndim=2, normalized=False, onesided=False)
q = q_unscaled[:,:,np.mod(q_unscaled.shape[2], scale)::scale, np.mod(q_unscaled.shape[3], scale)::scale]
Q = torch.rfft(q, signal_ndim=2, normalized=False, onesided=False)
# Q_star = utils.conj(Q)
# abs2_Q = utils.abs2(Q)
# H = torch.cat( (Q_star[:,:,:,:,0:1]/(abs2_Q[:,:,:,:,0:1] + eps), Q_star[:,:,:,:,1:2]/(abs2_Q[:,:,:,:,0:1] + eps)), dim=4)
H = utils.inv_complex(Q, eps)
h_ = torch.irfft(H, signal_ndim=2, normalized=False, onesided=False)
h = utils.roll_y(utils.roll_x(h_/h_.sum(), -1), -1)
x_h = utils.filter_2D_torch(x_s, utils.flip(h), device, pad=pad)
if(for_dag):
x_h = utils.bicubic_up(x_h, scale, device)
x_h = utils.downsample_bicubic(x_h, scale, device)
return x_h
def _main_():
args = argparser.parse_args()
config_path = args.config
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
weights_path = args.weights_path
sm_model = SMModel(config['model'])
sm_model.model.summary()
sm_model.model.load_weights(weights_path)
test_generator = DataGenerator(config=config['test'],
preprocessing=sm_model.preprocessing,
n_class=sm_model.n_class,
split='test')
encoded_pixels = []
image_id_class_id = []
for X, filenames in tqdm(list(test_generator)):
preds = sm_model.model.predict_on_batch(X)
if config['test']['tta']:
for flip_type in ['ud', 'lr', 'udlr']:
X_temp = flip(X.copy(), flip_type)
pred_temp = sm_model.model.predict_on_batch(X_temp)
preds += flip(pred_temp, flip_type)
preds /= 4
preds = postprocess(preds, config['postprocess'], True)
for i in range(len(preds)):
for j in range(4):
encoded_pixels.append(run_length_encode(preds[i, :, :, j]))
image_id_class_id.append(filenames[i] + '_{}'.format(j + 1))
df = pd.DataFrame(data=encoded_pixels,
index=image_id_class_id,
columns=['EncodedPixels'])
df.index.name = 'ImageId_ClassId'
df.to_csv('submission.csv')
def valid_epoch(self):
self.net.eval()
gt = []
pred = []
for i, (X1, X2, y_true) in enumerate(self.verifyLoader):
if cuda.is_available():
X1, X2, y_true = X1.cuda(), X2.cuda(), y_true.cuda()
feat1 = torch.cat([self.net(X1), self.net(flip(X1))],
dim=1).view(-1)
feat2 = torch.cat([self.net(X2), self.net(flip(X2))],
dim=1).view(-1)
# cosine = distCosine(feat1, feat2)
dist = torch.exp(-torch.norm(feat1 - feat2)**2)
gt += [y_true]
pred += [dist.detach()]
gt = torch.cat(list(map(lambda x: x, gt)), dim=0).cpu().numpy()
pred = torch.cat(list(map(lambda x: x.unsqueeze(0), pred)),
dim=0).cpu().numpy()
thresh, acc, f1 = cvSelectThreshold(pred, gt)
return thresh, acc, f1
def rotateImageCallBack():
data.matrix = utils.flip(data.matrix)
data.frameA = utils.flip(data.frameA)
data.frameB = utils.flip(data.frameB)
img = Image.fromarray(data.matrix)
data.image = img
logScreen("Imagen rotada.")
previewImageCallBack(1)
def main(datapath='../../data/lfw-Aligned',
modelpath='../MobileFacenet_best.pkl',
thresh=0.3):
dataset = LFWPairs(datapath=datapath)
dataloader = DataLoader(dataset)
net = load_net(modelpath) # TODO
if cuda.is_available(): net.cuda()
f = open("../../cosine_score_py.txt", 'w')
gt = []
pred = []
print('\033[2J')
print('\033[1;1H')
net.eval()
for i, (X1, X2, y_true) in enumerate(dataloader):
if cuda.is_available():
X1 = X1.cuda()
X2 = X2.cuda()
y_true = y_true.cuda()
feat1 = torch.cat([net.get_feature(X1),
net.get_feature(flip(X1))],
dim=1).view(-1)
feat2 = torch.cat([net.get_feature(X2),
net.get_feature(flip(X2))],
dim=1).view(-1)
cosine = distCosine(feat1, feat2)
gt += [y_true.cpu().numpy()]
pred += [cosine.detach().cpu().numpy()]
line = "{:d} {:d} {:f}\n".format(i, int(gt[i]), float(pred[i]))
f.write(line)
print('\033[0;0H\033[K')
print('[{:4d}]/[{:4d}] {:s}'.format(i, len(dataset), line))
gt = np.array(gt)
pred = np.array(pred)
pd = np.zeros(len(pred))
pd[pred > thresh] = 1
gt = gt.reshape(-1)
pd = pd.reshape(-1)
acc = accuracy_score(gt, pd)
p = precision_score(gt, pd)
r = recall_score(gt, pd)
print('accuracy: {:.4f}, precision: {:.4f}, recall: {:.4f}'.format(
acc, p, r))
f.close()
def new_split(self, rect):
if rect.w > MIN_BSP_SIZE * 2 and rect.h > MIN_BSP_SIZE * 2:
if utils.flip() and rect.x + rect.w / 2 + MIN_BSP_SIZE < len(
self.map) and rect.y + rect.h / 2 + MIN_BSP_SIZE < len(self.map[0]):
x = libtcod.random_get_int(0, rect.x + MIN_BSP_SIZE, rect.x + rect.w - MIN_BSP_SIZE)
w = (rect.x + rect.w) - x
baby = Rect(rect.w - w, rect.h, rect.x, rect.y)
baby.bsp(rect)
baby_2 = Rect(w - 1, rect.h, x, rect.y)
baby_2.bsp(rect)
rect.babies.append(baby)
rect.babies.append(baby_2)
else:
y = libtcod.random_get_int(0, rect.y + MIN_BSP_SIZE, rect.y + rect.h - MIN_BSP_SIZE)
h = (rect.y + rect.h) - y
baby = Rect(rect.w, rect.h - h, rect.x, rect.y)
baby.bsp(rect)
baby_2 = Rect(rect.w, h - 1, rect.x, y)
baby_2.bsp(rect)
rect.babies.append(baby)
rect.babies.append(baby_2)
else:
rect.end = True
for baby in rect.babies:
if baby.end != True:
self.new_split(baby)
def epsilon_linear_policy(self, epsilon, w, s):
best = np.argmax(
[np.dot(w, self.phi(s, a)) for a in range(self.nactions)])
if flip(epsilon):
return pr.choice(self.actions)
else:
return best
def split(self, rect):
if rect.w - 1 >= MIN_BSP_SIZE * 1.5 or rect.h - 1 >= MIN_BSP_SIZE * 1.5:
if utils.flip() and rect.w >= MIN_BSP_SIZE:
x = libtcod.random_get_int(0, rect.x + MIN_BSP_SIZE, rect.x + rect.w - MIN_BSP_SIZE)
w = (rect.x + rect.w) - x
tries = 0
while x + w >= len(self.map) or tries <= 3:
print x, w, " horz out of bounds"
x = rect.x + 1
w = rect.w / 2 - 1
baby = Rect(rect.w - w, rect.h, rect.x, rect.y)
baby.bsp(rect)
baby_2 = Rect(w, rect.h, x, rect.y)
baby_2.bsp(rect)
rect.babies.append(baby)
rect.babies.append(baby_2)
else:
if rect.w < MIN_BSP_SIZE:
y = libtcod.random_get_int(0, rect.y + MIN_BSP_SIZE, rect.y + rect.h - MIN_BSP_SIZE)
h = (rect.y + rect.h) - y
tries = 0
while y + h > len(self.map[0]) or tries <= 3:
print y, h, "vert out of bounds"
y = rect.y + 1
h = rect.h / 2
baby = Rect(rect.w, rect.h - h, rect.x, rect.y)
baby.bsp(rect)
rect.babies.append(baby)
else:
y = libtcod.random_get_int(0, rect.y + MIN_BSP_SIZE, rect.y + rect.h - MIN_BSP_SIZE)
h = (rect.y + rect.h) - y
tries = 0
while y + h > len(self.map[0]) or tries <= 3:
print y, h, "vert out of bounds"
y = rect.y + 1
h = rect.h / 2
baby = Rect(rect.w, rect.h - h, rect.x, rect.y)
baby.bsp(rect)
baby_2 = Rect(rect.w, h, rect.x, y)
baby_2.bsp(rect)
rect.babies.append(baby)
rect.babies.append(baby_2)
else:
rect.end = True
for baby in rect.babies:
if baby.end != True:
self.split(baby)
def _read_image(im):
im = cv2.imread(im)
im = utils.prewhiten(im)
im = utils.flip(im, random_flip=True)
im = utils.random_crop(im, image_size=299)
im = cv2.resize(im, (128, 128))
im = utils.random_rotate_image(im)
return im
def singleton_detection_nso(U_slice, **kwargs):
n = kwargs.get("n")
chunks = sign(np.reshape(U_slice, (len(U_slice) // (n + 1), n + 1)))
chunks = (np.mod((chunks.T + chunks[:, 0]).T, 2)).astype(dtype=int)[:, 1:]
choices = np.vstack(
(np.sum(chunks, axis=0), np.sum([flip(c) for c in chunks], axis=0)))
nso_k = np.argmin(choices, axis=0)
return nso_k, 1
def train_load_and_preprocess_from_path_label_c(self, path, label,
crop_bool, flip_bool):
image = load_and_preprocess_image(path, self.IMG_X_SIZE,
self.IMG_Y_SIZE)
if flip_bool:
image = flip(image)
if crop_bool:
image = crop(image, self.IMG_Y_SIZE, self.IMG_X_SIZE)
return image, tf.one_hot(indices=[label], depth=self.N_CLASSES)[0]
def extract_hidden_layers(self, input, hidden, index_target):
input_f, input_b = input
index_target_f = index_target
index_target_b = len(input_f) - index_target + 1
emb_f = self.drop(self.encoder(input_f[:index_target_f]))
emb_b = self.drop(self.encoder(input_b[:index_target_b]))
hidden_f = hidden[0]
hidden_b = hidden[1]
output_f, hidden_f = self.forward_lstm(emb_f, hidden_f)
output_b, hidden_b = self.backward_lstm(emb_b, hidden_b)
predictive_hidden_layers = []
for i in range(len(hidden_f)):
f = hidden_f[i][0]
b = utils.flip(hidden_b[i][0], dim=0)
output_i = torch.cat((f, b), dim=2)
predictive_hidden_layers.append(output_i.squeeze(0).squeeze(0))
hidden = self.init_hidden(1)
emb_f = self.drop(self.encoder(input_f[:index_target_f + 1]))
emb_b = self.drop(self.encoder(input_b[:index_target_b + 1]))
hidden_f = hidden[0]
hidden_b = hidden[1]
output_f, hidden_f = self.forward_lstm(emb_f, hidden_f)
output_b, hidden_b = self.backward_lstm(emb_b, hidden_b)
current_hidden_layers = []
for i in range(len(hidden_f)):
f = hidden_f[i][0]
b = utils.flip(hidden_b[i][0], dim=0)
output_i = torch.cat((f, b), dim=2)
current_hidden_layers.append(output_i.squeeze(0).squeeze(0))
return predictive_hidden_layers, current_hidden_layers
def __getitem__(self, index):
images = self.loader(self.images[index])
targets = torch.Tensor(self.boxes[index])
if self.aug:
images = photometric_distort(images)
if random.random() < 0.5:
images, targets = flip(images, targets)
inputs = self.transform(images)
width, height = images.size
targets = self.target_transform(targets, (width, height))
return inputs, targets
def process_csv_line(line):
"""
Given a csv line, it loads the image and performs some modifications
with the purpose of equalizing steering angles distribution.
"""
img, angle = load_img_from_csv_line(line)
# image perturbations
img, angle = translate(img, angle)
img = perturb_brightness(img)
img = add_shadow(img)
if do_flip():
img, angle = flip(img, angle)
return img, angle
def batch_generator(images, angles, augment_data= True, batch_size=64):
batch_images = []
batch_angles = []
sample_count = 0
while True:
for i in np.random.permutation(images.shape[0]):
center_path = 'data/'+images.iloc[i,0]
left_path = 'data/'+images.iloc[i,1]
right_path = 'data/'+images.iloc[i,2]
center_path = center_path.replace(" ", "")
left_path = left_path.replace(" ", "")
right_path = right_path.replace(" ", "")
center_image = utils.load_image(center_path)
angle = float(angles.iloc[i])
batch_images.append(center_image)
batch_angles.append(angle)
sample_count += 1
if augment_data:
flipped_image = utils.flip(center_path)
flipped_angle = -1.0 * angle
batch_images.append(flipped_image)
batch_angles.append(flipped_angle)
tint_image = utils.tint_image(center_path)
tint_angle = angle
batch_images.append(tint_image)
batch_angles.append(tint_angle)
jittered_image, jitter_angle = utils.jitter_image(center_path,angle)
batch_images.append(jittered_image)
batch_angles.append(jitter_angle)
left_image = utils.load_image(left_path)
left_angle = min(1.0, angle+ SIDE_STEERING_CONSTANT)
batch_images.append(left_image)
batch_angles.append(left_angle)
right_image = utils.load_image(right_path)
right_angle = max(-1.0, angle - SIDE_STEERING_CONSTANT)
batch_images.append(right_image)
batch_angles.append(right_angle)
if ((sample_count%batch_size == 0) or (sample_count % len(images)==0)):
yield np.array(batch_images),np.array(batch_angles)
batch_angles, batch_images = [], []
def forward(self, input, hidden):
input_f, input_b = input
emb_f = self.drop(self.encoder(input_f))
emb_b = self.drop(self.encoder(input_b))
hidden_f = hidden[0]
hidden_b = hidden[1]
output_f, hidden_f = self.forward_lstm(emb_f, hidden_f)
output_b, hidden_b = self.backward_lstm(emb_b, hidden_b)
output = output_f + utils.flip(
output_b, dim=0) # output is sum of forward and backward
decoded = self.decoder(
output.view(output.size(0) * output.size(1), output.size(2)))
return decoded.view(output.size(0), output.size(1),
decoded.size(1)), (hidden_f, hidden_b)
def saveImageThermalCallBack():
root.filename = tkinter.filedialog.asksaveasfilename(
initialdir=dir,
title="Select file",
filetypes=(("pdf files", "*.pdf"), ("all files", "*.*")))
data.filename = root.filename
thermal_matrix = np.array(data.thermal_matrix)
numrows, numcols = thermal_matrix.shape
def format_coord(x, y):
z = -150.0 + thermal_matrix[y, x] * 200 / 255.0
return z
imgt = Image.fromarray(thermal_matrix)
y = np.arange(0, numrows - 1, 1)
x = np.arange(0, numcols - 1, 1)
X, Y = np.meshgrid(x, y)
# Calculating the output and storing it in the array Z
Z = format_coord(X, Y)
plt.figure(figsize=(8, 6))
img = plt.imshow(utils.flip(Z),
cmap='gnuplot2',
interpolation='none',
origin='upper')
plt.grid(True)
plt.title("Temperatura ($^\circ$C )")
plt.colorbar(img)
plt.savefig(root.filename, dpi=300)
plt.show()
'''
fig, ax = plt.subplots(dpi = 300)
cax = ax.imshow(imgt, cmap='jet')
ax.format_coord = format_coord
ax.grid(True)
cbar = fig.colorbar(cax)
'''
logScreen("Imagen guardada en " + root.filename)
def new_split(self, rect):
if rect.w > MIN_BSP_SIZE * 2 and rect.h > MIN_BSP_SIZE * 2:
if utils.flip() and rect.x + rect.w / 2 + MIN_BSP_SIZE < len(
self.map) and rect.y + rect.h / 2 + MIN_BSP_SIZE < len(
self.map[0]):
x = libtcod.random_get_int(0, rect.x + MIN_BSP_SIZE,
rect.x + rect.w - MIN_BSP_SIZE)
w = (rect.x + rect.w) - x
baby = Rect(rect.w - w, rect.h, rect.x, rect.y)
baby.bsp(rect)
baby_2 = Rect(w - 1, rect.h, x, rect.y)
baby_2.bsp(rect)
rect.babies.append(baby)
rect.babies.append(baby_2)
else:
y = libtcod.random_get_int(0, rect.y + MIN_BSP_SIZE,
rect.y + rect.h - MIN_BSP_SIZE)
h = (rect.y + rect.h) - y
baby = Rect(rect.w, rect.h - h, rect.x, rect.y)
baby.bsp(rect)
baby_2 = Rect(rect.w, h - 1, rect.x, y)
baby_2.bsp(rect)
rect.babies.append(baby)
rect.babies.append(baby_2)
else:
rect.end = True
for baby in rect.babies:
if baby.end != True:
self.new_split(baby)
def generate_hdf5(ftxt, output, fname, argument=False):
data = getDataFromTxt(ftxt)
F_imgs = []
F_landmarks = []
EN_imgs = []
EN_landmarks = []
NM_imgs = []
NM_landmarks = []
for (imgPath, bbox, landmarkGt) in data:
img = cv2.imread(imgPath, cv2.CV_LOAD_IMAGE_GRAYSCALE)
assert(img is not None)
logger("process %s" % imgPath)
# F
f_bbox = bbox.subBBox(-0.05, 1.05, -0.05, 1.05)
f_face = img[f_bbox.top:f_bbox.bottom+1,f_bbox.left:f_bbox.right+1]
## data argument
if argument and np.random.rand() > -1:
### flip
face_flipped, landmark_flipped = flip(f_face, landmarkGt)
face_flipped = cv2.resize(face_flipped, (39, 39))
F_imgs.append(face_flipped.reshape((1, 39, 39)))
F_landmarks.append(landmark_flipped.reshape(10))
### rotation
if np.random.rand() > 0.5:
face_rotated_by_alpha, landmark_rotated = rotate(img, f_bbox, \
bbox.reprojectLandmark(landmarkGt), 5)
landmark_rotated = bbox.projectLandmark(landmark_rotated)
face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha, (39, 39))
F_imgs.append(face_rotated_by_alpha.reshape((1, 39, 39)))
F_landmarks.append(landmark_rotated.reshape(10))
### flip with rotation
face_flipped, landmark_flipped = flip(face_rotated_by_alpha, landmark_rotated)
face_flipped = cv2.resize(face_flipped, (39, 39))
F_imgs.append(face_flipped.reshape((1, 39, 39)))
F_landmarks.append(landmark_flipped.reshape(10))
### rotation
if np.random.rand() > 0.5:
face_rotated_by_alpha, landmark_rotated = rotate(img, f_bbox, \
bbox.reprojectLandmark(landmarkGt), -5)
landmark_rotated = bbox.projectLandmark(landmark_rotated)
face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha, (39, 39))
F_imgs.append(face_rotated_by_alpha.reshape((1, 39, 39)))
F_landmarks.append(landmark_rotated.reshape(10))
### flip with rotation
face_flipped, landmark_flipped = flip(face_rotated_by_alpha, landmark_rotated)
face_flipped = cv2.resize(face_flipped, (39, 39))
F_imgs.append(face_flipped.reshape((1, 39, 39)))
F_landmarks.append(landmark_flipped.reshape(10))
f_face = cv2.resize(f_face, (39, 39))
en_face = f_face[:31, :]
nm_face = f_face[8:, :]
f_face = f_face.reshape((1, 39, 39))
f_landmark = landmarkGt.reshape((10))
F_imgs.append(f_face)
F_landmarks.append(f_landmark)
# EN
# en_bbox = bbox.subBBox(-0.05, 1.05, -0.04, 0.84)
# en_face = img[en_bbox.top:en_bbox.bottom+1,en_bbox.left:en_bbox.right+1]
## data argument
if argument and np.random.rand() > 0.5:
### flip
face_flipped, landmark_flipped = flip(en_face, landmarkGt)
face_flipped = cv2.resize(face_flipped, (31, 39)).reshape((1, 31, 39))
landmark_flipped = landmark_flipped[:3, :].reshape((6))
EN_imgs.append(face_flipped)
EN_landmarks.append(landmark_flipped)
en_face = cv2.resize(en_face, (31, 39)).reshape((1, 31, 39))
en_landmark = landmarkGt[:3, :].reshape((6))
EN_imgs.append(en_face)
EN_landmarks.append(en_landmark)
# NM
# nm_bbox = bbox.subBBox(-0.05, 1.05, 0.18, 1.05)
# nm_face = img[nm_bbox.top:nm_bbox.bottom+1,nm_bbox.left:nm_bbox.right+1]
## data argument
if argument and np.random.rand() > 0.5:
### flip
face_flipped, landmark_flipped = flip(nm_face, landmarkGt)
face_flipped = cv2.resize(face_flipped, (31, 39)).reshape((1, 31, 39))
landmark_flipped = landmark_flipped[2:, :].reshape((6))
NM_imgs.append(face_flipped)
NM_landmarks.append(landmark_flipped)
nm_face = cv2.resize(nm_face, (31, 39)).reshape((1, 31, 39))
nm_landmark = landmarkGt[2:, :].reshape((6))
NM_imgs.append(nm_face)
NM_landmarks.append(nm_landmark)
#imgs, landmarks = process_images(ftxt, output)
F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)
EN_imgs, EN_landmarks = np.asarray(EN_imgs), np.asarray(EN_landmarks)
NM_imgs, NM_landmarks = np.asarray(NM_imgs),np.asarray(NM_landmarks)
F_imgs = processImage(F_imgs)
shuffle_in_unison_scary(F_imgs, F_landmarks)
EN_imgs = processImage(EN_imgs)
shuffle_in_unison_scary(EN_imgs, EN_landmarks)
NM_imgs = processImage(NM_imgs)
shuffle_in_unison_scary(NM_imgs, NM_landmarks)
# full face
base = join(OUTPUT, '1_F')
createDir(base)
output = join(base, fname)
logger("generate %s" % output)
with h5py.File(output, 'w') as h5:
h5['data'] = F_imgs.astype(np.float32)
h5['landmark'] = F_landmarks.astype(np.float32)
# eye and nose
base = join(OUTPUT, '1_EN')
createDir(base)
output = join(base, fname)
logger("generate %s" % output)
with h5py.File(output, 'w') as h5:
h5['data'] = EN_imgs.astype(np.float32)
h5['landmark'] = EN_landmarks.astype(np.float32)
# nose and mouth
base = join(OUTPUT, '1_NM')
createDir(base)
output = join(base, fname)
logger("generate %s" % output)
with h5py.File(output, 'w') as h5:
h5['data'] = NM_imgs.astype(np.float32)
h5['landmark'] = NM_landmarks.astype(np.float32)
def main(args):
IMG_SIZE = args.IMG_SIZE
print("Image size: " + str(IMG_SIZE))
save_dir = '../Data/' + str(IMG_SIZE)
landmark_save_dir = '../Data/' + str(IMG_SIZE) + '/landmark'
record_save_dir = '../Data/' + str(IMG_SIZE) + '/landmark_record.pkl'
if not os.path.exists(save_dir):
os.mkdir(save_dir)
if not os.path.exists(landmark_save_dir):
os.mkdir(landmark_save_dir)
f = open(RECORD_PATH, 'rb')
info = pkl.load(f)
f.close()
idx = 0
aug_list = []
for i in range(len(info)):
pic_info = info[i]
image_name = pic_info[0]
roi = pic_info[1]
landmark = pic_info[2]
print("Processing the image: " + image_name)
print(str(idx) + " images generated. ")
image = cv2.imread(
os.path.join('../Data/CelebA/Img/img_celeba', image_name))
height, width, channel = image.shape
# Crop out the face area
x1, y1, x2, y2 = roi
face = image[y1:y2, x1:x2]
w = x2 - x1 + 1
h = y2 - y1 + 1
# Calculate the relative position of each landmark
transfered_landmark = np.zeros((6, 2))
for k in range(6):
transfered_landmark[k][0] = (landmark[k * 2] - x1) / (x2 - x1)
transfered_landmark[k][1] = (landmark[k * 2 + 1] - y1) / (y2 - y1)
'''Verify
temp = face.copy()
for k in range(6):
cv2.circle(temp, (int(transfered_landmark[k][0] * (x2 - x1)), int(transfered_landmark[k][1] * (y2 - y1))), 2, (0, 255, 0), 1)
cv2.imshow('t', temp)
cv2.waitKey()
cv2.destroyAllWindows() # Verified
del temp
'''
# Save the resized face image
resized_face = cv2.resize(face, (IMG_SIZE, IMG_SIZE),
interpolation=cv2.INTER_LINEAR)
saving_name = str(idx) + '.jpg'
saving_path = os.path.join(landmark_save_dir, saving_name)
success = cv2.imwrite(saving_path, resized_face)
if not success:
raise Exception("Landmark picture " + str(idx) +
" saving failed. ")
aug_list.append([
'landmark/' + saving_name, -2,
np.array([-1, -1, -1, -1]),
np.squeeze(np.array(transfered_landmark.reshape(1, -1)))
])
idx = idx + 1
# Mirror
flipped_face, flipped_landmark = flip(face, transfered_landmark)
'''Verify
temp = flipped_face.copy()
for k in range(6):
cv2.circle(temp, (int(flipped_landmark[k][0] * (x2 - x1)), int(flipped_landmark[k][1] * (y2 - y1))), 2, (0, 255, 0), 1)
cv2.imshow('t', temp)
cv2.waitKey()
cv2.destroyAllWindows() # Verified
del temp
'''
# Save the resized face image
resized_flipped_face = cv2.resize(flipped_face, (IMG_SIZE, IMG_SIZE),
interpolation=cv2.INTER_LINEAR)
saving_name = str(idx) + '.jpg'
saving_path = os.path.join(landmark_save_dir, saving_name)
success = cv2.imwrite(saving_path, resized_flipped_face)
if not success:
raise Exception("Landmark picture " + str(idx) +
" saving failed. ")
aug_list.append([
'landmark/' + saving_name, -2,
np.array([-1, -1, -1, -1]),
np.squeeze(np.array(flipped_landmark.reshape(1, -1)))
])
idx = idx + 1
# Drop the faces that are too small or exceed the boundaries
if max(w, h) < 40 or x1 < 0 or y1 < 0:
continue
# Augment the cropped face
for j in range(10):
# Randomly pick a new size & shifts
new_size = np.random.randint(
int(min(w, h) * 0.8),
np.ceil(max(w, h) * 1.25)) # new_size = width/height - 1
delta_x = np.random.randint(-w * 0.15, w * 0.15)
delta_y = np.random.randint(-h * 0.15, h * 0.15)
new_x1 = int(max(0, x1 + w / 2 - new_size / 2 + delta_x))
new_y1 = int(max(0, y1 + h / 2 - new_size / 2 + delta_y))
new_x2 = new_x1 + new_size
new_y2 = new_y1 + new_size
if new_x2 > width or new_y2 > height:
continue
crop_box = np.array([new_x1, new_y1, new_x2, new_y2])
iou = IoU(crop_box, np.array(roi).reshape(1, -1))
if iou > 0.65:
cropped_image = image[new_y1:new_y2 + 1, new_x1:new_x2 + 1]
resized_image = cv2.resize(cropped_image, (IMG_SIZE, IMG_SIZE),
interpolation=cv2.INTER_LINEAR)
transfered_landmark = np.zeros((6, 2))
for k in range(6):
transfered_landmark[k][0] = (landmark[k * 2] -
new_x1) / new_size
transfered_landmark[k][1] = (landmark[k * 2 + 1] -
new_y1) / new_size
'''Verify
temp = cropped_image.copy()
for k in range(6):
cv2.circle(temp, (int(transfered_landmark[k][0] * new_size), int(transfered_landmark[k][1] * new_size)), 2, (0, 255, 0), 1)
cv2.imshow('t', temp)
cv2.waitKey()
cv2.destroyAllWindows() # Verified
del temp
'''
saving_name = str(idx) + '.jpg'
saving_path = os.path.join(landmark_save_dir, saving_name)
success = cv2.imwrite(saving_path, resized_image)
if not success:
raise Exception("Landmark picture " + str(idx) +
" saving failed. ")
aug_list.append([
'landmark/' + saving_name, -2,
np.array([-1, -1, -1, -1]),
np.squeeze(np.array(transfered_landmark.reshape(1, -1)))
])
idx = idx + 1
# Mirror
# if random.choice([0,1]) == 1:
flipped_image, flipped_landmark = flip(cropped_image,
transfered_landmark)
'''Verify
temp = flipped_image.copy()
for k in range(6):
cv2.circle(temp, (int(flipped_landmark[k][0] * new_size), int(flipped_landmark[k][1] * new_size)), 2, (0, 255, 0), 1)
cv2.imshow('t', temp)
cv2.waitKey()
cv2.destroyAllWindows() # Verified
del temp
'''
resized_image = cv2.resize(flipped_image, (IMG_SIZE, IMG_SIZE),
interpolation=cv2.INTER_LINEAR)
saving_name = str(idx) + '.jpg'
saving_path = os.path.join(landmark_save_dir, saving_name)
success = cv2.imwrite(saving_path, resized_image)
if not success:
raise Exception("Landmark picture " + str(idx) +
" saving failed. ")
aug_list.append([
'landmark/' + saving_name, -2,
np.array([-1, -1, -1, -1]),
np.squeeze(np.array(flipped_landmark.reshape(1, -1)))
])
idx = idx + 1
# Anti-Clockwise Rotate
# if random.choice([0,1]) == 1:
# a. Anti-clockwise rotate
theta = np.random.randint(5, 15)
rotated_face, rotated_landmark = rotate(
image, crop_box, landmark, theta
) # rotated_landmark here has not been transfered yet!
resized_rotated_face = cv2.resize(
rotated_face, (IMG_SIZE, IMG_SIZE),
interpolation=cv2.INTER_LINEAR)
transfered_rotated_landmark = np.zeros((6, 2))
for k in range(6):
transfered_rotated_landmark[k][0] = (
rotated_landmark[k][0] - new_x1) / new_size
transfered_rotated_landmark[k][1] = (
rotated_landmark[k][1] - new_y1) / new_size
'''Verify
temp = rotated_face.copy()
for k in range(6):
cv2.circle(temp, (int(transfered_rotated_landmark[k][0] * new_size), int(transfered_rotated_landmark[k][1] * new_size)), 2, (0, 255, 0), 1)
cv2.imshow('t', temp)
cv2.waitKey()
cv2.destroyAllWindows() # Verified
del temp
'''
saving_name = str(idx) + '.jpg'
saving_path = os.path.join(landmark_save_dir, saving_name)
success = cv2.imwrite(saving_path, resized_rotated_face)
if not success:
raise Exception("Landmark picture " + str(idx) +
" saving failed. ")
aug_list.append([
'landmark/' + saving_name, -2,
np.array([-1, -1, -1, -1]),
np.squeeze(
np.array(transfered_rotated_landmark.reshape(1, -1)))
])
idx = idx + 1
# b. Anti-clockwise rotate & mirror
flipped_rotated_face, flipped_transfered_rotated_landmark = flip(
rotated_face, transfered_rotated_landmark)
'''Verify
temp = flipped_rotated_face.copy()
for k in range(6):
cv2.circle(temp, (int(flipped_transfered_rotated_landmark[k][0] * new_size), int(flipped_transfered_rotated_landmark[k][1] * new_size)), 2, (0, 255, 0), 1)
cv2.imshow('t', temp)
cv2.waitKey()
cv2.destroyAllWindows() # Verified
del temp
'''
resized_flipped_rotated_face = cv2.resize(
flipped_rotated_face, (IMG_SIZE, IMG_SIZE),
interpolation=cv2.INTER_LINEAR)
saving_name = str(idx) + '.jpg'
saving_path = os.path.join(landmark_save_dir, saving_name)
success = cv2.imwrite(saving_path,
resized_flipped_rotated_face)
if not success:
raise Exception("Landmark picture " + str(idx) +
" saving failed. ")
aug_list.append([
'landmark/' + saving_name, -2,
np.array([-1, -1, -1, -1]),
np.squeeze(
np.array(
flipped_transfered_rotated_landmark.reshape(1,
-1)))
])
idx = idx + 1
# Clockwise Rotate
# if random.choice([0,1]) == 1:
# a. Clockwise rotate
theta = np.random.randint(5, 15)
rotated_face, rotated_landmark = rotate(
image, crop_box, landmark, -theta
) # rotated_landmark here has not been transfered yet!
resized_rotated_face = cv2.resize(
rotated_face, (IMG_SIZE, IMG_SIZE),
interpolation=cv2.INTER_LINEAR)
transfered_rotated_landmark = np.zeros((6, 2))
for k in range(6):
transfered_rotated_landmark[k][0] = (
rotated_landmark[k][0] - new_x1) / new_size
transfered_rotated_landmark[k][1] = (
rotated_landmark[k][1] - new_y1) / new_size
'''Verify
temp = rotated_face.copy()
for k in range(6):
cv2.circle(temp, (int(transfered_rotated_landmark[k][0] * new_size), int(transfered_rotated_landmark[k][1] * new_size)), 2, (0, 255, 0), 1)
cv2.imshow('t', temp)
cv2.waitKey()
cv2.destroyAllWindows() # Verified
del temp
'''
saving_name = str(idx) + '.jpg'
saving_path = os.path.join(landmark_save_dir, saving_name)
success = cv2.imwrite(saving_path, resized_rotated_face)
if not success:
raise Exception("Landmark picture " + str(idx) +
" saving failed. ")
aug_list.append([
'landmark/' + saving_name, -2,
np.array([-1, -1, -1, -1]),
np.squeeze(
np.array(transfered_rotated_landmark.reshape(1, -1)))
])
idx = idx + 1
# b. Clockwise rotate & mirror
flipped_rotated_face, flipped_transfered_rotated_landmark = flip(
rotated_face, transfered_rotated_landmark)
'''Verify
temp = flipped_rotated_face.copy()
for k in range(6):
cv2.circle(temp, (int(flipped_transfered_rotated_landmark[k][0] * new_size), int(flipped_transfered_rotated_landmark[k][1] * new_size)), 2, (0, 255, 0), 1)
cv2.imshow('t', temp)
cv2.waitKey()
cv2.destroyAllWindows() # Verified
del temp
'''
resized_flipped_rotated_face = cv2.resize(
flipped_rotated_face, (IMG_SIZE, IMG_SIZE),
interpolation=cv2.INTER_LINEAR)
saving_name = str(idx) + '.jpg'
saving_path = os.path.join(landmark_save_dir, saving_name)
success = cv2.imwrite(saving_path,
resized_flipped_rotated_face)
if not success:
raise Exception("Landmark picture " + str(idx) +
" saving failed. ")
aug_list.append([
'landmark/' + saving_name, -2,
np.array([-1, -1, -1, -1]),
np.squeeze(
np.array(
flipped_transfered_rotated_landmark.reshape(1,
-1)))
])
idx = idx + 1
# Save the augmentation list
file = open(record_save_dir, 'wb+')
pkl.dump(aug_list, file)
file.close()
print("Processing Finished. ")
def get_train_test_loader(Data_Band_Scaler, GroundTruth, class_num,
shot_num_per_class):
print(Data_Band_Scaler.shape) # (610, 340, 103)
[nRow, nColumn, nBand] = Data_Band_Scaler.shape
'''label start'''
num_class = int(np.max(GroundTruth))
data_band_scaler = utils.flip(Data_Band_Scaler)
groundtruth = utils.flip(GroundTruth)
del Data_Band_Scaler
del GroundTruth
HalfWidth = 4
G = groundtruth[nRow - HalfWidth:2 * nRow + HalfWidth,
nColumn - HalfWidth:2 * nColumn + HalfWidth]
data = data_band_scaler[nRow - HalfWidth:2 * nRow + HalfWidth,
nColumn - HalfWidth:2 * nColumn + HalfWidth, :]
[Row, Column] = np.nonzero(G) # (10249,) (10249,)
# print(Row)
del data_band_scaler
del groundtruth
nSample = np.size(Row)
print('number of sample', nSample)
# Sampling samples
train = {}
test = {}
da_train = {} # Data Augmentation
m = int(np.max(G)) # 9
nlabeled = TEST_LSAMPLE_NUM_PER_CLASS
print('labeled number per class:', nlabeled)
print((200 - nlabeled) / nlabeled + 1)
print(math.ceil((200 - nlabeled) / nlabeled) + 1)
for i in range(m):
indices = [
j for j, x in enumerate(Row.ravel().tolist())
if G[Row[j], Column[j]] == i + 1
]
np.random.shuffle(indices)
nb_val = shot_num_per_class
train[i] = indices[:nb_val]
da_train[i] = []
for j in range(math.ceil((200 - nlabeled) / nlabeled) + 1):
da_train[i] += indices[:nb_val]
test[i] = indices[nb_val:]
train_indices = []
test_indices = []
da_train_indices = []
for i in range(m):
train_indices += train[i]
test_indices += test[i]
da_train_indices += da_train[i]
np.random.shuffle(test_indices)
print('the number of train_indices:', len(train_indices)) # 520
print('the number of test_indices:', len(test_indices)) # 9729
print('the number of train_indices after data argumentation:',
len(da_train_indices)) # 520
print('labeled sample indices:', train_indices)
nTrain = len(train_indices)
nTest = len(test_indices)
da_nTrain = len(da_train_indices)
imdb = {}
imdb['data'] = np.zeros(
[2 * HalfWidth + 1, 2 * HalfWidth + 1, nBand, nTrain + nTest],
dtype=np.float32) # (9,9,100,n)
imdb['Labels'] = np.zeros([nTrain + nTest], dtype=np.int64)
imdb['set'] = np.zeros([nTrain + nTest], dtype=np.int64)
RandPerm = train_indices + test_indices
RandPerm = np.array(RandPerm)
for iSample in range(nTrain + nTest):
imdb['data'][:, :, :,
iSample] = data[Row[RandPerm[iSample]] -
HalfWidth:Row[RandPerm[iSample]] +
HalfWidth + 1, Column[RandPerm[iSample]] -
HalfWidth:Column[RandPerm[iSample]] +
HalfWidth + 1, :]
imdb['Labels'][iSample] = G[Row[RandPerm[iSample]],
Column[RandPerm[iSample]]].astype(np.int64)
imdb['Labels'] = imdb['Labels'] - 1 # 1-16 0-15
imdb['set'] = np.hstack(
(np.ones([nTrain]), 3 * np.ones([nTest]))).astype(np.int64)
print('Data is OK.')
train_dataset = utils.matcifar(imdb, train=True, d=3, medicinal=0)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=class_num *
shot_num_per_class,
shuffle=False,
num_workers=0)
del train_dataset
test_dataset = utils.matcifar(imdb, train=False, d=3, medicinal=0)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=100,
shuffle=False,
num_workers=0)
del test_dataset
del imdb
# Data Augmentation for target domain for training
imdb_da_train = {}
imdb_da_train['data'] = np.zeros(
[2 * HalfWidth + 1, 2 * HalfWidth + 1, nBand, da_nTrain],
dtype=np.float32) # (9,9,100,n)
imdb_da_train['Labels'] = np.zeros([da_nTrain], dtype=np.int64)
imdb_da_train['set'] = np.zeros([da_nTrain], dtype=np.int64)
da_RandPerm = np.array(da_train_indices)
for iSample in range(da_nTrain): # radiation_noise，flip_augmentation
imdb_da_train['data'][:, :, :, iSample] = utils.radiation_noise(
data[Row[da_RandPerm[iSample]] -
HalfWidth:Row[da_RandPerm[iSample]] + HalfWidth + 1,
Column[da_RandPerm[iSample]] -
HalfWidth:Column[da_RandPerm[iSample]] + HalfWidth + 1, :])
imdb_da_train['Labels'][iSample] = G[
Row[da_RandPerm[iSample]],
Column[da_RandPerm[iSample]]].astype(np.int64)
imdb_da_train['Labels'] = imdb_da_train['Labels'] - 1 # 1-16 0-15
imdb_da_train['set'] = np.ones([da_nTrain]).astype(np.int64)
print('ok')
return train_loader, test_loader, imdb_da_train, G, RandPerm, Row, Column, nTrain
def process_data(data, size, augmentation):
face_images = []
face_landmarks = []
idx = 0
for d in data:
image_file = d['image_file']
bbox = d['bbox']
points = d['landmark']
img = cv2.imread(image_file)
im_h, im_w, _ = img.shape
x1, y1, x2, y2 = bbox
face_roi = img[y1:y2, x1:x2]
face_roi = resize(face_roi, size)
# 归一化
landmark = normalize_landmark(points, x1, x2, y1, y2)
face_images.append(face_roi)
face_landmarks.append(np.array(landmark).reshape(10))
#数据增强
if augmentation:
idx = idx + 1
# gt's width
bbox_w = x2 - x1 + 1
# gt's height
bbox_h = y2 - y1 + 1
if max(bbox_w, bbox_h) < 40 or x1 < 0 or y1 < 0:
continue
# random shift
for i in range(10):
bbox_size, nx1, nx2, ny1, ny2 = new_bbox(bbox_h, bbox_w, x1, y1)
if nx2 > im_w or ny2 > im_h:
continue
crop_box = np.array([nx1, ny1, nx2, ny2])
cropped_im = img[ny1:ny2 + 1, nx1:nx2 + 1, :]
_iou = iou(crop_box, np.expand_dims(bbox, 0))
if _iou > 0.65:
resize_im = resize(cropped_im, size)
face_images.append(resize_im)
# normalize
landmark = normalize_landmark2(bbox_size, points, nx1, ny1)
face_landmarks.append(landmark.reshape(10))
landmark_ = landmark.reshape(-1, 2)
nbbox = nx1, ny1, nx2, ny2
# mirror
if random.choice([0, 1]) > 0:
face_flipped, landmark_flipped = flip(resize_im, landmark_)
face_flipped = resize(face_flipped, size)
# c*h*w
face_images.append(face_flipped)
face_landmarks.append(landmark_flipped.reshape(10))
# rotate
if random.choice([0, 1]) > 0:
_landmark = reproject_landmark(nbbox, landmark_)
face_rotated_by_alpha, landmark_rotated = rotate(img, nbbox, _landmark, 5)
# landmark_offset
landmark_rotated = project_landmark(nbbox, landmark_rotated)
face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha, (size, size))
face_images.append(face_rotated_by_alpha)
face_landmarks.append(landmark_rotated.reshape(10))
# flip
face_flipped, landmark_flipped = flip(face_rotated_by_alpha, landmark_rotated)
face_flipped = cv2.resize(face_flipped, (size, size))
face_images.append(face_flipped)
face_landmarks.append(landmark_flipped.reshape(10))
# inverse clockwise rotation
if random.choice([0, 1]) > 0:
_landmark = reproject_landmark(nbbox, landmark_)
face_rotated_by_alpha, landmark_rotated = rotate(img, nbbox, _landmark, -5) # 顺时针旋转
landmark_rotated = project_landmark(nbbox, landmark_rotated)
face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha, (size, size))
face_images.append(face_rotated_by_alpha)
face_landmarks.append(landmark_rotated.reshape(10))
face_flipped, landmark_flipped = flip(face_rotated_by_alpha, landmark_rotated)
face_flipped = cv2.resize(face_flipped, (size, size))
face_images.append(face_flipped)
face_landmarks.append(landmark_flipped.reshape(10))
return face_images, face_landmarks
def generateSampleBox(img_path,
bndbox,
part,
nJoints,
imgset,
scale_factor,
dataset,
train=True):
nJoints_coco = 17
nJoints_mpii = 16
img = load_image(img_path)
if train:
img[0].mul_(random.uniform(0.7, 1.3)).clamp_(0, 1)
img[1].mul_(random.uniform(0.7, 1.3)).clamp_(0, 1)
img[2].mul_(random.uniform(0.7, 1.3)).clamp_(0, 1)
ori_img = img.clone()
img[0].add_(-0.406)
img[1].add_(-0.457)
img[2].add_(-0.480)
upLeft = torch.Tensor((int(bndbox[0][0]), int(bndbox[0][1])))
bottomRight = torch.Tensor((int(bndbox[0][2]), int(bndbox[0][3])))
ht = bottomRight[1] - upLeft[1]
width = bottomRight[0] - upLeft[0]
imght = img.shape[1]
imgwidth = img.shape[2]
scaleRate = random.uniform(*scale_factor)
upLeft[0] = max(0, upLeft[0] - width * scaleRate / 2)
upLeft[1] = max(0, upLeft[1] - ht * scaleRate / 2)
bottomRight[0] = min(imgwidth - 1, bottomRight[0] + width * scaleRate / 2)
bottomRight[1] = min(imght - 1, bottomRight[1] + ht * scaleRate / 2)
# Doing Random Sample
if opt.addDPG:
PatchScale = random.uniform(0, 1)
if PatchScale > 0.85:
ratio = ht / width
if width < ht:
patchWidth = PatchScale * width
patchHt = patchWidth * ratio
else:
patchHt = PatchScale * ht
patchWidth = patchHt / ratio
xmin = upLeft[0] + random.uniform(0, 1) * (width - patchWidth)
ymin = upLeft[1] + random.uniform(0, 1) * (ht - patchHt)
xmax = xmin + patchWidth + 1
ymax = ymin + patchHt + 1
else:
xmin = max(
1,
min(upLeft[0] + np.random.normal(-0.0142, 0.1158) * width,
imgwidth - 3))
ymin = max(
1,
min(upLeft[1] + np.random.normal(0.0043, 0.068) * ht,
imght - 3))
xmax = min(
max(xmin + 2,
bottomRight[0] + np.random.normal(0.0154, 0.1337) * width),
imgwidth - 3)
ymax = min(
max(ymin + 2,
bottomRight[1] + np.random.normal(-0.0013, 0.0711) * ht),
imght - 3)
upLeft[0] = xmin
upLeft[1] = ymin
bottomRight[0] = xmax
bottomRight[1] = ymax
# Counting Joints number
jointNum = 0
if imgset == 'coco':
for i in range(17):
if part[i][0] > 0 and part[i][0] > upLeft[0] and part[i][1] > upLeft[1] \
and part[i][0] < bottomRight[0] and part[i][1] < bottomRight[1]:
jointNum += 1
else:
for i in range(16):
if part[i][0] > 0 and part[i][0] > upLeft[0] and part[i][1] > upLeft[1] \
and part[i][0] < bottomRight[0] and part[i][1] < bottomRight[1]:
jointNum += 1
# Doing Random Crop
if opt.addDPG:
if jointNum > 13 and train:
switch = random.uniform(0, 1)
if switch > 0.96:
bottomRight[0] = (upLeft[0] + bottomRight[0]) / 2
bottomRight[1] = (upLeft[1] + bottomRight[1]) / 2
elif switch > 0.92:
upLeft[0] = (upLeft[0] + bottomRight[0]) / 2
bottomRight[1] = (upLeft[1] + bottomRight[1]) / 2
elif switch > 0.88:
upLeft[1] = (upLeft[1] + bottomRight[1]) / 2
bottomRight[0] = (upLeft[0] + bottomRight[0]) / 2
elif switch > 0.84:
upLeft[0] = (upLeft[0] + bottomRight[0]) / 2
upLeft[1] = (upLeft[1] + bottomRight[1]) / 2
elif switch > 0.80:
bottomRight[0] = (upLeft[0] + bottomRight[0]) / 2
elif switch > 0.76:
upLeft[0] = (upLeft[0] + bottomRight[0]) / 2
elif switch > 0.72:
bottomRight[1] = (upLeft[1] + bottomRight[1]) / 2
elif switch > 0.68:
upLeft[1] = (upLeft[1] + bottomRight[1]) / 2
ori_inp = cropBox(ori_img, upLeft, bottomRight, opt.inputResH,
opt.inputResW)
inp = cropBox(img, upLeft, bottomRight, opt.inputResH, opt.inputResW)
if jointNum == 0:
inp = torch.zeros(3, opt.inputResH, opt.inputResW)
out_bigcircle = torch.zeros(nJoints, opt.outputResH, opt.outputResW)
out_smallcircle = torch.zeros(nJoints, opt.outputResH, opt.outputResW)
out = torch.zeros(nJoints, opt.outputResH, opt.outputResW)
setMask = torch.zeros(nJoints, opt.outputResH, opt.outputResW)
# Draw Label
if imgset == 'coco':
for i in range(nJoints_coco):
if part[i][0] > 0 and part[i][0] > upLeft[0] and part[i][1] > upLeft[1] \
and part[i][0] < bottomRight[0] and part[i][1] < bottomRight[1]:
out_bigcircle[i] = drawBigCircle(
out_bigcircle[i],
transformBox(part[i], upLeft, bottomRight, opt.inputResH,
opt.inputResW, opt.outputResH,
opt.outputResW), opt.hmGauss * 2)
out_smallcircle[i] = drawSmallCircle(
out_smallcircle[i],
transformBox(part[i], upLeft, bottomRight, opt.inputResH,
opt.inputResW, opt.outputResH,
opt.outputResW), opt.hmGauss)
out[i] = drawGaussian(
out[i],
transformBox(part[i], upLeft, bottomRight, opt.inputResH,
opt.inputResW, opt.outputResH,
opt.outputResW), opt.hmGauss)
setMask[i].add_(1)
elif imgset == 'mpii':
for i in range(nJoints_coco, nJoints_coco + nJoints_mpii):
if part[i - nJoints_coco][0] > 0 and part[i - nJoints_coco][0] > upLeft[0] and part[i - nJoints_coco][1] > upLeft[1] \
and part[i - nJoints_coco][0] < bottomRight[0] and part[i - nJoints_coco][1] < bottomRight[1]:
out_bigcircle[i] = drawBigCircle(
out_bigcircle[i],
transformBox(part[i - nJoints_coco], upLeft, bottomRight,
opt.inputResH, opt.inputResW, opt.outputResH,
opt.outputResW), opt.hmGauss * 2)
out_smallcircle[i] = drawSmallCircle(
out_smallcircle[i],
transformBox(part[i - nJoints_coco], upLeft, bottomRight,
opt.inputResH, opt.inputResW, opt.outputResH,
opt.outputResW), opt.hmGauss)
out[i] = drawGaussian(
out[i],
transformBox(part[i - nJoints_coco], upLeft, bottomRight,
opt.inputResH, opt.inputResW, opt.outputResH,
opt.outputResW), opt.hmGauss)
setMask[i].add_(1)
else:
for i in range(nJoints_coco, nJoints_coco + nJoints_mpii):
if part[i - nJoints_coco][0] > 0 and part[i - nJoints_coco][0] > upLeft[0] and part[i - nJoints_coco][1] > upLeft[1] \
and part[i - nJoints_coco][0] < bottomRight[0] and part[i - nJoints_coco][1] < bottomRight[1]:
out_bigcircle[i] = drawBigCircle(
out_bigcircle[i],
transformBox(part[i - nJoints_coco], upLeft, bottomRight,
opt.inputResH, opt.inputResW, opt.outputResH,
opt.outputResW), opt.hmGauss * 2)
out_smallcircle[i] = drawSmallCircle(
out_smallcircle[i],
transformBox(part[i - nJoints_coco], upLeft, bottomRight,
opt.inputResH, opt.inputResW, opt.outputResH,
opt.outputResW), opt.hmGauss)
out[i] = drawGaussian(
out[i],
transformBox(part[i - nJoints_coco], upLeft, bottomRight,
opt.inputResH, opt.inputResW, opt.outputResH,
opt.outputResW), opt.hmGauss)
if i != 6 + nJoints_coco and i != 7 + nJoints_coco:
setMask[i].add_(1)
if opt.debug:
preds_hm, preds_img, preds_scores = getPrediction(
out.unsqueeze(0), upLeft.unsqueeze(0), bottomRight.unsqueeze(0),
opt.inputResH, opt.inputResW, opt.outputResH, opt.outputResW)
tmp_preds = preds_hm.mul(opt.inputResH / opt.outputResH)
drawCOCO(ori_inp.unsqueeze(0), tmp_preds, preds_scores)
if train:
# Flip
if random.uniform(0, 1) < 0.5:
inp = flip(inp)
ori_inp = flip(ori_inp)
out_bigcircle = shuffleLR(flip(out_bigcircle), dataset)
out_smallcircle = shuffleLR(flip(out_smallcircle), dataset)
out = shuffleLR(flip(out), dataset)
# Rotate
r = rnd(opt.rotate)
if random.uniform(0, 1) < 0.6:
r = 0
if r != 0:
inp = cv_rotate(inp, r, opt.inputResW, opt.inputResH)
out_bigcircle = cv_rotate(out_bigcircle, r, opt.outputResW,
opt.outputResH)
out_smallcircle = cv_rotate(out_smallcircle, r, opt.outputResW,
opt.outputResH)
out = cv_rotate(out, r, opt.outputResW, opt.outputResH)
return inp, out_bigcircle, out_smallcircle, out, setMask
def generate_dataset(self, mode='train'):
assert mode in ['train', 'test']
try:
if mode == 'train':
os.mkdir(self.train_path)
os.mkdir(os.path.join(self.train_path, 'images'))
else:
os.mkdir(self.test_path)
os.mkdir(os.path.join(self.test_path, 'images'))
print(f'created data/{mode} folder')
except:
print(
f"data/{mode} folder already exist .. delete it to generate a new dataset"
)
return
lines = self.train_lines if mode == 'train' else self.test_lines
save_path = self.train_path if mode == 'train' else self.test_path
# annotation for all train/test dataset strings
all_annotations_str = []
k = 0
for annotations_line in lines:
# read annotations
annotations = self.read_annotations(annotations_line)
image_full_path = annotations['path']
image = self.read_image(image_full_path)
rect = annotations['rect']
landmarks = annotations['landmarks']
attributes = annotations['attributes']
# ============= Data Augumentation =================
all_images = []
all_landmarks = []
if mode == 'test':
image, landmarks, skip = self.crop_face_landmarks(
image, landmarks, False)
if skip:
continue
all_images = [image]
all_landmarks = [landmarks]
else:
for i in range(self.n_augumentation):
angle = np.random.randint(-30, 30)
augument_image, augument_landmarks = self.rotate(
np.copy(image), np.copy(landmarks), angle)
augument_image, augument_landmarks, skip = self.crop_face_landmarks(
augument_image, augument_landmarks)
if skip:
continue
if np.random.choice((True, False)):
augument_image, augument_landmarks = flip(
augument_image, augument_landmarks)
# # visualize
# img = np.copy(augument_image)
# for point in augument_landmarks:
# point = (int(point[0]), int(point[1]))
# cv2.circle(img, point, 1, (0,255,0), -1)
# # img = cv2.resize(img, (300,300))
# cv2.imshow("image", img)
# if cv2.waitKey(0) == 27:
# exit(0)
# print("*"*80)
all_images.append(augument_image)
all_landmarks.append(augument_landmarks)
# for every augumented image
for i, img in enumerate(all_images):
img = all_images[i]
landmark = all_landmarks[i] / 112
# generate euler angles from landmarks
_, _, euler_angles = self.euler_estimator.eular_angles_from_landmarks(
landmark)
euler_str = ' '.join(
[str(round(angle, 2)) for angle in euler_angles])
# get image name
new_image_path = self.save_image(
img, image_full_path, k, i,
save_path) # id should be unique for every img
# convert landmarks to string
landmarks_list = landmark.reshape(196, ).tolist()
landmarks_str = ' '.join([str(l) for l in landmarks_list])
# attributes list to string
attributes_str = ' '.join(
[str(attribute) for attribute in attributes])
# annotation string = image_name + 98 landmarks + attributes + euler
new_annotation = ' '.join(
[new_image_path, landmarks_str, attributes_str, euler_str])
all_annotations_str.append(new_annotation)
# print(new_annotation)
k += 1
if k % 100 == 0:
print(f'{mode} dataset: {k} generated data')
# ========= Save annotations ===============
one_annotation_str = '\n'.join(
[annotation for annotation in all_annotations_str])
annotations_path = os.path.join(save_path, 'annotations.txt')
annotations_file = open(annotations_path, 'w')
annotations_file.write(one_annotation_str)
annotations_file.close()
print(
'*' * 60,
f'\n\t {mode} annotations is saved @ data/{mode}/annotations.txt')
time.sleep(2)
def generate_hdf5(ftxt, output, fname, argument=False):
data = getDataFromTxt(ftxt)
F_imgs = []
F_landmarks = []
EN_imgs = []
EN_landmarks = []
NM_imgs = []
NM_landmarks = []
for (imgPath, bbox, landmarkGt) in data:
img = cv2.imread(imgPath, cv2.IMREAD_GRAYSCALE) #读取灰度图
assert (img is not None)
logger("process %s" % imgPath)
# F
f_bbox = bbox.subBBox(-0.05, 1.05, -0.05, 1.05)
f_face = img[f_bbox.top:f_bbox.bottom + 1,
f_bbox.left:f_bbox.right + 1] #截图
## data argument
if argument and np.random.rand() > -1:
### flip
face_flipped, landmark_flipped = flip(
f_face, landmarkGt) #图片翻转，数据增强，相应坐标也要发生变换
face_flipped = cv2.resize(face_flipped, (39, 39)) #固定图片大小
F_imgs.append(face_flipped.reshape((
1, 39,
39))) #caffe要求输入的格式 ['batch_size','channel','height','width']
F_landmarks.append(landmark_flipped.reshape(10)) #把10个关键坐标点值变成一维向量
### rotation
# if np.random.rand() > 0.5:
# face_rotated_by_alpha, landmark_rotated = rotate(img, f_bbox,
# bbox.reprojectLandmark(landmarkGt), 5)
# landmark_rotated = bbox.projectLandmark(landmark_rotated)
# face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha, (39, 39))
# F_imgs.append(face_rotated_by_alpha.reshape((1, 39, 39)))
# F_landmarks.append(landmark_rotated.reshape(10))
# ### flip with rotation
# face_flipped, landmark_flipped = flip(face_rotated_by_alpha, landmark_rotated)
# face_flipped = cv2.resize(face_flipped, (39, 39))
# F_imgs.append(face_flipped.reshape((1, 39, 39)))
# F_landmarks.append(landmark_flipped.reshape(10))
# ### rotation
# if np.random.rand() > 0.5:
# face_rotated_by_alpha, landmark_rotated = rotate(img, f_bbox,
# bbox.reprojectLandmark(landmarkGt), -5)
# landmark_rotated = bbox.projectLandmark(landmark_rotated)
# face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha, (39, 39))
# F_imgs.append(face_rotated_by_alpha.reshape((1, 39, 39)))
# F_landmarks.append(landmark_rotated.reshape(10))
# ### flip with rotation
# face_flipped, landmark_flipped = flip(face_rotated_by_alpha, landmark_rotated)
# face_flipped = cv2.resize(face_flipped, (39, 39))
# F_imgs.append(face_flipped.reshape((1, 39, 39)))
# F_landmarks.append(landmark_flipped.reshape(10))
f_face = cv2.resize(f_face, (39, 39))
en_face = f_face[:31, :]
nm_face = f_face[8:, :]
f_face = f_face.reshape((1, 39, 39))
f_landmark = landmarkGt.reshape((10))
F_imgs.append(f_face)
F_landmarks.append(f_landmark)
# EN
# en_bbox = bbox.subBBox(-0.05, 1.05, -0.04, 0.84)
# en_face = img[en_bbox.top:en_bbox.bottom+1,en_bbox.left:en_bbox.right+1]
## data argument
if argument and np.random.rand() > 0.5:
### flip
face_flipped, landmark_flipped = flip(en_face, landmarkGt)
face_flipped = cv2.resize(face_flipped, (31, 39)).reshape(
(1, 31, 39))
landmark_flipped = landmark_flipped[:3, :].reshape((6))
EN_imgs.append(face_flipped)
EN_landmarks.append(landmark_flipped)
en_face = cv2.resize(en_face, (31, 39)).reshape((1, 31, 39))
en_landmark = landmarkGt[:3, :].reshape((6))
EN_imgs.append(en_face)
EN_landmarks.append(en_landmark)
# NM
# nm_bbox = bbox.subBBox(-0.05, 1.05, 0.18, 1.05)
# nm_face = img[nm_bbox.top:nm_bbox.bottom+1,nm_bbox.left:nm_bbox.right+1]
## data argument
if argument and np.random.rand() > 0.5:
### flip
face_flipped, landmark_flipped = flip(nm_face, landmarkGt)
face_flipped = cv2.resize(face_flipped, (31, 39)).reshape(
(1, 31, 39))
landmark_flipped = landmark_flipped[2:, :].reshape((6))
NM_imgs.append(face_flipped)
NM_landmarks.append(landmark_flipped)
nm_face = cv2.resize(nm_face, (31, 39)).reshape((1, 31, 39))
nm_landmark = landmarkGt[2:, :].reshape((6))
NM_imgs.append(nm_face)
NM_landmarks.append(nm_landmark)
#imgs, landmarks = process_images(ftxt, output)
F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)
EN_imgs, EN_landmarks = np.asarray(EN_imgs), np.asarray(EN_landmarks)
NM_imgs, NM_landmarks = np.asarray(NM_imgs), np.asarray(NM_landmarks)
F_imgs = processImage(F_imgs) #数据标准化
shuffle_in_unison_scary(F_imgs, F_landmarks) #随机打乱
EN_imgs = processImage(EN_imgs)
shuffle_in_unison_scary(EN_imgs, EN_landmarks)
NM_imgs = processImage(NM_imgs)
shuffle_in_unison_scary(NM_imgs, NM_landmarks)
# full face
base = join(OUTPUT, '1_F')
createDir(base)
output = join(base, fname) #D:.\deep_landmark\dataset\train\1_F\train.h5
logger("generate %s" % output)
with h5py.File(output, 'w') as h5:
h5['data'] = F_imgs.astype(np.float32)
h5['landmark'] = F_landmarks.astype(np.float32)
# eye and nose
base = join(OUTPUT, '1_EN')
createDir(base)
output = join(base, fname)
logger("generate %s" % output)
with h5py.File(output, 'w') as h5:
h5['data'] = EN_imgs.astype(np.float32)
h5['landmark'] = EN_landmarks.astype(np.float32)
# nose and mouth
base = join(OUTPUT, '1_NM')
createDir(base)
output = join(base, fname)
logger("generate %s" % output)
with h5py.File(output, 'w') as h5:
h5['data'] = NM_imgs.astype(np.float32)
h5['landmark'] = NM_landmarks.astype(np.float32)
def fog_train(args,
model,
fog_graph,
nodes,
X_trains,
y_trains,
device,
epoch,
loss_fn,
consensus,
rounds,
radius,
d2d,
factor=10,
alpha_store={},
prev_grad=0,
shuffle_worker_data=False):
# fog learning with model averaging
if loss_fn == 'nll':
loss_fn_ = F.nll_loss
elif loss_fn == 'hinge':
loss_fn_ = multiClassHingeLoss()
log = []
log_head = []
if args.var_theta:
if args.true_eps:
log_head.append('est')
log_head += ['div', 'true_grad']
if args.dynamic_alpha:
log_head += [
'D', 'mu', args.delta_or_psi, 'eta', 'grad', 'omega', 'N', 'L',
'phi'
]
log_head += ['rounds', 'agg', 'rho', 'sig', 'cls_n']
log_head.append('rounded')
log.append(log_head)
model.train()
worker_data = {}
worker_targets = {}
worker_num_samples = {}
worker_models = {}
worker_optims = {}
worker_losses = {}
# send data, model to workers
# setup optimizer for each worker
if shuffle_worker_data:
data = list(zip(X_trains, y_trains))
shuffle(data)
X_trains, y_trains = zip(*data)
workers = [_ for _ in nodes.keys() if 'L0' in _]
for w, x, y in zip(workers, X_trains, y_trains):
worker_data[w] = x.send(nodes[w])
worker_targets[w] = y.send(nodes[w])
worker_num_samples[w] = x.shape[0]
for w in workers:
worker_models[w] = model.copy().send(nodes[w])
node_model = worker_models[w].get()
worker_optims[w] = optim.SGD(
params=node_model.parameters(),
lr=args.lr * np.exp(-0.01 * epoch) if args.nesterov else args.lr,
weight_decay=args.decay if loss_fn == 'hinge' else 0,
)
data = worker_data[w].get()
target = worker_targets[w].get()
dataloader = get_dataloader(data, target, args.batch_size)
for data, target in dataloader:
data, target = data.to(device), target.to(device)
worker_optims[w].zero_grad()
output = node_model(data)
loss = loss_fn_(output, target)
loss.backward()
worker_optims[w].step()
worker_models[w] = node_model.send(nodes[w])
worker_losses[w] = loss.item()
num_rounds = []
num_div = []
var_radius = type(radius) == list
for l in range(1, len(args.num_clusters) + 1):
aggregators = [_ for _ in nodes.keys() if 'L{}'.format(l) in _]
N = len(aggregators)
cluster_rounds = []
cluster_div = []
for a in aggregators:
agg_log = []
worker_models[a] = model.copy().send(nodes[a])
worker_num_samples[a] = 1
children = fog_graph[a]
for child in children:
worker_models[child].move(nodes[a])
if consensus == 'averaging' or flip(1 - d2d):
model_sum = averaging_consensus(children, worker_models,
worker_num_samples)
worker_models[a].load_state_dict(model_sum)
elif consensus == 'laplacian':
num_nodes_in_cluster = len(children)
V = consensus_matrix(
num_nodes_in_cluster,
radius if not var_radius else radius[l - 1], factor,
args.topology)
eps = get_cluster_eps(children, worker_models,
worker_num_samples, nodes, fog_graph)
if args.true_eps:
est_eps = eps
agg_log.append(est_eps)
eps = get_true_cluster_eps(children, worker_models,
worker_num_samples, nodes,
fog_graph)
agg_log.append(eps)
cluster_div.append(eps)
if args.var_theta:
Z = V - (1 / num_nodes_in_cluster)
eig, dump = np.linalg.eig(Z)
lamda = eig.max()
true_grad = estimate_true_gradient(prev_grad, args.omega)
agg_log.append(true_grad)
if args.dynamic_alpha:
if true_grad:
phi = sum(args.num_clusters)
L = len(args.num_clusters) + 1
num_params = get_num_params(model)
if args.delta_or_psi == 'delta':
alpha, alpha_log = get_alpha_closed_form(
args, true_grad, phi, N, L, num_params, l)
elif args.delta_or_psi == 'psi':
alpha, alpha_log = get_alpha_using_psi(
args, phi, N, L, num_params, l)
agg_log += alpha_log
rounds = estimate_rounds(alpha,
num_nodes_in_cluster, eps,
lamda)
else:
rounds = 50
agg_log += [''] * 9
alpha = 'N/A'
else:
alpha_store = get_alpha(num_nodes_in_cluster, eps, a,
alpha_store, args.alpha,
args.dynamic_alpha)
alpha = alpha_store[a]
rounds = estimate_rounds(alpha, num_nodes_in_cluster,
eps, lamda)
agg_log += [rounds, a, lamda, alpha, num_nodes_in_cluster]
try:
rounds = int(np.ceil(rounds))
except TypeError:
rounds = 50
if rounds > 50:
rounds = 50
elif rounds < 1:
rounds = 1
cluster_rounds.append(rounds)
agg_log.append(rounds)
model_sum = laplacian_consensus(children, worker_models,
worker_num_samples,
V.to(device), rounds)
agg_model = worker_models[a].get()
agg_model.load_state_dict(model_sum)
worker_models[a] = agg_model.send(nodes[a])
else:
raise Exception
log.append(agg_log)
num_rounds.append(cluster_rounds)
num_div.append(cluster_div)
table = AsciiTable(log)
print(table.table)
assert len(aggregators) == 1
master = get_model_weights(worker_models[aggregators[0]].get(),
1 / args.num_train)
grad = model_gradient(model.state_dict(), master, args.lr)
model.load_state_dict(master)
if epoch % args.log_interval == 0:
loss = np.array([_ for dump, _ in worker_losses.items()])
print('Train Epoch: {}({}) \tLoss: {:.6f} +- {:.6f} \tGrad: {}'.format(
epoch, len(dataloader), loss.mean(), loss.std(),
dict(grad).values()))
return grad, num_rounds, num_div, alpha_store
def split(self, rect):
if rect.w - 1 >= MIN_BSP_SIZE * 1.5 or rect.h - 1 >= MIN_BSP_SIZE * 1.5:
if utils.flip() and rect.w >= MIN_BSP_SIZE:
x = libtcod.random_get_int(0, rect.x + MIN_BSP_SIZE,
rect.x + rect.w - MIN_BSP_SIZE)
w = (rect.x + rect.w) - x
tries = 0
while x + w >= len(self.map) or tries <= 3:
print x, w, " horz out of bounds"
x = rect.x + 1
w = rect.w / 2 - 1
baby = Rect(rect.w - w, rect.h, rect.x, rect.y)
baby.bsp(rect)
baby_2 = Rect(w, rect.h, x, rect.y)
baby_2.bsp(rect)
rect.babies.append(baby)
rect.babies.append(baby_2)
else:
if rect.w < MIN_BSP_SIZE:
y = libtcod.random_get_int(0, rect.y + MIN_BSP_SIZE,
rect.y + rect.h - MIN_BSP_SIZE)
h = (rect.y + rect.h) - y
tries = 0
while y + h > len(self.map[0]) or tries <= 3:
print y, h, "vert out of bounds"
y = rect.y + 1
h = rect.h / 2
baby = Rect(rect.w, rect.h - h, rect.x, rect.y)
baby.bsp(rect)
rect.babies.append(baby)
else:
y = libtcod.random_get_int(0, rect.y + MIN_BSP_SIZE,
rect.y + rect.h - MIN_BSP_SIZE)
h = (rect.y + rect.h) - y
tries = 0
while y + h > len(self.map[0]) or tries <= 3:
print y, h, "vert out of bounds"
y = rect.y + 1
h = rect.h / 2
baby = Rect(rect.w, rect.h - h, rect.x, rect.y)
baby.bsp(rect)
baby_2 = Rect(rect.w, h, rect.x, y)
baby_2.bsp(rect)
rect.babies.append(baby)
rect.babies.append(baby_2)
else:
rect.end = True
for baby in rect.babies:
if baby.end != True:
self.split(baby)
#
# You should have received a copy of the GNU General Public License
# along with this software; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street,
# Boston, MA 02110-1301, USA.
import sys
sys.path.insert(0, './extras/')
sys.path.insert(0, './')
import apt
import utils
import numpy as np
import scipy.signal
from PIL import Image
matrix = apt.decode('wav/am_demod/sample.wav')
utils.plot_histogram(matrix,'Imagen completa')
utils.plot_image(matrix,'Imagen completa')
# Giro la imagen 180 grados porque el satélite recorría de Sur a Norte
frameA = utils.flip(utils.get_frame(matrix,"A"))
frameB = utils.flip(utils.get_frame(matrix,"B"))
#
utils.plot_histogram(frameB,'Histograma Banda Infrarroja', save = True)
utils.plot_histogram(frameA,'Histograma Espectro visible', save = True)
def epsilon_linear_policy(self, epsilon, w, s):
best = np.argmax([np.dot(w,self.phi(s,a)) for a in range(self.nactions)])
if flip(epsilon):
return pr.choice(self.actions)
else:
return best
X_train, X_test, Y_train, Y_test, A_train, A_test = train_test_split(X_scaled,
Y,
A,
test_size = 0.2,
random_state=0,
stratify=Y)
# Work around indexing bug
X_train = X_train.reset_index(drop=True)
A_train = A_train.reset_index(drop=True)
X_test = X_test.reset_index(drop=True)
A_test = A_test.reset_index(drop=True)
# A_test = A_test.map({ 0:"female", 1:"male"})
# flip across different groups
Y_noised = flip(Y_train, A_train, error_rate=error_rate)
noise_matrix = generate_noise_matrix(Y_noised, Y_train)
est_error_rate = estimation(X_train.values, Y_noised, A_train.values, ngroups=2**args.ngroups)
print(f"True error rate is {error_rate}.\nEstimated error rate is {est_error_rate}.")
# Learning with Noisy Labels
lnl = LearningWithNoisyLabels(clf=LogisticRegression())
lnl.fit(X=X_train.values, s=Y_noised, noise_matrix=noise_matrix)
Y_lnlt = lnl.predict(X_train.values).astype(int)
lnl.fit(X=X_train.values, s=Y_noised)
Y_lnle = lnl.predict(X_train.values).astype(int)
def run_corrupt(fairness_constraints):
all_results = {}
all_results['eps'] = fairness_constraints
for epoch in range(1, args.max_epoch + 1):
lr_scheduler.step()
model.train()
tl = Averager()
ta = Averager()
for i, batch in enumerate(train_loader, 1):
data, _ = [_.cuda() for _ in batch]
p = args.shot * args.train_way
qq = p + args.query * args.train_way
data_shot, data_query = data[:p], data[p:qq]
if args.shot == 1:
data_shot = torch.cat((data_shot, flip(data_shot, 3)), dim=0)
proto = model(data_shot)
proto = proto.reshape(shot_num, args.train_way, -1)
proto = torch.transpose(proto, 0, 1)
hyperplanes, mu = projection_pro.create_subspace(proto, args.train_way, shot_num)
label = torch.arange(args.train_way).repeat(args.query)
label = label.type(torch.cuda.LongTensor)
logits, disc = projection_pro.projection_metric(model(data_query), hyperplanes, mu=mu)
loss = F.cross_entropy(logits, label) + 0.05*disc
acc = count_acc(logits, label)
tl.add(loss.item())
def generate_hdf5(ftxt, output, fname, argument=False):
data = getDataFromTxt(ftxt)
F_imgs = []
F_landmarks = []
EN_imgs = []
EN_landmarks = []
NM_imgs = []
NM_landmarks = []
for (imgPath, bbox, landmarkGt) in data:
img = cv2.imread(imgPath, cv2.CV_LOAD_IMAGE_GRAYSCALE)
assert (img is not None)
logger("process %s" % imgPath)
# F
f_bbox = bbox.subBBox(-0.05, 1.05, -0.05, 1.05)
f_face = img[f_bbox.top:f_bbox.bottom + 1,
f_bbox.left:f_bbox.right + 1]
## data argument
if argument and np.random.rand() > -1:
### flip
face_flipped, landmark_flipped = flip(f_face, landmarkGt)
face_flipped = cv2.resize(face_flipped, (39, 39))
F_imgs.append(face_flipped.reshape((1, 39, 39)))
F_landmarks.append(landmark_flipped.reshape(10))
### rotation
if np.random.rand() > 0.5:
face_rotated_by_alpha, landmark_rotated = rotate(img, f_bbox, \
bbox.reprojectLandmark(landmarkGt), 5)
landmark_rotated = bbox.projectLandmark(landmark_rotated)
face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha,
(39, 39))
F_imgs.append(face_rotated_by_alpha.reshape((1, 39, 39)))
F_landmarks.append(landmark_rotated.reshape(10))
### flip with rotation
face_flipped, landmark_flipped = flip(face_rotated_by_alpha,
landmark_rotated)
face_flipped = cv2.resize(face_flipped, (39, 39))
F_imgs.append(face_flipped.reshape((1, 39, 39)))
F_landmarks.append(landmark_flipped.reshape(10))
### rotation
if np.random.rand() > 0.5:
face_rotated_by_alpha, landmark_rotated = rotate(img, f_bbox, \
bbox.reprojectLandmark(landmarkGt), -5)
landmark_rotated = bbox.projectLandmark(landmark_rotated)
face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha,
(39, 39))
F_imgs.append(face_rotated_by_alpha.reshape((1, 39, 39)))
F_landmarks.append(landmark_rotated.reshape(10))
### flip with rotation
face_flipped, landmark_flipped = flip(face_rotated_by_alpha,
landmark_rotated)
face_flipped = cv2.resize(face_flipped, (39, 39))
F_imgs.append(face_flipped.reshape((1, 39, 39)))
F_landmarks.append(landmark_flipped.reshape(10))
f_face = cv2.resize(f_face, (39, 39))
en_face = f_face[:31, :]
nm_face = f_face[8:, :]
f_face = f_face.reshape((1, 39, 39))
f_landmark = landmarkGt.reshape((10))
F_imgs.append(f_face)
F_landmarks.append(f_landmark)
# EN
# en_bbox = bbox.subBBox(-0.05, 1.05, -0.04, 0.84)
# en_face = img[en_bbox.top:en_bbox.bottom+1,en_bbox.left:en_bbox.right+1]
## data argument
if argument and np.random.rand() > 0.5:
### flip
face_flipped, landmark_flipped = flip(en_face, landmarkGt)
face_flipped = cv2.resize(face_flipped, (31, 39)).reshape(
(1, 31, 39))
landmark_flipped = landmark_flipped[:3, :].reshape((6))
EN_imgs.append(face_flipped)
EN_landmarks.append(landmark_flipped)
en_face = cv2.resize(en_face, (31, 39)).reshape((1, 31, 39))
en_landmark = landmarkGt[:3, :].reshape((6))
EN_imgs.append(en_face)
EN_landmarks.append(en_landmark)
# NM
# nm_bbox = bbox.subBBox(-0.05, 1.05, 0.18, 1.05)
# nm_face = img[nm_bbox.top:nm_bbox.bottom+1,nm_bbox.left:nm_bbox.right+1]
## data argument
if argument and np.random.rand() > 0.5:
### flip
face_flipped, landmark_flipped = flip(nm_face, landmarkGt)
face_flipped = cv2.resize(face_flipped, (31, 39)).reshape(
(1, 31, 39))
landmark_flipped = landmark_flipped[2:, :].reshape((6))
NM_imgs.append(face_flipped)
NM_landmarks.append(landmark_flipped)
nm_face = cv2.resize(nm_face, (31, 39)).reshape((1, 31, 39))
nm_landmark = landmarkGt[2:, :].reshape((6))
NM_imgs.append(nm_face)
NM_landmarks.append(nm_landmark)
#imgs, landmarks = process_images(ftxt, output)
F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)
EN_imgs, EN_landmarks = np.asarray(EN_imgs), np.asarray(EN_landmarks)
NM_imgs, NM_landmarks = np.asarray(NM_imgs), np.asarray(NM_landmarks)
F_imgs = processImage(F_imgs)
shuffle_in_unison_scary(F_imgs, F_landmarks)
EN_imgs = processImage(EN_imgs)
shuffle_in_unison_scary(EN_imgs, EN_landmarks)
NM_imgs = processImage(NM_imgs)
shuffle_in_unison_scary(NM_imgs, NM_landmarks)
# full face
base = join(OUTPUT, '1_F')
createDir(base)
output = join(base, fname)
logger("generate %s" % output)
with h5py.File(output, 'w') as h5:
h5['data'] = F_imgs.astype(np.float32)
h5['landmark'] = F_landmarks.astype(np.float32)
# eye and nose
base = join(OUTPUT, '1_EN')
createDir(base)
output = join(base, fname)
logger("generate %s" % output)
with h5py.File(output, 'w') as h5:
h5['data'] = EN_imgs.astype(np.float32)
h5['landmark'] = EN_landmarks.astype(np.float32)
# nose and mouth
base = join(OUTPUT, '1_NM')
createDir(base)
output = join(base, fname)
logger("generate %s" % output)
with h5py.File(output, 'w') as h5:
h5['data'] = NM_imgs.astype(np.float32)
h5['landmark'] = NM_landmarks.astype(np.float32)
satellite = sat.NOAA_15()
'''
Display Raw Image (flipped if needed..)
'''
display = True
if display == True:
img = Image.fromarray(matrix)
img.show()
img.histogram()
img.save(image_dir + prefix + '_raw_image.png')
'''
Sobel Filter
'''
if flip_condition:
matrix_filtered = utils.flip(matrix)
img_filtered = utils.sobel_filter(matrix_filtered)
img_filtered.astype(np.uint8)
display = True
if display == True:
img = Image.fromarray(img_filtered)
if img.mode != 'RGB':
img = img.convert('RGB')
img.show()
img.save(image_dir + prefix + '_sobel_filter.png')
'''
Normalize image with Telemetry Frame
'''
frame = "A"