def main():
# Parse command line arguments
parser = argparse.ArgumentParser()
parser.add_argument('--data_folder',
help="Data from the Platform",
required=True)
parser.add_argument('--trained_model', help="Trained model", required=True)
args = parser.parse_args()
# Parameters
data_folder = args.data_folder
trained_model = args.trained_model
# Configurations
SETUP_PATH = 'configuration_test.yml'
configurations = Configurations(SETUP_PATH)
eprint(''.join("%s:\t%s\n" % item
for item in vars(configurations).items()))
# Data
get_data_test(data_folder, configurations, trained_model)
# Inference
# predictions = test_model(X_test, data_folder, trained_model, configurations)
# for i in range(5):
generate_visuals(data_folder,
os.path.join(configurations.output_folder, 'Prediction/'),
thold_area=0)
def _recursive_get_urls(crawled_urls,
test_page,
max_urls,
parent_url,
domain,
depth=0):
if depth == 0 or len(crawled_urls) == max_urls:
return crawled_urls
asyncio.get_event_loop().run_until_complete(get_page(
test_page, parent_url))
html = test_page.source
soup = BeautifulSoup(html, features='html.parser')
urls = soup.findAll('a')
for a in set(urls):
url = a.get('href')
if url is None:
continue
if url.startswith('/'):
url = parent_url.rstrip('/') + url
if urlparse(url).netloc == domain and url not in crawled_urls:
if len(crawled_urls) <= max_urls:
crawled_urls.append(url)
eprint('[LOG] Added: {}'.format(url))
_recursive_get_urls(crawled_urls, max_urls, url, domain,
depth - 1)
def predict(image_file):
model_path = os.path.join('inference/model_files', 'frednetv2.pth')
if not os.path.exists(model_path):
eprint("[ERR] Model file does not exist")
exit(4)
model = NNet()
model.load_state_dict(torch.load(model_path, map_location='cpu'))
model.eval()
with torch.no_grad():
pilim = Image.open(image_file).convert('L').convert('RGB')
pilim = preprocess_pilim(pilim)
input_array = prepare_for_input(pilim, flip_lr=False)
lr_input_array = prepare_for_input(pilim, flip_lr=True)
try:
out_array = get_output(model(get_tensor(input_array)))
except:
exit(2)
lr_out_array = np.fliplr(get_output(model(get_tensor(lr_input_array))))
out_array = (out_array + lr_out_array) / 2
out_array = threshold_output(out_array, 0.5)
out_array *= 255
out_array = np.array(out_array, dtype='uint8')
return out_array
def get_recursive_urls(parent_url, max_depth, max_urls):
scraped_urls = [parent_url]
domain = urlparse(parent_url).netloc
page = MyPage()
asyncio.get_event_loop().run_until_complete(get_page(page, parent_url))
_recursive_get_urls(scraped_urls,
page,
max_urls,
parent_url,
domain,
depth=max_depth)
eprint('[LOG] Finished crawling URLs for {}'.format(parent_url))
return scraped_urls
def get_data_test(data_folder, configurations, trained_model):
# Parameters
IMG_WIDTH = configurations.size_img
IMG_HEIGHT = configurations.size_img
IMG_CHANNELS = 3
TEST_PATH = data_folder
COUNT = configurations.sample_count
# Path of Image Tiles and Masks
path = os.path.join(TEST_PATH, "img")
# path_mask = os.path.join(TEST_PATH, "mask")
# total = int(sum([len(files) for r, d, files in os.walk(path)]))
eprint(
f'[DEBUG][get_data_test] Getting and Resizing({IMG_WIDTH}x{IMG_HEIGHT}) Test Images and Masks... '
)
# Get and resize Test images and masks
# test_cpt = int(sum([len(files) for r, d, files in os.walk(path)]))
# X_test = np.ndarray((test_cpt, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.float32)
# Y_test = np.ndarray((test_cpt, IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.float32) # dtype=np.bool)
eprint(
f'[DEBUG][get_data_test] Getting and Resizing Test Images and Masks Done!\nPath to img: {path}'
)
sys.stdout.flush()
_, _, files_orj = next(os.walk(path))
# _, _, files_mask = next(os.walk(path_mask))
files_orj = sorted(files_orj)
# files_mask = sorted(files_mask)
eprint(f'[DEBUG][get_data_test] Number of Image Tiles: {len(files_orj)}')
# for i, f in enumerate(files_orj[:COUNT]):
# img = cv2.imread(os.path.join(path, f))
# img = cv2.resize(img, (IMG_HEIGHT, IMG_WIDTH), interpolation=cv2.INTER_AREA)
# img = img / 255
# X_test[i] = img
# for i, fm in enumerate(files_mask[:COUNT]):
# img_mask = cv2.imread(os.path.join(path_mask, fm), cv2.IMREAD_GRAYSCALE)
# img_mask = cv2.resize(img_mask, (IMG_HEIGHT, IMG_WIDTH), interpolation=cv2.INTER_AREA)
# img_mask = img_mask / 255
# img_mask = np.expand_dims(img_mask, axis=-1)
# Y_test[i] = img_mask
# Load Trained Model
model = load_model(trained_model,
custom_objects={
'dice_coef': dice_coef,
'dice_coef_loss': dice_coef_loss
})
for i, f in enumerate(files_orj):
X_test = np.ndarray((1, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS),
dtype=np.float32)
img = cv2.imread(os.path.join(path, f))
img = cv2.resize(img, (IMG_HEIGHT, IMG_WIDTH),
interpolation=cv2.INTER_AREA)
img = img / 255
X_test[0] = img
# predictions = test_model(X_test, data_folder, trained_model, configurations)
# Predict
preds_test = model.predict(X_test)
preds_reshaped = np.ndarray((1, IMG_HEIGHT, IMG_WIDTH),
dtype=np.float32)
preds_reshaped[0] = preds_test[0].reshape(IMG_HEIGHT, IMG_WIDTH)
preds_upsampled = [
np.expand_dims(cv2.resize(preds_reshaped[0],
(IMG_HEIGHT, IMG_WIDTH)),
axis=-1)
]
print("[INFO] Upsampling is done!(upsampled to ({}, {}) from ({}, {})".
format(IMG_HEIGHT, IMG_WIDTH, preds_test[0].shape[0],
preds_test[0].shape[1]))
output_pred = os.path.join(configurations.output_folder, 'Prediction')
mkdir_if_not_exist(configurations.output_folder)
mkdir_if_not_exist(output_pred)
theshold_pred = 0.5
img = preds_upsampled[0].copy()
img_raw = img * 255
out_name_raw = os.path.join(output_pred, "pred-raw-" + files_orj[i])
cv2.imwrite(out_name_raw, img_raw)
img[img > theshold_pred] = 1
img[img <= theshold_pred] = 0
img *= 255
out_name = os.path.join(output_pred, "pred-" + files_orj[i])
cv2.imwrite(out_name, img)
print('[INFO] Finished Prediction!')
def get_data(configurations, data_folder):
# Write Directory
dir_write = os.path.join(
configurations.dir_write,
'/Run_Train_' + configurations.model_name + '_' + str(current_time))
dir_pred = os.path.join(dir_write, 'Pred_imgs')
dir_model = os.path.join(dir_write, 'Model')
dir_log = os.path.join(dir_write, 'Log')
if not os.path.exists(dir_write):
os.makedirs(dir_write)
os.makedirs(dir_pred)
os.makedirs(dir_model)
os.makedirs(dir_log)
IMG_WIDTH = configurations.size_img
IMG_HEIGHT = configurations.size_img
IMG_CHANNELS = 3
TRAIN_PATH = data_folder
# Path of Image Tiles and Masks
print(data_folder)
path = os.path.join(TRAIN_PATH, "img")
path_mask = os.path.join(TRAIN_PATH, "mask")
path_bud_info = os.path.join(TRAIN_PATH, "Bud_Info")
eprint(
f'[DEBUG][get_data] Getting and Resizing({IMG_WIDTH}x{IMG_HEIGHT}) Train Images and Masks... '
)
# Get and resize train images and masks
train_cpt = int(
sum([len(files) for r, d, files in os.walk(TRAIN_PATH + "img/")]))
eprint(
f'[DEBUG][get_data] Getting and Resizing Train Images and Masks Done!\nPath to img: {path}'
)
sys.stdout.flush()
_, _, files_orj = next(os.walk(path))
_, _, files_mask = next(os.walk(path_mask))
files_orj = sorted(files_orj)
files_mask = sorted(files_mask)
eprint(
f'[DEBUG][get_data] Number of Image Tiles: {len(files_orj)}\t Number of Image Masks: {len(files_mask)}\n'
)
train_cpt_filtered = len(files_orj)
files_orj_filtered = files_orj
files_mask_filtered = files_mask
if int(configurations.thold_tbud) > 0:
train_cpt_filtered = 0
files_orj_filtered = []
files_mask_filtered = []
for i, f in enumerate(files_orj):
# Apply Bud Threshold
if filter_tbud_count(path_bud_info, f,
int(configurations.thold_tbud)):
train_cpt_filtered += 1
files_orj_filtered.append(files_orj[i])
files_mask_filtered.append(files_mask[i])
X_train = np.ndarray(
(train_cpt_filtered, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS),
dtype=np.float32)
Y_train = np.ndarray((train_cpt_filtered, IMG_HEIGHT, IMG_WIDTH, 1),
dtype=np.float32) # dtype=np.bool)
for i, f in enumerate(files_orj_filtered):
# # Apply Bud Threshold
# if not filter_tbud_count(path_bud_info, f, configurations.thold_tbud):
# continue
img = cv2.imread(os.path.join(path, f))
img = cv2.resize(img, (IMG_HEIGHT, IMG_WIDTH),
interpolation=cv2.INTER_AREA)
img = img / 255
X_train[i] = img
for i, fm in enumerate(files_mask_filtered):
# # Apply Bud Threshold
# if not filter_tbud_count(path_bud_info, fm, configurations.thold_tbud):
# continue
img_mask = cv2.imread(os.path.join(path_mask, fm),
cv2.IMREAD_GRAYSCALE)
img_mask = cv2.resize(img_mask, (IMG_HEIGHT, IMG_WIDTH),
interpolation=cv2.INTER_AREA)
img_mask = img_mask / 255
img_mask = np.expand_dims(img_mask, axis=-1)
Y_train[i] = img_mask
eprint(
f'[DEBUG][get_data] After Filter thold_tbud:{configurations.thold_tbud} Number of Image Tiles: {len(X_train)}\t Number of Image Masks: {len(Y_train)}\n'
)
eprint(
f"[DEBUG][INFO] Data Matrix: {round(X_train.nbytes / (1024 * 1000.0),3)} MB\n"
)
pixels = Y_train.flatten().reshape(train_cpt_filtered,
IMG_HEIGHT * IMG_WIDTH)
weights_train = pixels.copy()
pixels = np.expand_dims(pixels, axis=-1)
eprint(f"Data Read is Done!")
return X_train, pixels
def train_model(X, y, configurations):
# Parameters - IMG
IMG_HEIGHT = int(configurations.size_img)
IMG_WIDTH = int(configurations.size_img)
IMG_CHANNELS = 3
# Parameters - Model
lr_rate = float(configurations.learning_rate)
model_name = str(configurations.model_name)
model_type = str(configurations.model_type)
dir_write = mkdir_if_not_exist(str(configurations.dir_write))
activation = str(configurations.activation)
batch_size = int(configurations.batch_size)
epochs = int(configurations.epoch)
dropout_ratio = float(configurations.dropout_ratio)
dropout_level = int(configurations.dropout_level)
model_string = str(configurations.model_string)
eprint(f"[INFO][train_model] {model_string}")
# Free up RAM in case the model definition cells were run multiple times
K.clear_session()
# Stop training when a monitoring quantity has stopped improving
# earlystopper = EarlyStopping(monitor='val_loss', patience=100, verbose=1)
# Initialize the model
if model_type.lower() == 'resunet':
model = unetModel_residual(IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS, dropout_ratio=dropout_ratio, \
lr_rate=lr_rate, activation=activation, dropout_level=dropout_level)
else:
model = unetModel_basic_4(IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS, dropout_ratio=dropout_ratio, \
lr_rate=lr_rate, activation=activation, dropout_level=dropout_level)
# Save the model after every epoch
checkpointer = ModelCheckpoint(dir_write + "/" + model_string + '_main_modelCheckpoint.h5', verbose=0, monitor='val_loss', \
save_best_only=True, save_weights_only=False, period=1, mode='auto')
# Log training
csv_logger = CSVLogger('{}/log_{}.training.csv'.format(
dir_write, model_string))
# Reduce lr_rate on plateau
reduce_lr = ReduceLROnPlateau(monitor='val_dice_coef',
factor=0.5,
patience=10,
verbose=0,
mode='max',
cooldown=1,
min_lr=0.000001)
# Early stopping with patience
earlystopping = EarlyStopping(monitor='val_dice_coef',
patience=25,
mode='max')
# Fit model
eprint("[INFO][train_model] Model Fit...")
results = model.fit(
X,
y,
validation_split=0.2,
batch_size=batch_size,
epochs=epochs,
callbacks=[checkpointer, csv_logger, reduce_lr, earlystopping],
verbose=1,
shuffle=True) #, sample_weight=weights_train)
eprint("[INFO][train_model] Model Fit Done!")
# Write model history to the file
pd.DataFrame(results.history).to_csv(dir_write + "history_" +
model_string + ".csv")
return model, results
def work(baseline_dir, updated_dir, prefix):
baseline_dir = os.path.join("./tmp", baseline_dir)
updated_dir = os.path.join("./tmp", updated_dir)
images = sorted([
image for image in os.listdir(baseline_dir) if image.endswith('.png')
])
scores_dict = {}
for i, image in enumerate(images):
mask_matches = []
baseline_image_path = os.path.join(baseline_dir, image)
updated_image_path = os.path.join(updated_dir, image)
baseline_image = load_image_helper(baseline_image_path)
updated_image = load_image_helper(updated_image_path)
eprint('[LOG] Making prediction for baseline - {}, image - {}'.format(
prefix, image))
baseline_image_mask = predict(baseline_image_path)
eprint('[LOG] Saving masks for baseline - {}'.format(prefix))
save_masks(baseline_dir, image, baseline_image_mask, baseline_image)
eprint('[LOG] Making prediction for updated - {}, image - {}'.format(
prefix, image))
updated_image_mask = predict(updated_image_path)
eprint('[LOG] Saving masks for updated - {}'.format(prefix))
save_masks(updated_dir, image, updated_image_mask, updated_image)
eprint('[LOG] Finished predictions')
if baseline_image.shape != updated_image.shape:
eprint('[LOG] Images have different shapes. Using DP algo')
for c in range(0, 5):
mask_matches.append(
match_images(baseline_image_mask[:, :, c],
updated_image_mask[:, :, c], STEP))
eprint('[LOG] Calculating mask divergence score for {}, image - {}'.
format(prefix, image))
mask_divergence_scores = Scores.diff_mask_divergence(
baseline_image_mask // 255, updated_image_mask // 255,
mask_matches)
eprint(
'[LOG] Calculating pixelwise divergence score for {}, image - {}'.
format(prefix, image))
pixelwise_divergence_scores = Scores.diff_pixelwise_divergence(
baseline_image, updated_image, baseline_image_mask // 255,
updated_image_mask // 255, mask_matches)
baseline_js_log_file = os.path.join(
baseline_dir,
image.split('.')[0] + "_js_log.json")
updated_js_log_file = os.path.join(
updated_dir,
image.split('.')[0] + "_js_log.json")
baseline_network_log_file = os.path.join(
baseline_dir,
image.split('.')[0] + "_network_log.json")
updated_network_log_file = os.path.join(
updated_dir,
image.split('.')[0] + "_network_log.json")
log_processor = LogProcessor(baseline_js_log_file, updated_js_log_file,
baseline_network_log_file,
updated_network_log_file)
result = log_processor.run()
ui_risk_score = max(mask_divergence_scores['overall'],
pixelwise_divergence_scores['overall'])
scores_dict[i + 1] = {
'ui_stats': {
'mask_div': mask_divergence_scores,
'pixelwise_div': pixelwise_divergence_scores,
'risk_score': ui_risk_score
},
'js_stats': result['javascript'],
'network_stats': result['network'],
'risk_score': result['risk_score']
}
with open(os.path.join('./tmp', prefix + '_scores.json'), 'w') as f:
json.dump(scores_dict, f, indent=2)
eprint('[LOG] Saved scores dictionary for {}'.format(prefix))
def work(baseline_url, updated_url, max_depth, max_urls, prefix,
auth_baseline_username, auth_baseline_password, auth_updated_username,
auth_updated_password):
baseline_url = add_auth(url=baseline_url,
username=auth_baseline_username,
password=auth_baseline_password)
updated_url = add_auth(url=updated_url,
username=auth_updated_username,
password=auth_updated_password)
crawled_baseline = get_recursive_urls(baseline_url, max_depth,
max_urls)[:max_urls]
crawled_upgraded = get_recursive_urls(updated_url, max_depth,
max_urls)[:max_urls]
baseline_domain = get_domain(baseline_url)
updated_domain = get_domain(updated_url)
crawled_baseline_paths = [get_path(path) for path in crawled_baseline]
crawled_updated_paths = [get_path(path) for path in crawled_upgraded]
all_paths = list(set(crawled_baseline_paths) | set(crawled_updated_paths))
ss_report = {}
for i, path in enumerate(all_paths):
eprint('[LOG] Taking screenshots for {} - {}'.format(prefix, path))
collect_data(baseline_domain + path, prefix + '_baseline',
'{}.png'.format(i + 1))
collect_data(updated_domain + path, prefix + '_updated',
'{}.png'.format(i + 1))
ss_report[i + 1] = {
'baseline': baseline_domain + path,
'updated': updated_domain + path,
'endpoint': path,
'baseline_assets': 'tmp/' + prefix + "_baseline/",
'updated_assets': 'tmp/' + prefix + "_updated/"
}
eprint('[LOG] Finished taking screenshots for {}'.format(prefix))
with open(os.path.join('./tmp', prefix + '_ss_report.json'), 'w') as f:
json.dump(ss_report, f, indent=2)
p = Popen([
'python3', 'worker_predict.py', '--baseline-dir', prefix + '_baseline',
'--updated-dir', prefix + '_updated', '--prefix', prefix
])
if p.poll() is not None and p.poll() > 0:
eprint('[ERR] Failed to launch inference process')
exit(3)
eprint('[LOG] Waiting for {}'.format(prefix))
p.wait()
if p.poll() != 0:
eprint('[ERR] Prediction script failed for {}'.format(prefix))
exit(p.poll())
eprint('[LOG] Finished prediction for {}'.format(prefix))
ui_risk_scores = []
network_risk_scores = []
js_stats_total = []
net_stats_total = []
pixelwise_div_total = []
mask_div_total = []
with open(os.path.join('./tmp', prefix + '_report.json'), 'w') as f:
scores_report = json.load(
open(os.path.join('./tmp', prefix + '_scores.json')))
screenshots_report = json.load(
open(os.path.join('./tmp', prefix + '_ss_report.json')))
page_report = {}
for i in range(1, len(all_paths) + 1):
page_report[i] = scores_report[str(i)]
js_stats_total.append(scores_report[str(i)]["js_stats"])
net_stats_total.append(scores_report[str(i)]["network_stats"])
page_report[i]['links'] = screenshots_report[str(i)]
ui_risk_scores.append(page_report[i]["ui_stats"]["risk_score"])
network_risk_scores.append(page_report[i]["risk_score"])
pixelwise_div_total.append(
page_report[i]['ui_stats']['pixelwise_div'])
mask_div_total.append(page_report[i]['ui_stats']['mask_div'])
page_report['risk_score'] = max(max(ui_risk_scores),
max(network_risk_scores))
page_report['js_stats'] = dsum(js_stats_total)
page_report['ui_stats'] = {
'pixelwise_div_mean': dsum(pixelwise_div_total, True),
'mask_div_mean': dsum(mask_div_total, True),
'pixelwise_div_std': dstd(pixelwise_div_total),
'mask_div_std': dstd(mask_div_total)
}
page_report['network_stats'] = dsum(net_stats_total)
json.dump(page_report, f, indent=4)
eprint('[LOG] Saved {} report to {}'.format(prefix,
prefix + '_report.json'))
os.remove(os.path.join('./tmp', prefix + '_scores.json'))
os.remove(os.path.join('./tmp', prefix + '_ss_report.json'))
exit(0)
