def _get_prediction(img_path, threshold):
"""
Gets the prediction of an image given a threshold.
:param img_path: Image path.
:param threshold: Threshold.
:return: Predicted masks, boxes and classes.
"""
with Timer('Transform image'):
with Image.open(img_path).convert('RGB') as img:
img = image_utils.resize_aspect(img)
img.save(img_path, format='JPEG', quality=95)
transform = torch_transform.Compose([torch_transform.ToTensor()])
img_transformed = transform(img)
with Timer('Predict'):
prediction = model([img_transformed])
with Timer('Get results'):
prediction_score = list(prediction[0]['scores'].detach().numpy())
prediction_t = [prediction_score.index(x) for x in prediction_score if x > threshold][-1]
masks = (prediction[0]['masks'] > 0.5).squeeze().detach().cpu().numpy()
prediction_class = [COCO_INSTANCE_CATEGORY_NAMES[i] for i in list(prediction[0]['labels'].numpy())]
prediction_boxes = [[(i[0], i[1]), (i[2], i[3])] for i in list(prediction[0]['boxes'].detach().numpy())]
masks = masks[:prediction_t+1]
prediction_boxes = prediction_boxes[:prediction_t+1]
prediction_class = prediction_class[:prediction_t+1]
return masks, prediction_boxes, prediction_class
def instance_segmentation_api(image_path, object_list):
"""
Cut every object of a list from an image.
:param image_path: Image path.
:param object_list: List of objects to cut.
:return: Output path of the generated image.
"""
masks, boxes, prediction_cls = _get_prediction(image_path, MODEL_THRESHOLD)
with Timer('Apply mask'):
img = cv2.imread(image_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
if masks.any():
rgb_mask_list = np.zeros(masks[0].shape)
for i in range(len(masks)):
if prediction_cls[i] in object_list:
rgb_mask_list += masks[i]
rgb_mask = _random_colour_masks(rgb_mask_list)
img = cv2.addWeighted(img, 1, rgb_mask, 1, 0)
with Timer('Generate image result'):
image_path_output = f'{".".join(image_path.split(".")[:-1])}_output.png'
plt.figure(figsize=(img.shape[0] * PIX_TO_INCH, img.shape[1] * PIX_TO_INCH))
plt.axis('off')
plt.imshow(img)
plt.savefig(image_path_output, bbox_inches='tight', dpi=175)
del img
return image_path_output
def select_option_from_drop_down(self, option, drop_down):
self.try_and_click(drop_down)
Timer.sleep()
drop_down_element = self.driver.find_element_by_xpath(
'//div[@data-automation-label="{0}"]/../../..'.format(option))
self.try_and_click(drop_down_element)
Timer.sleep()
def fill(self):
base = super(MyInformationPage, self)
self.driver.find_element_by_xpath(
'//*[@id="textInput.nameComponent--uid12-input"]').send_keys(
self.user.first_name)
Timer.sleep()
self.driver.find_element_by_xpath(
'//*[@id="textInput.nameComponent--uid13-input"]').send_keys(
self.user.last_name)
Timer.sleep()
self.driver.find_element_by_xpath(
'//*[@id="textInput.phone--uid19-input"]').send_keys(
self.user.phone_number)
self.driver.find_element_by_xpath(
'//*[@id="dropDownSelectList.sources-input--uid20-input"]/div[1]/div'
).click()
Timer.sleep()
drop_down_element = self.driver.find_element_by_xpath(
'//div[@id="dropDownSelectList.sources-input-entry-3"]')
base.try_and_click(drop_down_element)
Timer.sleep()
self.driver.find_element_by_xpath(
'//button[@data-automation-id="wd-CommandButton_next"]').click()
Timer.sleep()
base.click_next_button()
def fill(self):
base = super(SelfIdentityPage, self)
check_boxes = self.driver.find_elements_by_xpath('//div[@data-automation-id="checkboxPanel"]')
answer = InputOutput.input_yes_no('\n\nDo you wish to answer a question on your disability(/ies), if any?')
if not answer:
base.try_and_click(check_boxes[2])
else:
InputOutput.output("How do I know if I have a disability?\nYou are considered to have a disability if "
"you have a physical or mental impairment or medical condition that substantially "
"limits a major life activity, or if you have a history or record of such an "
"impairment or medical condition.\nDisabilities include, but are not limited to: \n- "
"Blindness\n- Deafness\n- Cancer\n- Diabetes\n- Epilepsy\n- Autism\n- Cerebral "
"palsy\n- HIV/AIDS\n- Schizophrenia\n- Muscular dystrophy\n- Bipolar disorder\n- "
"Major depression\n- Multiple sclerosis (MS)\n- Missing limbs or partially missing "
"limbs\n- Post-traumatic stress disorder (PTSD)\n- Obsessive compulsive disorder\n- "
"Impairments requiring the use of a wheelchair\n- Intellectual disability ("
"previously called mental retardation)")
disabilities_answer = InputOutput.input_yes_no('Do you have or have had any of the above disabilities?')
if disabilities_answer:
base.try_and_click(check_boxes[0])
else:
base.try_and_click(check_boxes[1])
self.driver.find_element_by_xpath('(//div[@data-automation-id="textInput"])[6]/input').send_keys(self.full_name)
Timer.sleep()
base.try_and_click(self.driver.find_element_by_xpath('//span[@data-automation-id="dateSectionMonth"]'))
ActionChains(self.driver).send_keys(time.strftime("%m%d%Y")).perform()
base.click_next_button()
def login():
try:
with Timer('Get Auth URL'):
auth_url = spotify_api.get_auth_url()
return response.make(error=False, response=dict(redirect=auth_url))
except Exception as e:
log.error(
f'Exception while processing {login.__name__} function: [{e}]')
log.exception(e)
return response.make(error=True, message=MESSAGE_ERROR)
def playlist(code, github_user):
try:
with Timer('Request token retrieving'):
access_token = spotify_api.get_access_token(code)
if not access_token:
return response.make(error=True,
message=MESSAGE_TOKEN_NOT_FOUND)
with Timer('Get profile data'):
user_id = spotify_api.get_current_user_id(access_token)
if not user_id:
return response.make(error=True,
message=MESSAGE_SPOTIFY_NOT_FOUND)
with Timer('Playlist generation'):
playlist_id, playlist_url = spotify_api.post_playlist(
access_token, user_id, github_user)
if not playlist_id and not playlist_url:
return response.make(error=True,
message=MESSAGE_SPOTIFY_PLAYLIST_ERROR)
with Timer('Retrieve commits from user'):
commit_messages = github_api.get_commit_messages(github_user)
if not commit_messages:
return response.make(error=True,
message=MESSAGE_COMMIT_NOT_FOUND)
with Timer('Retrieve most common words'):
most_common_words = nltk.extract_most_common(commit_messages)
with Timer('Search for tracks'):
with ThreadPool(CONCURRENT_POOL) as pool:
thread_args = [(access_token, word)
for word in most_common_words]
track_uri_list = list(
pool.imap(spotify_api.search_for_tracks, thread_args))
track_uri_list = [t for t in track_uri_list if t]
with Timer('Add tracks to the playlist'):
success = spotify_api.add_tracks_to_playlist(
access_token, playlist_id, track_uri_list)
if not success:
return response.make(error=True,
message=MESSAGE_SPOTIFY_TRACK_ERROR)
return response.make(error=False, response=dict(url=playlist_url))
except Exception as e:
log.error(
f'Exception while processing {playlist.__name__} function: [{e}]')
log.exception(e)
return response.make(error=True, message=MESSAGE_ERROR)
def __wait_till_other_filter_drop_downs_refresh():
Timer.sleep(2)
def run():
with Timer('Accessing to Log in page'):
browser = helper.init_browser()
def post():
"""
Main function for /cut endpoint.
:return: JSON response.
"""
try:
body = request.json
with Timer('Validate input data'):
required_parameters = ['objects']
if not all(x in body for x in required_parameters):
return jsonify(
make_response(
True,
message=
f'{required_parameters} body parameters are required.')
), 400
if not all(o in COCO_INSTANCE_CATEGORY_NAMES
for o in body['objects']):
return jsonify(
make_response(
True,
message=
'Some objects from the list will not be detected.')
), 400
if bool('image_url' in body) == bool('image_base64' in body):
return jsonify(
make_response(
True,
message=
'image_url (x)or image_base64 has to be specified')
), 400
if 'return_white_bg' not in body:
body['return_white_bg'] = False
with Timer('Download image'):
if 'image_url' in body:
image_path = image_utils.download(body['image_url'])
elif 'image_base64' in body:
image_path = image_utils.decode(body['image_base64'])
else:
image_path = None
if not image_path:
return jsonify(
make_response(True, message='Wrong image specified.')), 400
with Timer('Generate image'):
output_image_path = instance_segmentation_api(
image_path, body['objects'])
with Timer('Removing white color'):
if not body['return_white_bg']:
image_utils.remove_white(output_image_path)
with Timer('Encoding image'):
encoded_string = image_utils.encode(output_image_path)
with Timer('Remove input and output images'):
if os.path.exists(image_path):
os.remove(image_path)
if os.path.exists(output_image_path):
os.remove(output_image_path)
return jsonify(make_response(False, image_base64=encoded_string)), 200
except Exception as e:
return jsonify(make_response(True,
message=f'Unexpected error: {e}')), 400
finally:
gc.collect()
job_index = int(
InputOutput.input('Enter Sl.No of Job to apply for : '))
if job_index in range(0, len(job_results)):
email_address = InputOutput.input('Email address : ')
first_name = InputOutput.input('First Name : ')
last_name = InputOutput.input('Last Name : ')
phone_number = InputOutput.get_valid_phone_number()
gender = InputOutput.input('Gender',
['Male', 'Female', 'Undeclared'])
user = User(first_name, last_name, phone_number, email_address,
InputOutput.get_valid_password(), gender)
answers = Answers(False, False, False, False, False, ["India"],
True, True)
job_application = JobApplication(driver, job_results[job_index],
user, build_pages())
job_application.apply()
InputOutput.output('\n\n' + Color.UNDERLINE + Color.BOLD +
'Job Application complete! Thank you for using '
'our service.' + Color.END + Color.END)
Timer.sleep(5)
else:
raise Exception('Invalid job chosen')
except Exception as e:
InputOutput.output(e)
InputOutput.output('No Jobs found')
finally:
driver.close()
def get(github_user):
try:
with Timer('Initialize response dictionary'):
resp = {}
with Timer('Basic information'):
basic_information = github_api.get_basic_user_information(
github_user)
if not basic_information:
return response.make(error=True,
message=MESSAGE_USER_NOT_FOUND)
resp['username'] = github_user
resp['photo'] = response.get('avatar_url', basic_information)
resp['public_repos'] = response.get('public_repos',
basic_information)
resp['public_gists'] = response.get('public_gists',
basic_information)
resp['followers'] = response.get('followers', basic_information)
resp['following'] = response.get('following', basic_information)
with Timer('Repositories'):
repos_list = github_api.get_repos_from_user(github_user)
if not repos_list:
return response.make(error=True,
message=MESSAGE_USER_NOT_FOUND)
resp['repo_amount'] = len(repos_list)
resp['repo_fork_amount'] = sum(
[response.get('fork', d, default=False) for d in repos_list])
resp['repo_total_size'] = sum(
[response.get('size', d, default=0)
for d in repos_list]) / 1000
resp['repo_total_stars'] = sum([
response.get('stargazers_count', d, default=0)
for d in repos_list
])
resp['repo_total_forks'] = sum([
response.get('forks_count', d, default=0) for d in repos_list
])
resp['repo_total_open_issues'] = sum([
response.get('open_issues', d, default=0) for d in repos_list
])
resp['repo_avg_size'] = formatter.to_float(
resp['repo_total_size'] / resp['repo_amount'])
resp['repo_avg_stars'] = formatter.to_float(
resp['repo_total_stars'] / resp['repo_amount'])
resp['repo_avg_forks'] = formatter.to_float(
resp['repo_total_forks'] / resp['repo_amount'])
resp['repo_avg_open_issues'] = formatter.to_float(
resp['repo_total_open_issues'] / resp['repo_amount'])
with Timer('Languages & topics - threads'):
with ThreadPool(CONCURRENT_POOL) as pool:
thread_args = [(github_user, response.get('name', r))
for r in repos_list if response.get('name', r)]
language_response_list = list(
pool.imap(github_api.get_languages, thread_args))
topic_response_list = list(
pool.imap(github_api.get_topics, thread_args))
with Timer('Languages & topics - amount'):
languages_dict = {}
for language_response in language_response_list:
if language_response:
for key, value in language_response.items():
if key not in languages_dict:
languages_dict[key] = 0
languages_dict[key] += value
topics_dict = {}
for topic_response in topic_response_list:
if topic_response:
for topic in topic_response:
if topic not in topics_dict:
topics_dict[topic] = 0
topics_dict[topic] += 1
with Timer('Languages - percentage'):
resp['languages'] = []
total_languages = sum(languages_dict.values())
sorted_languages = sorted(languages_dict.items(),
key=operator.itemgetter(1),
reverse=True)
for idx in range(0, len(sorted_languages)):
if idx < GITHUB_LANGUAGES_MAX:
language_name, language_amount = sorted_languages[idx]
percentage = formatter.to_float(
(language_amount / total_languages) * 100)
resp['languages'].append(
dict(label=language_name,
amount=language_amount,
percentage=percentage))
else:
language_amount = sum([
v[1] for v in sorted_languages[GITHUB_LANGUAGES_MAX:]
])
percentage = formatter.to_float(
(language_amount / total_languages) * 100)
resp['languages'].append(
dict(label='Others',
amount=language_amount,
percentage=percentage))
break
with Timer('Topics - percentage'):
resp['topics'] = []
total_topics = sum(topics_dict.values())
sorted_topics = sorted(topics_dict.items(),
key=operator.itemgetter(1),
reverse=True)
for idx in range(0, len(sorted_topics)):
if idx < GITHUB_TOPICS_MAX:
topic_name, topic_amount = sorted_topics[idx]
percentage = formatter.to_float(
(topic_amount / total_topics) * 100)
resp['topics'].append(
dict(label=topic_name,
amount=topic_amount,
percentage=percentage))
else:
topic_amount = sum(
[v[1] for v in sorted_topics[GITHUB_TOPICS_MAX:]])
percentage = formatter.to_float(
(topic_amount / total_topics) * 100)
resp['topics'].append(
dict(label='others',
amount=topic_amount,
percentage=percentage))
break
with Timer('Contributors - thread'):
with ThreadPool(CONCURRENT_POOL) as pool:
thread_args = [(github_user, response.get('name', r))
for r in repos_list if response.get('name', r)]
contributor_response_list = list(
pool.imap(github_api.get_contributors, thread_args))
with Timer('Contributors - save'):
resp['commits'] = 0
resp['commits_user'] = 0
resp['commits_contributor'] = 0
contributors_dict = {}
for contributor_response in contributor_response_list:
if contributor_response:
for contributor in contributor_response:
contributor_name = response.get('login', contributor)
if contributor_name:
contributions = response.get('contributions',
contributor,
default=0)
resp['commits'] += contributions
if contributor_name.lower() == github_user.lower():
resp['commits_user'] += contributions
else:
if contributor_name not in contributors_dict:
contributors_dict[contributor_name] = dict(
commits=0,
photo=response.get('avatar_url',
contributor,
default=''),
url=response.get('html_url',
contributor,
default=''))
resp['commits_contributor'] += contributions
contributors_dict[contributor_name][
'commits'] += contributions
resp['commits_user_percentage'] = formatter.to_float(
(resp['commits_user'] / resp['commits']) * 100)
resp['commits_contributor_percentage'] = formatter.to_float(
(resp['commits_contributor'] / resp['commits']) * 100)
resp['contributors'] = []
for contributor_name, contributor_dict in contributors_dict.items(
):
contributor_dict['label'] = contributor_name
resp['contributors'].append(contributor_dict)
resp['contributors'] = sorted(resp['contributors'],
key=lambda k: k['commits'],
reverse=True)
return response.make(error=False, response=resp)
except Exception as e:
log.error(f'Exception while processing {get.__name__} function: [{e}]')
log.exception(e)
return response.make(error=True, message=MESSAGE_ERROR)
def auto_factorial_cron_job():
with Timer(auto_factorial_cron_job.__name__):
runner.run()