def sendToStorage(image):
convert(image)
storage_client = storage.Client()
print "get bucket"
bucket = storage_client.get_bucket('enter-project-id-here')
print "set blob"
blob = bucket.blob('fejs')
print "upload blob"
blob.upload_from_filename('face.jpg')
print "after upload"
sendNotification('fejs')
def image_post_request():
x = image.convert(request.json['image'])
y = model.predict(x.reshape((1, 28, 28, 1))).reshape((10, ))
n = int(np.argmax(y, axis=0))
y = [float(i) for i in y]
return jsonify({'result': y, 'digit': n})
def process(ch, method, properties, body):
data = json.loads(body)
image = convert(data['path'], IMAGE_SIZE)
list_predictions = []
if image:
image = np.array(image)
list_predictions = []
for network in image_networks:
list_predictions.append(network.classify(image.copy()))
payload = {
'id': data['id'],
'path': data['path'],
'predictions': determine_predictions(list_predictions),
'reject': False
}
if len(payload['predictions']) == 0:
payload['reject'] = True
message = json.dumps(payload)
channel.basic_publish(
exchange='',
routing_key='return_queue',
body=message,
properties=pika.BasicProperties(
delivery_mode=2, # make message persistent
))
ch.basic_ack(delivery_tag=method.delivery_tag)
def _scan(frame, window, model, roi):
lt, rb = roi.bbox(window)
#cv2.rectangle(frame, lt, rb, color=(255,255,255), thickness=4)
subimage = scale_roi(frame, window, lt, rb)
cvt = convert(subimage)
position, features = zip(*fast_sliding_window(cvt, window //
(2 * SIZE) + 1))
prediction = model.predict(features)
return ((*restore_position(xy, window, lt, rb), window)
for xy in np.array(position)[np.where(prediction == 1)])
def image_convert(files=None, dest_typ=None):
if not dest_typ:
dest_typ = best_dest_type()
ext_ = ext(dest_typ)
image_fmt = dest_typ.split('/', 1)[1]
LOG.debug('image_convert dest=%s files=%s', dest_typ, repr(files)[:100])
for stream, mimetype, name in request_files(files):
if 'application/pdf' == mimetype:
# n = len(pdf.get_pages(stream))
for i, part in enumerate(pdf.split_pdf(stream)):
try:
yield (image.convert(part, image_fmt, 'application/pdf'),
dest_typ, name + ('-page_%02d' % (i + 1)) + ext_)
except Exception, exc:
LOG.error("error converting %s (%s) to pdf: %s",
repr(part)[:100], image_fmt, exc)
else:
try:
yield (image.convert(stream, image_fmt, mimetype),
dest_typ, name + ext_)
except Exception, exc:
LOG.error("error converting %s (%s) to pdf: %s",
repr(stream)[:100], image_fmt, exc)
def to_jpeg(self):
new_basename_root, old_ext = \
os.path.splitext(os.path.basename(self.filename))
new_basename = new_basename_root + '.jpg'
new_filename = tempfile.gettempdir() + os.path.sep + new_basename
image = Image.open(self.filename)
if image.mode != 'RGB':
image = image.convert('RGB')
image.save(new_filename, 'JPEG', quality=100)
new_file = JPEGFile(new_filename)
new_file.item = self.item
new_file.index = self.index
return new_file
def to_jpeg(self):
new_basename_root, old_ext = \
os.path.splitext(os.path.basename(self.filename))
new_basename = new_basename_root + '.jpg'
new_filename = tempfile.gettempdir() + os.path.sep + new_basename
image = Image.open(self.filename)
if image.mode != 'RGB':
image = image.convert('RGB')
image.save(new_filename, 'JPEG', quality=100)
new_file = JPEGFile(new_filename)
new_file.item = self.item
new_file.index = self.index
return new_file
async def convert_local(data: ConvertImage):
"""
Converts incoming base64 images of any compatible formats to the specified output format. If no format specified, JPEG is assumed. See here for compatible incoming and outgoing formats: https://tinyurl.com/yymmmpwk
"""
b64_image = data.get('base64_image')
if get_b64_size(b64_image) > 20971520:
raise HTTPException(status_code=413, detail="Content is too large.")
image_format = data.get('image_format')
content = BytesIO(base64.b64decode(b64_image))
content.seek(0)
image_buffer = convert(content, image_format)
content.close()
return Response(image_buffer.getvalue(), status_code=200)
async def convert_remote(url: str = 'https://s.gravatar.com/avatar/434d67e1ebc4109956d035077ef5adb8', image_format: str = 'JPEG'):
"""
Converts image at specified URL to specified format. JPEG is assumed if no format specified. Allowed formats: https://tinyurl.com/yymmmpwk
"""
response = requests.get(url)
if response.status_code != 200:
raise HTTPException(status_code=response.status_code,
detail=f'Server returned error {response.status_code}.')
if len(response.content) > 20971520:
raise HTTPException(status_code=413, detail="Content is too large.")
content = BytesIO(response.content)
content.seek(0)
image_buffer = convert(content, image_format)
content.close()
return Response(image_buffer.getvalue(), status_code=200)
def average_hash(image, hash_size=9):
"""
Average Hash computation
Implementation follows http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html
Step by step explanation: https://www.safaribooksonline.com/blog/2013/11/26/image-hashing-with-python/
@image must be a PIL instance.
"""
if hash_size < 0:
raise ValueError("Hash size must be positive")
# reduce size and complexity, then covert to grayscale
image = image.convert("L").resize((hash_size, hash_size), Image.ANTIALIAS)
# find average pixel value; 'pixels' is an array of the pixel values, ranging from 0 (black) to 255 (white)
pixels = numpy.asarray(image)
avg = pixels.mean()
# create string of bits
diff = pixels > avg
# make a hash
return ImageHash(diff)
def test_bmp(self):
i = 'images/samoyed.bmp'
image = convert(i, self.IMAGE_SIZE)
image.show()
self.verify_correct(image)
def formatImage(image):
if image.mode == 'RGB':
return image
return image.convert('RGB')
def image_post_request():
x = image.convert(request.json['image'])
y, n = classifier.classify(x)
return jsonify({'result': y, 'digit': n})
def image2pdf_gm(stream, inp_fmt=None):
return image.convert(stream, 'pdf', inp_fmt=inp_fmt)
import engine
import image
import battle
# Image of a bag
bag = image.convert('images/bag.txt')
# Selected item in inventory
selected = 0
# Combination of food and armour and weapons (used for drawing)
loot_together = []
# Draw the inventory menu to the screen
def draw_inventory():
global loot_together
global selected
global bag
battle.compute_max_health()
# Draw the inventory menu
engine.draw_text_box(0,
0,
20,
22,
fill=True,
text=' I N V E N T O R Y \n===================')
engine.draw_buf(bag.data, (14, 22))
test = pytesseract.image_to_string(crop)
uc_txt.append(test)
for l in range(len(lista)):
if lista[l] in test:
path = os.path.join("./directoies/", lista[l])
if (os.path.isdir(path)):
print("{} Already exists".format(lista[l]))
else:
path = os.path.join("./directoies/", lista[l])
os.mkdir(path, mode=755)
print("path of {} is {}".format(lista[l], path))
print("{} found".format(lista[l]))
image = Image.open('./images3/{}.jpg'.format(i))
img = image.convert('RGB')
img.save("{}/{}.jpg".format(path, i))
##########Appending the respective pages to the list #############
if (lista[l] == "Closing Cost Worksheet"):
CCW.append(i)
dic["Closing Cost Worksheet"] = CCW
c += 1
break
elif (lista[l] == "LOAN ESTIMATE"):
LE.append(i)
dic["LOAN ESTIMATE"] = LE
c += 1
break
elif (lista[l] == "Uniform Residential Loan Application"):
URLA.append(i)
# Player's HP
player_health = constants.START_MAX_HEALTH
player_max_health = constants.START_MAX_HEALTH
# Food and equipment
player_food = Counter()
player_equip = set()
# Items the player is currently wearing
current_head = ('nothing', 'hat', 0)
current_body = ('nothing', 'body', 0)
current_legs = ('nothing', 'legs', 0)
current_weapon = ('nothing', 'weapon', 1)
# Crab image (O B A M A I S G O N E !!!)
crab = image.convert('images/crab.txt')
# Draw healthbar
def draw_bar(frac, size, pos):
for i in range(size):
engine.plot("▰▱"[floor(i / size - frac + 1)], (pos[0], pos[1] + i))
# Battle drawing and stuff
def battle():
global cursor_pos
# Draw the menu
engine.draw_text_box(
0,
from keras import backend as K
from pprint import pprint
from nets import ImageNetNetwork
from determiner import determine_predictions
from image import convert
from utils.profiler import timer_avg
# K.set_session(K.tf.Session(config=K.tf.ConfigProto(intra_op_parallelism_threads=1,
# inter_op_parallelism_threads=1)))
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
IMAGE_SIZE = 224
MODE = os.getenv('MODE', 'local2')
preload_image = convert('samoyed.jpg', IMAGE_SIZE)
preload_image = np.array(preload_image)
if MODE == 'production':
print('=== Production')
NETWORKS = [('seresnet50', IMAGE_SIZE, preload_image),
('densenet169', IMAGE_SIZE, preload_image),
('resnet34', IMAGE_SIZE, preload_image)]
HOST = 'rabbitmq-server'
elif MODE == 'testing':
print('=== Testing')
NETWORKS = [('mobilenet', IMAGE_SIZE, preload_image)]
HOST = 'rabbitmq-server'
elif MODE == 'local1':
print('=== Local1')
NETWORKS = [('mobilenet', IMAGE_SIZE, preload_image)]
def uploaded():
global assign
global fixed
global cd
global CAN
cd.clear()
CAN.clear()
W2.clear()
URLNF.clear()
DOT.clear()
folder = './static/fnma'
i = 0
dele = []
while i < len(fixed):
dele = str(fixed[i]) + ".jpg"
i = i + 1
for filename in os.listdir(folder):
print(filename)
file_path = os.path.join(folder, filename)
try:
if (os.path.isfile(file_path)
or os.path.islink(file_path)) and filename not in dele:
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
print('Failed to delete %s. Reason: %s' % (file_path, e))
data = []
doc = []
fnma.clear()
paatalo.clear()
target = os.path.join('./uploads')
##print(target)
if not os.path.isdir(target):
os.mkdir(target)
for file in request.files.getlist("file"):
##print(file)
filename = file.filename
destination = "/".join([target, filename])
##print("Accept incoming file:", filename)
##print(destination)
file.save(destination) #i made change here
if ".pdf" in filename:
import pytesseract
from PIL import Image
x = './uploads/{}'.format(filename)
pages = convert_from_path(x)
print("--- %s seconds converted ---" % (time.time() - start_time))
for i, im in enumerate(pages):
im.save("./static/images/{}.jpg".format(i + 1))
print("--- %s seconds After Naming images ---" %
(time.time() - start_time))
print("images done")
pdf = PdfFileReader(request.files['file'])
print(pdf.getNumPages())
counter = 0
i = 0
global pp
global c
global uc
CCW.clear()
LE.clear()
GFE.clear()
URLA.clear()
F3021.clear()
MF.clear()
CD.clear()
APP.clear()
a.clear()
unc.clear()
dic.clear()
uc_txt.clear()
pp.clear()
dic.clear()
unc.clear()
for i in range(pdf.getNumPages()):
i = i + 1
print(i)
result = ''
image = cv2.imread('./static/images/{}.jpg'.format(i))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
crop = gray[1950:2180, 10:1700].copy()
test = pytesseract.image_to_string(crop)
uc_txt.append(test)
for l in range(len(lista)):
if lista[l] in test:
path = os.path.join("./directoies/", lista[l])
if (os.path.isdir(path)):
print("{} Already exists".format(lista[l]))
else:
path = os.path.join("./directoies/", lista[l])
os.mkdir(path, mode=755)
print("path of {} is {}".format(lista[l], path))
print("{} found".format(lista[l]))
image = Image.open('./static/images/{}.jpg'.format(i))
img = image.convert('RGB')
img.save("{}/{}.jpg".format(path, i))
##########Appending the respective pages to the list #############
if (lista[l] == "Closing Cost Worksheet"):
CCW.append(i)
dic["Closing Cost Worksheet"] = CCW
c += 1
break
elif (lista[l] == "LOAN ESTIMATE"):
LE.append(i)
dic["LOAN ESTIMATE"] = LE
c += 1
break
elif (lista[l] ==
"Uniform Residential Loan Application"):
URLA.append(i)
dic["Uniform Residential Loan Application"] = URLA
c += 1
break
elif (lista[l] == "Form 3021"):
F3021.append(i)
dic["Form 3021"] = F3021
c += 1
break
elif (lista[l] == "MULTISTATE FIXED"):
MF.append(i)
dic["MULTISTATE FIXED"] = MF
c += 1
break
elif (lista[l] == "CLOSING DISCLOSURE"):
CD.append(i)
dic["CLOSING DISCLOSURE"] = CD
c += 1
break
elif (lista[l] == "appraisal software by a la mode"):
APP.append(i)
dic["appraisal software by a la mode"] = APP
c += 1
break
else:
path = os.path.join("./directoies/", lista[l])
if (os.path.isdir(path)):
print("{} Already exists".format(lista[l]))
else:
path = os.path.join("./directoies/", lista[l])
os.mkdir(path, mode=755)
print("path of {} is {}".format(lista[l], path))
print("{} found".format(lista[l]))
image = Image.open('./static/images/{}.jpg'.format(i))
img = image.convert('RGB')
img.save("{}/{}.jpg".format(path, i))
if (("Closing Cost Worksheet" and "LOAN ESTIMATE"
and "Uniform Residential Loan Application"
and "Form 3021" and "MULTISTATE FIXED"
and "CLOSING DISCLOSURE"
and "appraisal software by a la mode") not in uc_txt):
uc += 1
unc.append(i)
dic["unclassified"] = unc
#######Assembling the images and converting to pdf ###########
for nam, pag in dic.items():
pp = list(compress_ranges(pag))
counter = 0
for x in pp:
counter = counter + 1
pre_path = os.path.join("./directoies/", nam)
prefix = ("{}/".format(pre_path))
suffix = ".jpg"
out_fname = "./static/pdf/{0} {1}.pdf".format(nam, counter)
for z in range(x[0], x[1] + 1):
a.append(z)
process_images(prefix, suffix, out_fname, a)
a.clear()
data.append(str(x[0]) + ".jpg")
doc.append("pdf/{0} {1}.pdf".format(nam, counter))
print("count of classified is {}".format(c))
print("count uclassfied is {}".format(uc))
print("data {}".format(data))
return render_template('index.html', data=data, doc=doc)
0,
79,
4,
text=" ran away successfully!")
constants.game_state['mode'] = 'main'
# Bag state
elif constants.game_state['mode'] == 'bag':
inventory.draw_inventory()
# Draw the screen to the terminal
engine.draw()
# Images!
title = image.convert('images/title.txt')
battlescreen = image.convert('images/battlescreen.txt')
levelup = image.convert('images/levelup.txt')
player_x = 0
player_y = 0
if __name__ == '__main__':
try:
run(b' ')
while (1):
run(getch.getch())
except KeyboardInterrupt:
sys.exit()
def test_gif(self):
i = 'images/reject.gif'
image = convert(i, self.IMAGE_SIZE)
self.assertIsNone(image)
def test_broken(self):
i = 'images/broken.jpg'
image = convert(i, self.IMAGE_SIZE)
self.assertIsNone(image)
def formatImage(image):
if image.mode == 'RGB':
return image
return image.convert('RGB')
