def vision_client():
"""Create vision client."""
return vision.ImageAnnotatorClient()
def extract_words(img, height, width, ocr_engine='pytesseract'):
if ocr_engine == 'pytesseract':
data = pytesseract.image_to_data(img, output_type=Output.DICT)
n_boxes = len(data['text'])
words = [
{
'text': data['text'][i],
'left': data['left'][i],
'top': data['top'][i],
'right': data['left'][i] + data['width'][i],
'bottom': data['top'][i] + data['height'][i]
}
for i in range(n_boxes) if data['text'][i]
]
return words
elif ocr_engine=='google_ocr':
img_byte_arr = io.BytesIO()
img.save(img_byte_arr, format='PNG')
img_byte_arr = img_byte_arr.getvalue()
client = vision.ImageAnnotatorClient()
content=img_byte_arr
image_ = vision.Image(content=content)
response = client.text_detection(image=image_)
texts = response.text_annotations
words=[]
first=True
for text in texts:
if first:
first=False
continue
data={}
data['text']=text.description
x_vert=[]
y_vert=[]
for vertex in text.bounding_poly.vertices:
x_vert.append(vertex.x)
y_vert.append(vertex.y)
data['left']=min(x_vert)
data['right']=max(x_vert)
data['top']=min(y_vert)
data['bottom']=max(y_vert)
words.append(data)
return words
elif ocr_engine == 'aws_textract':
import boto3
# use aws textract
client = boto3.client('textract')
# convert PpmImageFile to byte
img_byte_arr = io.BytesIO()
img.save(img_byte_arr, format='PNG')
img_byte_arr = img_byte_arr.getvalue()
# call aws-textract API
response = client.detect_document_text(Document={'Bytes': img_byte_arr})
# get image weight and height to convert normalized coordinate from response
words = [
{
'text': data['Text'],
'left': math.floor((data['Geometry']['BoundingBox']['Left']) * width),
'top': math.floor((data['Geometry']['BoundingBox']['Top']) * height),
'right': math.ceil(
(data['Geometry']['BoundingBox']['Left'] + data['Geometry']['BoundingBox']['Width']) * width),
'bottom': math.ceil(
(data['Geometry']['BoundingBox']['Top'] + data['Geometry']['BoundingBox']['Height']) * height)
} for data in response['Blocks'] if "Text" in data
]
return words
def lambda_handler(event, context):
client = vision.ImageAnnotatorClient()
print(client)
def p2a_ocr_pdf(bucket, pdf_blob):
"""
https://cloud.google.com/vision/docs/pdf
"""
# define the input config
gcs_source_uri = "gs://{}/{}".format(bucket.name, pdf_blob.name)
gcs_source = vision.GcsSource(uri=gcs_source_uri)
# Supported mime_types are: 'application/pdf' and 'image/tiff'
mime_type = 'application/pdf'
# How many pages should be grouped into each json output file.
batch_size = 2
client = vision.ImageAnnotatorClient()
feature = vision.Feature(type_=vision.Feature.Type.DOCUMENT_TEXT_DETECTION)
gcs_source = vision.GcsSource(uri=gcs_source_uri)
input_config = vision.InputConfig(gcs_source=gcs_source,
mime_type=mime_type)
# define output config
pdf_id = pdf_blob.name.replace(".pdf",
"")[:4] # use the first 4 chars as pdf_id
gcs_destination_uri = "gs://{}/{}".format(bucket.name, pdf_id + "_")
gcs_destination = vision.GcsDestination(uri=gcs_destination_uri)
output_config = vision.OutputConfig(gcs_destination=gcs_destination,
batch_size=batch_size)
async_request = vision.AsyncAnnotateFileRequest(
features=[feature],
input_config=input_config,
output_config=output_config)
operation = client.async_batch_annotate_files(requests=[async_request])
print('Waiting for the operation to finish.')
operation.result(timeout=420)
# Once the request has completed and the output has been
# written to GCS, we can list all the output files.
# storage_client = storage.Client()
# match = re.match(r'gs://([^/]+)/(.+)', gcs_destination_uri)
# bucket_name = match.group(1)
# prefix = match.group(2)
# bucket = storage_client.get_bucket(bucket_name)
# # List objects with the given prefix.
# blob_list = list(bucket.list_blobs(prefix=prefix))
# print('Output files:')
# for blob in blob_list:
# print(blob.name)
# # Process the first output file from GCS.
# # Since we specified batch_size=2, the first response contains
# # the first two pages of the input file.
# output = blob_list[0]
# json_string = output.download_as_string()
# response = json.loads(json_string)
# # The actual response for the first page of the input file.
# first_page_response = response['responses'][0]
# annotation = first_page_response['fullTextAnnotation']
# # Here we print the full text from the first page.
# # The response contains more information:
# # annotation/pages/blocks/paragraphs/words/symbols
# # including confidence scores and bounding boxes
# print('Full text:\n')
# print(annotation['text'])
# convert PDF to PNG files for annotation
if ANNOTATION_MODE:
convert_pdf2png(bucket, pdf_blob)
def __init__(self):
self.img = "mirror.jpg"
self.client = vision.ImageAnnotatorClient()
def crop_flooded_objects_boundary(google_api_key, file_list):
credentials = service_account.Credentials.from_service_account_file(
google_api_key)
object_dict = {}
cropped_images = []
# creating a tenth and cycle counter to output progress of function
tenth_counter = 0
cycle_counter = 1
#looping though each image in the list submitted to the function
for file_item in file_list:
#need to have google vision credentials saved to credentials
client = vision.ImageAnnotatorClient(credentials=credentials)
# path to the images that need to be cropped
with open(file_item, 'rb') as image_file:
content = image_file.read()
image = vision.types.Image(content=content)
#same path just using OpenCV to get image shape and will use to save the cropped images later
im_cv2 = cv2.imread(file_item)
height, width, color = im_cv2.shape
#Using Google vision to actually find objects in the image
objects = client.object_localization(
image=image).localized_object_annotations
tenth_counter += 1
file_crops = []
#looping through each of the objects Google vision found in the image
for object_ in objects:
# ignoring all objects that don't have to do with the cars in the image
if object_.name in objects_to_crop_around:
vertex_dict = {}
#need to make sure the normalized vertex are multipled by the corresponding image distance so the vertex are in pixels counts
for index, vertex in enumerate(
object_.bounding_poly.normalized_vertices):
vertex_dict[f'vertex_{index}'] = [
int(width * vertex.x),
int(height * vertex.y)
]
object_dict[object_.name] = vertex_dict
# Cropping the image around the vertices of the object
# https://www.life2coding.com/cropping-polygon-or-non-rectangular-region-from-image-using-opencv-python/
# https://stackoverflow.com/questions/48301186/cropping-concave-polygon-from-image-using-opencv-python
mask = np.zeros(im_cv2.shape[:2], np.uint8)
points = np.array([
object_dict[object_.name]['vertex_0'],
object_dict[object_.name]['vertex_1'],
object_dict[object_.name]['vertex_2'],
object_dict[object_.name]['vertex_3']
])
#creating the bounding rectangle from the object vertices
rect = cv2.boundingRect(points)
x, y, w, h = rect
# cropping the image using OpenCV and the dimentions of the bounding rectangle
cropped = im_cv2[y:y + h, x:x + w].copy()
file_crops.append(
Image.fromarray(cv2.cvtColor(cropped, cv2.COLOR_BGR2RGB)))
cropped_images.append(file_crops)
return cropped_images
def hello_world(request):
from google.cloud import vision
from datetime import datetime
import re
import itertools
import write2bq
#from google.oauth2 import service_account
import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"]="C:\gcp_credentials\elaborate-howl-285701-105c2e8355a8.json"
#SCOPES = ['https://www.googleapis.com/auth/sqlservice.admin']
#SERVICE_ACCOUNT_FILE = 'C:\gcp_credentials\elaborate-howl-285701-105c2e8355a8.json'
#credentials = service_account.Credentials.from_service_account_file(
# SERVICE_ACCOUNT_FILE, scopes=SCOPES)
table_id='elaborate-howl-285701.context.image_web_entities'#destination table name
now = str(datetime.now())# time
print("now="+now)
client = vision.ImageAnnotatorClient()
request_json = request.get_json()
image = vision.Image()
if request_json:
source_url = request_json['source_url']
print("source_url="+source_url)
source_url=re.match(r'gs://([^/]+)/(.+)', source_url)
bucket_name=source_url.group(1) #credential bucket name
print(bucket_name)
prefix=source_url.group(2)# credential prefix name
print(prefix)
file_name=prefix
exact_file_name_list = re.split("/", file_name)
exact_file_name=exact_file_name_list[-1]
uri="gs://"+bucket_name+"/"+file_name
print("uri="+uri)
image.source.image_uri = uri
response = client.web_detection(image=image)
matching_images_lst=[]
matching_images=response.web_detection.full_matching_images# url string in it creates problem from json
for matching_image in matching_images:
matching_images_lst.append(matching_image.url)
# list is made for matching images
page_lst=[]
for page in response.web_detection.pages_with_matching_images:
page_lst.append(page.url)
# list is made for pages
best_match_lst=[]#list empty which stores best match result
for best_match in response.web_detection.best_guess_labels:
best_match_lst.append(best_match.label)
for (a, b, c) in itertools.zip_longest(matching_images_lst, page_lst, best_match_lst):
documentEntities={"time_stamp":now,"file_name":exact_file_name,"matching_images":a,"pages_with_images":b,"best_guess":c,"input_uri":uri}
write2bq.BQ(documentEntities,table_id)
return "success"
import io
import os
# Imports the Google Cloud client library
from google.cloud import vision
print "a"
# Instantiates a client (Change the line below******)
vision_client = vision.ImageAnnotatorClient(credentials='My Project-6748fa243896.json')
print "b"
# The name of the image file to annotate (Change the line below 'image_path.jpg' ******)
file_name = os.path.join(os.path.dirname(__file__), 'locol1.png') # Your image path from current directory
print "c"
# Loads the image into memory
with io.open(file_name, 'rb') as image_file:
print "d"
content = image_file.read()
print content
image = vision_client.
annotate_image(content=content)
print "e"
# Performs label detection on the image file
labels = image.detect_labels()
print "f"
print('Labels:')
for label in labels:
print(label.description)
print("g")
print("i")
def imagecheck():
ans = ""
ans1 = "You are dressed perfectly for the temperature"
warmth = 0
wet = 0
api_result = requests.get('http://api.weatherstack.com/current', params)
api_response = api_result.json()
temp = api_response['current']['temperature']
precip_rain = api_response['current']['precip']
precip_snow = 0
wind = api_response['current']['wind_speed']
weatherCode = api_response['current']['weather_code']
# less than 113 is sunny
if (weatherCode <= 113):
weather = "sunny"
# between 113 and 143 is cloudy
elif (weatherCode <= 143):
weather = "cloudy"
# between 122 and 230 is snowing
elif (weatherCode < 230):
weather = "snow"
# between 230 and 326
elif (weatherCode < 326):
weather = "rain"
# everything above 326 is snow again
else:
weather = "snow"
# set the precipitation to either snow or rain
# default set to rain, but if its snowing then swap
if (weather == "snow"):
precip_snow = precip_rain
precip_rain = 0
import csv
csv_columns = [
'Wind Speed', 'average temperature', 'precipitation', 'snow fall'
]
dict_data = [{
'Wind Speed': wind,
'average temperature': temp,
'precipitation': precip_rain,
'snow fall': precip_snow
}]
csv_file = "Names.csv"
try:
with open(csv_file, 'w') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=csv_columns)
writer.writeheader()
for data in dict_data:
writer.writerow(data)
except IOError:
print("I/O error")
data = pd.read_csv("Names.csv")
model = pickle.load(open('model.sav', 'rb'))
output = model.predict(data)
out = output.tolist()
wetp = int((out[0] - out[0] % 10) / 10)
warmp = (out[0] % 10)
if request.method == 'POST':
file = request.files['pic']
file.filename = "image.jpg"
file_name = file.filename
file.save(file_name)
with open('./image.jpg', 'rb') as image_file:
content = image_file.read()
client = vision.ImageAnnotatorClient()
response = client.label_detection({'content': content})
labels = response.label_annotations
print('Labels:')
for label in labels[0:8]:
print(label.description)
if (label.description == "Jacket" or label.description == "Coat"):
warmth = warmth + 7
if (label.description == "Sweater"
or label.description == "Hoodie"):
warmth = warmth + 5
if (label.description == "Hat"):
warmth += 1
wet = 1
if (label.description == "gloves"):
wet = 3
if (label.description == "Boots"):
wet = 4
warmth += 1
if (label.description == "Hood" or label.description == "Hoodie"):
wet = 1
if (label.description == "Umbrella"):
wet = 2
if (wetp > 2 and wet < 3):
ans = "also you might want to be careful of snow"
elif (wetp > 0 and wet < 1):
ans = "also you might want to grab an umbrella"
elif (wetp == 0 and wet > 0 and wet < 3):
ans = "also you probably won't need that umbrella"
if ((warmp - warmth) < -1):
ans1 = "You may be overdressed for the weather"
elif ((warmp - warmth) > 1):
ans1 = "You may be underdressed for the weather"
out = ans1 + " " + ans
return out
def upload_photo():
photo = request.files["file"]
# Create a Cloud Storage client.
storage_client = storage.Client()
# Get the bucket that the file will be uploaded to.
bucket = storage_client.get_bucket(CLOUD_STORAGE_BUCKET)
# Create a new blob and upload the file's content.
blob = bucket.blob(photo.filename)
blob.upload_from_string(photo.read(), content_type=photo.content_type)
# Make the blob publicly viewable.
blob.make_public()
# Create a Cloud Vision client.
vision_client = vision.ImageAnnotatorClient()
# Use the Cloud Vision client to detect a face for our image.
source_uri = "gs://{}/{}".format(CLOUD_STORAGE_BUCKET, blob.name)
image = vision.Image(source=vision.ImageSource(gcs_image_uri=source_uri))
faces = vision_client.face_detection(image=image).face_annotations
# If a face is detected, save to Datastore the likelihood that the face
# displays 'joy,' as determined by Google's Machine Learning algorithm.
if len(faces) > 0:
face = faces[0]
# Convert the likelihood string.
likelihoods = [
"Unknown",
"Very Unlikely",
"Unlikely",
"Possible",
"Likely",
"Very Likely",
]
face_joy = likelihoods[face.joy_likelihood]
else:
face_joy = "Unknown"
# Create a Cloud Datastore client.
datastore_client = datastore.Client()
# Fetch the current date / time.
current_datetime = datetime.now()
# The kind for the new entity.
kind = "Faces"
# The name/ID for the new entity.
name = blob.name
# Create the Cloud Datastore key for the new entity.
key = datastore_client.key(kind, name)
# Construct the new entity using the key. Set dictionary values for entity
# keys blob_name, storage_public_url, timestamp, and joy.
entity = datastore.Entity(key)
entity["blob_name"] = blob.name
entity["image_public_url"] = blob.public_url
entity["timestamp"] = current_datetime
entity["joy"] = face_joy
# Save the new entity to Datastore.
datastore_client.put(entity)
# Redirect to the home page.
return redirect("/")
def detect_web(path):
from google.cloud import vision
"""Detects web annotations given an image."""
assert os.path.exists(path) and os.path.isfile(path)
client = vision.ImageAnnotatorClient()
with io.open(path, 'rb') as image_file:
content = image_file.read()
image = vision.types.Image(content=content)
response = client.web_detection(image=image)
annotations = response.web_detection
if annotations.best_guess_labels:
for label in annotations.best_guess_labels:
print('\nBest guess label: {}'.format(label.label))
if annotations.pages_with_matching_images:
print('\n{} Pages with matching images found:'.format(
len(annotations.pages_with_matching_images)))
# for page in annotations.pages_with_matching_images:
# print('\n\tPage url : {}'.format(page.url))
# if page.full_matching_images:
# print('\t{} Full Matches found: '.format(
# len(page.full_matching_images)))
# for image in page.full_matching_images:
# print('\t\tImage url : {}'.format(image.url))
# if page.partial_matching_images:
# print('\t{} Partial Matches found: '.format(
# len(page.partial_matching_images)))
# for image in page.partial_matching_images:
# print('\t\tImage url : {}'.format(image.url))
# if annotations.web_entities:
# print('\n{} Web entities found: '.format(
# len(annotations.web_entities)))
# for entity in annotations.web_entities:
# print('\n\tScore : {}'.format(entity.score))
# print(u'\tDescription: {}'.format(entity.description))
# if annotations.visually_similar_images:
# print('\n{} visually similar images found:\n'.format(
# len(annotations.visually_similar_images)))
# for image in annotations.visually_similar_images:
# print('\tImage url : {}'.format(image.url))
if response.error.message:
raise Exception('{}\nFor more info on error messages, check: '
'https://cloud.google.com/apis/design/errors'.format(
response.error.message))
return annotations
def call_api():
# authorize twitter, initialize tweepy
screen_name = "NBA"
lable_output = "label.json"
Image_Number = 4
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_key, access_secret)
api = tweepy.API(auth)
# Get the tweets from a user up to 200
tweets = api.user_timeline(screen_name=screen_name,
count=200,
include_rts=False,
exclude_replies=True)
last_id = tweets[-1].id
while (True):
more_tweets = api.user_timeline(screen_name=screen_name,
count=200,
include_rts=False,
exclude_replies=True,
max_id=last_id - 1)
# If there are no more tweets
if (len(more_tweets) == 0):
break
else:
last_id = more_tweets[-1].id - 1
tweets = tweets + more_tweets
# Obtain the full path for the images
media_files = set()
for status in tweets:
media = status.entities.get('media', [])
if (len(media) > 0):
media_files.add(media[0]['media_url'])
count = 0
for media_file in media_files:
#print(media_file)
wget.download(media_file, 'image' + str(count) + media_file[-4:])
count += 1
if count == Image_Number:
break
# Instantiates a client, specify the cert file
os.environ[
"GOOGLE_APPLICATION_CREDENTIALS"] = "/usr/local/lib/googlecloudsdk/EC500hw1-70f8d0b192a0.json"
client = vision.ImageAnnotatorClient()
data = {}
data['Pictures'] = []
data['Account'] = screen_name
pictures = [pic for pic in os.listdir(".") if pic.endswith('jpg')]
picNum = 0
order = 0
for i in pictures:
file = os.path.join(os.path.dirname(__file__), i)
new = str(picNum) + '.jpg'
os.renames(file, new)
Subject = {}
picNum = picNum + 1
# Loads the image into memory
with io.open(new, 'rb') as image_file:
content = image_file.read()
image = types.Image(content=content)
response = client.label_detection(image=image)
labels = response.label_annotations
label_list = []
for label in labels:
label_list.append(label.description)
Subject[str(order)] = label_list
data['Pictures'].append(Subject)
order = order + 1
#Create json file
with open(lable_output, 'w') as JSONObject:
json.dump(data, JSONObject, indent=4, sort_keys=True)
#Initialize MongoDB
client = MongoClient()
db = client.picture.database
collection = db.picture_collection
db.posts.insert(data)
pprint.pprint(db.posts.find_one({'Account': screen_name}))
os.system(
"cat *.jpg | ffmpeg -f image2pipe -framerate .5 -i - -vf 'crop=in_w-1:in_h' -vcodec libx264 video.mp4"
)
def async_detect_document(gcs_source_uri, gcs_destination_uri):
"""OCR with PDF/TIFF as source files on GCS"""
import re
from google.cloud import vision
from google.cloud import storage
from google.protobuf import json_format
# Supported mime_types are: 'application/pdf' and 'image/tiff'
mime_type = 'application/pdf'
# How many pages should be grouped into each json output file.
batch_size = 2
client = vision.ImageAnnotatorClient()
feature = vision.types.Feature(
type=vision.enums.Feature.Type.DOCUMENT_TEXT_DETECTION)
gcs_source = vision.types.GcsSource(uri=gcs_source_uri)
input_config = vision.types.InputConfig(gcs_source=gcs_source,
mime_type=mime_type)
gcs_destination = vision.types.GcsDestination(uri=gcs_destination_uri)
output_config = vision.types.OutputConfig(gcs_destination=gcs_destination,
batch_size=batch_size)
async_request = vision.types.AsyncAnnotateFileRequest(
features=[feature],
input_config=input_config,
output_config=output_config)
operation = client.async_batch_annotate_files(requests=[async_request])
print('Waiting for the operation to finish.')
operation.result(timeout=180)
# Once the request has completed and the output has been
# written to GCS, we can list all the output files.
storage_client = storage.Client()
match = re.match(r'gs://([^/]+)/(.+)', gcs_destination_uri)
bucket_name = match.group(1)
prefix = match.group(2)
bucket = storage_client.get_bucket(bucket_name)
# List objects with the given prefix.
blob_list = list(bucket.list_blobs(prefix=prefix))
print('Output files:')
for blob in blob_list:
print(blob.name)
# Process the first output file from GCS.
# Since we specified batch_size=2, the first response contains
# the first two pages of the input file.
output = blob_list[0]
json_string = output.download_as_string()
response = json_format.Parse(json_string,
vision.types.AnnotateFileResponse())
# The actual response for the first page of the input file.
first_page_response = response.responses[0]
annotation = first_page_response.full_text_annotation
# Here we print the full text from the first page.
# The response contains more information:
# annotation/pages/blocks/paragraphs/words/symbols
# including confidence scores and bounding boxes
print(u'Full text:\n{}'.format(annotation.text))
# Imports the Google Cloud client library
from google.cloud import vision
from google.cloud.vision import types
from utilities.digicon_classes import coordinate, boundingBox, image_location
class FeatureType(Enum):
PAGE = 1
BLOCK = 2
PARA = 3
WORD = 4
SYMBOL = 5
# Instantiates a client
CLIENT = vision.ImageAnnotatorClient()
# google_sample.pkl stores the returned types.AnnotateImageResponse object for frontend/src/samples/1.jpg
# Saves us unnecessary calls to the API while testing
# with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'google_sample.pkl'), 'rb') as input:
# SAMPLE_RESPONSE = pickle.load(input)
def parse_google_ocr(ocr_response):
# Collect specified feature bounds by enumerating all document features
sentence_bounds = []
word_bounds = []
all_text = ''
temp_coordinate = coordinate(0, 0)
for page in ocr_response.pages:
for block in page.blocks:
for paragraph in block.paragraphs:
paragraph_bounds = []
def get_all_tweets(screen_name):
#Twitter only allows access to a users most recent 3240 tweets with this method
#authorize twitter, initialize tweepy
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_key, access_secret)
api = tweepy.API(auth)
#initialize a list to hold all the tweepy Tweets
alltweets = []
#make initial request for most recent tweets (200 is the maximum allowed count)
new_tweets = api.user_timeline(screen_name = screen_name,count=10)
#save most recent tweets
alltweets.extend(new_tweets)
#save the id of the oldest tweet less one
oldest = alltweets[-1].id - 1
#keep grabbing tweets until there are no tweets left to grab
while len(new_tweets) > 0:
#all subsiquent requests use the max_id param to prevent duplicates
new_tweets = api.user_timeline(screen_name = screen_name,count=10,max_id=oldest)
#save most recent tweets
alltweets.extend(new_tweets)
#update the id of the oldest tweet less one
oldest = alltweets[-1].id - 1
if(len(alltweets) > 15):
break
print ("...%s tweets downloaded so far" % (len(alltweets)))
data = {}
data['info'] = []
data['info'].append({
'name': screen_name
})
media_files = set()
for status in alltweets :
try :
media = status.extended_entities.get('media', [])
except :
media = status.entities.get('mdeia',[])
# print (media[0])
if(len(media) > 0):
for i in range(len(media)):
media_files.add(media[i]['media_url'])
# mm = 1
for media_file in media_files:
# data['info'].append({
# 'picture ' + str(m): media_file
# })
# mm = mm + 1
print(media_file)
wget.download(media_file)
os.system("ffmpeg -framerate 1 -pattern_type glob -i '*.jpg' -c:v libx264 -r 30 -pix_fmt yuv420p out1.mp4")
os.system("ffmpeg -framerate 1 -pattern_type glob -i '*.png' -c:v libx264 -r 30 -pix_fmt yuv420p out2.mp4")
# for google vision
client = vision.ImageAnnotatorClient()
file = open("label.txt","w")
point = 0
numlist = '0123456789'
OBJ = [pic for pic in listdir(".") if pic.endswith('jpg') or pic.endswith('png')]
# print(OBJ)
for i in OBJ:
file_name = os.path.join(os.path.dirname(__file__),i)
ttt = 1
new_name = numlist[point] +'.jpg'
data['info'].append({
'picture ' + str(ttt): i
})
os.renames(file_name,new_name)
print(file_name)
print("changed down")
point = point + 1
# Loads the image into memory
with io.open(new_name, 'rb') as image_file:
content = image_file.read()
image = types.Image(content=content)
# Performs label detection on the image file
response = client.label_detection(image=image)
labels = response.label_annotations
file.write('Lables for '+new_name+' :\n')
label_list = []
for label in labels:
label_list.append(label.description)
file.write(label.description+'\n')
data['info'].append({
'description ' + str(ttt):label_list
})
ttt += 1
file.close()
print(data)
client = MongoClient()
db = client.picture.database
collection = db.picture_collection
posts = db.posts
posts.insert_one(data)
def get_vision_instance():
client = vision.ImageAnnotatorClient()
return client
def ProcessMaerskInvoice(ImageList):
keywordlist = [
'no.:', 'maersk', 'from:', 'to:', 'description:', 'quantity',
'itinerary', "size", 'sub.', 'collapsible', 'gross', 'equip', 'pack.',
'weight', 'volume', 'qty/kind', 'type', 'release', 'vessel', 'voy',
'etd', 'eta'
]
############ Preprocess Image ###########
for image in ImageList:
currentImage = cv2.imread(image)
currentImage[currentImage < 10] = 0
currentImage[(currentImage != 0) & (currentImage != 255)] = 255
cv2.imwrite(image, currentImage)
################ Invoke Vision API for 2nd page ############################
try:
for count, image in enumerate(ImageList):
if count < 2:
currentfile = ImageList[count]
with io.open(currentfile, 'rb') as gen_image_file:
content = gen_image_file.read()
client = vision.ImageAnnotatorClient()
#image = vision.types.Image(content=content)
image = vision.Image(content=content)
response = client.text_detection(image=image)
DictResponse = MessageToDict(response._pb)
if count == 0:
FirstPageDictResponse = DictResponse
else:
SecondPageDictResponse = DictResponse
except:
return "invocation error"
############# Create Message To Dict For 2nd Page ###############
SecondPageDictResponse = MessageToDict(response._pb)
############# Check for Keywords ##################
WholeContentDescription = FirstPageDictResponse['textAnnotations'][0][
'description'].lower() + " " + SecondPageDictResponse[
'textAnnotations'][0]['description'].lower()
match = 0
for keyword in keywordlist:
if keyword in WholeContentDescription:
match = match + 1
else:
print(keyword)
if match != len(keywordlist):
return "missing keywords"
############# create Dataframes #########################
WordsAndCoordinatesPage1 = FirstPageDictResponse['textAnnotations'][1:]
WordsAndCoordinatesPage2 = SecondPageDictResponse['textAnnotations'][1:]
WordsAndCoordinates = [WordsAndCoordinatesPage1, WordsAndCoordinatesPage2]
for num in range(0, len(WordsAndCoordinates)):
currentWordandCoordinate = WordsAndCoordinates[num]
word_list = []
llx_list = []
lly_list = []
lrx_list = []
lry_list = []
urx_list = []
ury_list = []
ulx_list = []
uly_list = []
for i in range(0, len(currentWordandCoordinate)):
word_list.append(currentWordandCoordinate[i]['description'])
llx_list.append(currentWordandCoordinate[i]['boundingPoly']
['vertices'][0]['x'])
lly_list.append(currentWordandCoordinate[i]['boundingPoly']
['vertices'][0]['y'])
lrx_list.append(currentWordandCoordinate[i]['boundingPoly']
['vertices'][1]['x'])
lry_list.append(currentWordandCoordinate[i]['boundingPoly']
['vertices'][1]['y'])
urx_list.append(currentWordandCoordinate[i]['boundingPoly']
['vertices'][2]['x'])
ury_list.append(currentWordandCoordinate[i]['boundingPoly']
['vertices'][2]['y'])
ulx_list.append(currentWordandCoordinate[i]['boundingPoly']
['vertices'][3]['x'])
uly_list.append(currentWordandCoordinate[i]['boundingPoly']
['vertices'][3]['y'])
##################### Create Dictionary for the lists #####################
WordsAndCoordinatesDict = {
"Word": word_list,
'llx': llx_list,
'lly': lly_list,
'lrx': lrx_list,
'lry': lry_list,
'urx': urx_list,
'ury': ury_list,
'ulx': ulx_list,
'uly': uly_list
}
####################### Create Dataframe ######################
if num == 0:
WordsAndCoordinatesDF_Page1 = pd.DataFrame.from_dict(
WordsAndCoordinatesDict)
elif num == 1:
WordsAndCoordinatesDF_Page2 = pd.DataFrame.from_dict(
WordsAndCoordinatesDict)
###################### Get Values ###########################
try:
############## Booking Number ############################
BookingNumber_uly = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['No.:'
])]['uly'].values[0] - 20
BookingNumber_lly = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['No.:'
])]['lly'].values[0] + 20
BookingNumber_urx = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['No.:'
])]['urx'].values[0]
MeraskSpot_llx = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(
['Maersk'])].sort_values(by='lly').head(1)['llx'].values[0]
BookingNumber = WordsAndCoordinatesDF_Page1[
(WordsAndCoordinatesDF_Page1['uly'] > BookingNumber_uly)
& (WordsAndCoordinatesDF_Page1['lly'] < BookingNumber_lly) &
(WordsAndCoordinatesDF_Page1['ulx'] > BookingNumber_urx) &
(WordsAndCoordinatesDF_Page1['urx'] < MeraskSpot_llx)]
BookingNumber = " ".join(BookingNumber['Word'].values).strip()
print(BookingNumber)
############## From #############################
From_uly = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['From:'
])]['uly'].values[0] - 30
From_lly = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['From:'
])]['lly'].values[0] + 30
From_urx = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['From:'
])]['urx'].values[0]
From = WordsAndCoordinatesDF_Page1[
(WordsAndCoordinatesDF_Page1['uly'] > From_uly)
& (WordsAndCoordinatesDF_Page1['lly'] < From_lly) &
(WordsAndCoordinatesDF_Page1['ulx'] > From_urx)]
From = " ".join(From['Word'].values).strip()
print(From)
################# To #############################
To_uly = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['To:'
])]['uly'].values[0] - 20
To_lly = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['To:'
])]['lly'].values[0] + 20
To_urx = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['To:'])]['urx'].values[0]
To = WordsAndCoordinatesDF_Page1[
(WordsAndCoordinatesDF_Page1['uly'] > To_uly)
& (WordsAndCoordinatesDF_Page1['lly'] < To_lly) &
(WordsAndCoordinatesDF_Page1['ulx'] > To_urx)]
To = " ".join(To['Word'].values).strip()
print(To)
############# Commodity Description ###################
Description_uly = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Description:'
])]['uly'].values[0] - 20
Description_lly = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Description:'
])]['lly'].values[0] + 20
Description_urx = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Description:'
])]['urx'].values[0]
Commodity = WordsAndCoordinatesDF_Page1[
(WordsAndCoordinatesDF_Page1['uly'] > Description_uly)
& (WordsAndCoordinatesDF_Page1['lly'] < Description_lly) &
(WordsAndCoordinatesDF_Page1['ulx'] > Description_urx)]
CommodityDescription = " ".join(Commodity['Word'].values).strip()
################ Quantity #########################
Quantity_LLY = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Quantity'
])]['lly'].values[0] + 20
Itinerary_ULY = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Itinerary'
])]['uly'].values[0] - 20
Size_LLX = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Size/Type/Height'
])]['llx'].values[0]
Quantity = WordsAndCoordinatesDF_Page1[
(WordsAndCoordinatesDF_Page1['lly'] > Quantity_LLY)
& (WordsAndCoordinatesDF_Page1['uly'] < Itinerary_ULY) &
(WordsAndCoordinatesDF_Page1['lrx'] < Size_LLX)]
Quantity = " ".join(Quantity['Word'].values).strip()
print(Quantity)
################ Size ##############################
Quantity_LLY = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Quantity'
])]['lly'].values[0] + 20
Itinerary_ULY = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Itinerary'
])]['uly'].values[0] - 20
Quatity_LRX = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Quantity'
])]['lrx'].values[0]
Sub_LLX = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Sub.'
])]['llx'].values[0]
Size = WordsAndCoordinatesDF_Page1[
(WordsAndCoordinatesDF_Page1['lly'] > Quantity_LLY)
& (WordsAndCoordinatesDF_Page1['uly'] < Itinerary_ULY) &
(WordsAndCoordinatesDF_Page1['llx'] > Quatity_LRX) &
(WordsAndCoordinatesDF_Page1['lrx'] < Sub_LLX)].sort_values(
by=['llx'])
Size = " ".join(Size['Word'].values).strip()
print(Size)
################ Sub Equipment #####################
Quantity_LLY = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Quantity'
])]['lly'].values[0] + 20
Itinerary_ULY = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Itinerary'
])]['uly'].values[0] - 20
Collapsible_LRX = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Collapsible'
])]['lrx'].values[0]
Gross_LLX = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Gross'
])]['llx'].values[0]
SubEquipment = WordsAndCoordinatesDF_Page1[
(WordsAndCoordinatesDF_Page1['lly'] > Quantity_LLY)
& (WordsAndCoordinatesDF_Page1['uly'] < Itinerary_ULY) &
(WordsAndCoordinatesDF_Page1['llx'] > Collapsible_LRX) &
(WordsAndCoordinatesDF_Page1['lrx'] < Gross_LLX)]
SubEquipment = " ".join(SubEquipment['Word'].values).strip()
############### Gross Weight #####################
Quantity_LLY = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Quantity'
])]['lly'].values[0] + 20
Itinerary_ULY = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Itinerary'
])]['uly'].values[0] - 20
Equip_LRX = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Equip'
])]['lrx'].values[0]
Pack_LLX = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Pack.'
])]['llx'].values[0]
GrossWeight = WordsAndCoordinatesDF_Page1[
(WordsAndCoordinatesDF_Page1['lly'] > Quantity_LLY)
& (WordsAndCoordinatesDF_Page1['uly'] < Itinerary_ULY) &
(WordsAndCoordinatesDF_Page1['llx'] > Equip_LRX) &
(WordsAndCoordinatesDF_Page1['lrx'] < Pack_LLX)]
GrossWeight = " ".join(GrossWeight['Word'].values).strip()
############### Pack Quantity ######################
Quantity_LLY = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Quantity'
])]['lly'].values[0] + 20
Itinerary_ULY = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Itinerary'
])]['uly'].values[0] - 20
Weight_LRX = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Weight'
])]['lrx'].values[0] + 40
Cargo_LLX = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Volume'
])]['llx'].values[0]
PackQuantity = WordsAndCoordinatesDF_Page1[
(WordsAndCoordinatesDF_Page1['lly'] > Quantity_LLY)
& (WordsAndCoordinatesDF_Page1['uly'] < Itinerary_ULY) &
(WordsAndCoordinatesDF_Page1['llx'] > Weight_LRX) &
(WordsAndCoordinatesDF_Page1['lrx'] < Cargo_LLX)]
PackQuantity = " ".join(PackQuantity['Word'].values).strip()
############## Cargo Volume ##########################
Quantity_LLY = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Quantity'
])]['lly'].values[0] + 20
Itinerary_ULY = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Itinerary'
])]['uly'].values[0] - 20
Weight_LRX = WordsAndCoordinatesDF_Page1[
WordsAndCoordinatesDF_Page1['Word'].isin(['Qty/Kind'
])]['lrx'].values[0] + 20
CargoVolume = WordsAndCoordinatesDF_Page1[
(WordsAndCoordinatesDF_Page1['lly'] > Quantity_LLY)
& (WordsAndCoordinatesDF_Page1['uly'] < Itinerary_ULY) &
(WordsAndCoordinatesDF_Page1['llx'] > Weight_LRX)]
CargoVolume = " ".join(CargoVolume['Word'].values).strip()
######## Load Itinerary Type and Location ###############
Type_lly = WordsAndCoordinatesDF_Page2[
WordsAndCoordinatesDF_Page2['Word'].isin(['Туре'
])]['lly'].values[0]
MaxLowerLimit = Type_lly + 160
Type_urx = WordsAndCoordinatesDF_Page2[
WordsAndCoordinatesDF_Page2['Word'].isin(['Туре'
])]['urx'].values[0]
MaxURX = Type_urx + 160
LoadItineraryType = WordsAndCoordinatesDF_Page2[
(WordsAndCoordinatesDF_Page2['lly'] > Type_lly)
& (WordsAndCoordinatesDF_Page2['lly'] < MaxLowerLimit) &
(WordsAndCoordinatesDF_Page2['lrx'] < MaxURX)]
LoadItineraryType = " ".join(LoadItineraryType['Word'].values)
Location_lly = WordsAndCoordinatesDF_Page2[
WordsAndCoordinatesDF_Page2['Word'].isin(['Location'
])]['lly'].values[0]
MaxLowerLimit = Location_lly + 160
Type_urx = WordsAndCoordinatesDF_Page2[
WordsAndCoordinatesDF_Page2['Word'].isin(['Туре'
])]['urx'].values[0]
MaxURX = Type_urx + 160
Release_llx = WordsAndCoordinatesDF_Page2[
WordsAndCoordinatesDF_Page2['Word'].isin(['Release'
])]['ulx'].values[0]
LoadItineraryLocation = WordsAndCoordinatesDF_Page2[
(WordsAndCoordinatesDF_Page2['lly'] > Location_lly)
& (WordsAndCoordinatesDF_Page2['lly'] < MaxLowerLimit) &
(WordsAndCoordinatesDF_Page2['lrx'] < Release_llx) &
(WordsAndCoordinatesDF_Page2['llx'] > MaxURX)]
LoadItineraryLocation = " ".join(LoadItineraryLocation['Word'].values)
############### TransportPlanVessel ##########################
Vessel_lly = WordsAndCoordinatesDF_Page2[
WordsAndCoordinatesDF_Page2['Word'].isin(['Vessel'
])]['lly'].values[0] + 20
max_lly = Vessel_lly + 80
Vessel_llx = WordsAndCoordinatesDF_Page2[
WordsAndCoordinatesDF_Page2['Word'].isin(['Vessel'
])]['llx'].values[0]
Voy_llx = WordsAndCoordinatesDF_Page2[
WordsAndCoordinatesDF_Page2['Word'].isin(['Voy'])]['llx'].values[0]
TransportPlanVessel = WordsAndCoordinatesDF_Page2[
(WordsAndCoordinatesDF_Page2['lly'] > Vessel_lly)
& (WordsAndCoordinatesDF_Page2['lly'] < max_lly) &
(WordsAndCoordinatesDF_Page2['llx'] >= Vessel_llx) &
(WordsAndCoordinatesDF_Page2['lrx'] < Voy_llx)]
TransportPlanVessel = " ".join(TransportPlanVessel['Word'].values)
############ TransportVoyNumber #############################
Voy_lly = WordsAndCoordinatesDF_Page2[
WordsAndCoordinatesDF_Page2['Word'].isin(['Voy'
])]['lly'].values[0] + 20
max_lly = Voy_lly + 80
Voy_llx = WordsAndCoordinatesDF_Page2[
WordsAndCoordinatesDF_Page2['Word'].isin(['Voy'])]['llx'].values[0]
ETD_llx = WordsAndCoordinatesDF_Page2[
WordsAndCoordinatesDF_Page2['Word'].isin(['ETD'])]['llx'].values[0]
TransportVoyNumber = WordsAndCoordinatesDF_Page2[
(WordsAndCoordinatesDF_Page2['lly'] > Voy_lly)
& (WordsAndCoordinatesDF_Page2['lly'] < max_lly) &
(WordsAndCoordinatesDF_Page2['llx'] >= Voy_llx) &
(WordsAndCoordinatesDF_Page2['lrx'] < ETD_llx)]
TransportVoyNumber = " ".join(TransportVoyNumber['Word'].values)
############## TransportPlanETD ###############################
ETD_lly = WordsAndCoordinatesDF_Page2[
WordsAndCoordinatesDF_Page2['Word'].isin(['ETD'])]['lly'].values[0]
max_lly = Voy_lly + 80
ETD_llx = WordsAndCoordinatesDF_Page2[
WordsAndCoordinatesDF_Page2['Word'].isin(['ETD'
])]['llx'].values[0] - 20
ETA_llx = WordsAndCoordinatesDF_Page2[
WordsAndCoordinatesDF_Page2['Word'].isin(['ЕТА'])]['llx'].values[0]
TransportPlanETD = WordsAndCoordinatesDF_Page2[
(WordsAndCoordinatesDF_Page2['lly'] > ETD_lly)
& (WordsAndCoordinatesDF_Page2['lly'] < max_lly) &
(WordsAndCoordinatesDF_Page2['llx'] >= ETD_llx) &
(WordsAndCoordinatesDF_Page2['lrx'] < ETA_llx)]
TransportPlanETD = " ".join(TransportPlanETD['Word'].values)
################## TransportPlanETA #############################
ETA_lly = WordsAndCoordinatesDF_Page2[
WordsAndCoordinatesDF_Page2['Word'].isin(['ЕТА'])]['lly'].values[0]
max_lly = ETA_lly + 80
ETA_llx = WordsAndCoordinatesDF_Page2[
WordsAndCoordinatesDF_Page2['Word'].isin(['ЕТА'
])]['llx'].values[0] - 20
TransportPlanETA = WordsAndCoordinatesDF_Page2[
(WordsAndCoordinatesDF_Page2['lly'] > ETA_lly)
& (WordsAndCoordinatesDF_Page2['lly'] < max_lly) &
(WordsAndCoordinatesDF_Page2['llx'] >= ETA_llx)]
TransportPlanETA = " ".join(TransportPlanETA['Word'].values)
print(TransportPlanETA)
return dict(msg="Success",
BookingNumber=BookingNumber,
From=From,
To=To,
CommodityDescription=CommodityDescription,
Quantity=Quantity,
Size=Size,
SubEquipment=SubEquipment,
GrossWeight=GrossWeight,
PackQuantity=PackQuantity,
CargoVolume=CargoVolume,
LoadItineraryType=LoadItineraryType,
LoadItineraryLocation=LoadItineraryLocation,
TransportPlanVessel=TransportPlanVessel,
TransportVoyNumber=TransportVoyNumber,
TransportPlanETD=TransportPlanETD,
TransportPlanETA=TransportPlanETA)
except:
return "unable to extract data from Google Vision API."
import io
import os
import re
#*****************************************************
# Google Vision API
#*****************************************************
# Imports the Google Cloud client library
from google.cloud import vision
from google.cloud.vision import types
from google.oauth2 import service_account
credentials = service_account.Credentials.from_service_account_file('key.json')
client = vision.ImageAnnotatorClient(
credentials=credentials) # [END vision_python_migration_client]
for i in range(1, 10):
# The name of the image file to annotate (Change the line below 'image_path.jpg' ******)
file_name = os.path.join(os.path.dirname(__file__),
str(i) +
'.jpg') # Your image path from current directory
with io.open(file_name, 'rb') as image_file:
content = image_file.read()
image = vision.types.Image(content=content)
response = client.text_detection(image=image)
texts = response.text_annotations
def init():
# [END vision_python_migration_import]
# Instantiates a g_client
# [START vision_python_migration_client]
global g_client
g_client = vision.ImageAnnotatorClient()
def get_labels(Start_Pic_No):
# Setup to access to the Google Vision API
# os.system cannot upload the credential correctly, so FOR NOW it is necessary to run this in shell
client = vision.ImageAnnotatorClient()
i = 1
print('Getting labels from google and printing labels on it')
while (1):
# Check if there are pictures inside the folder
if os.path.exists('./PICS/' + str(i) + '.jpg') == True:
file_name = os.path.join(os.path.dirname(__file__),
'./PICS/' + str(i) + '.jpg')
# Read the pictures and get ready to push it to Google
with io.open(file_name, 'rb') as image_file:
content = image_file.read()
image = types.Image(content=content)
# Get the labels from Google Vision
try:
response = client.label_detection(image=image)
labels = response.label_annotations
except:
print(
'Google API is not accessable at this time, please check your creditional or try again later.'
)
return 0
# Connect to the database and create a cursor
cnx = mysql.connector.connect(user=DB_USER,
password=DB_PASSWORD,
database='Pics')
cursor = cnx.cursor()
# Setup the cursor
add_tags = ("INSERT IGNORE INTO tags "
"(tags_no, tags) "
"VALUES (%s, %s)")
add_pic_tags = ("INSERT IGNORE INTO pic_tags "
"(Pic_No, tags_no) "
"VALUES (%s, %s)")
# Setup PILLOW to put labels into the picture
# Input the direction of your fonts here
im = Image.open('./PICS/' + str(i) + '.jpg')
draw = ImageDraw.Draw(im)
myfont = ImageFont.truetype(fonts, size=35)
# As a result, the FONTs.ttf should be copied to the same folders
fillcolor = 'red'
# Put label into the picture
m = 0
for label in labels:
m = m + 1
# Put tags into database, and put the connections between pic and tags into database
cursor.execute("SELECT MAX(tags_no) FROM tags")
tags_start_id = cursor.fetchall()[0][0]
if tags_start_id == None:
tags_start_id = 0
data_tags = (tags_start_id + 1, label.description)
cursor.execute(add_tags, data_tags)
cnx.commit()
cursor.execute("SELECT tags_no FROM tags WHERE tags = '" +
label.description + "'")
tags_no = cursor.fetchall()[0][0]
data_pic_tags = (Start_Pic_No + i, tags_no)
cursor.execute(add_pic_tags, data_pic_tags)
cnx.commit()
if m <= 2:
# Only draw 3 tags into the picture
draw.text((40, 40 * m),
label.description,
font=myfont,
fill=fillcolor)
im.save('./PICS/' + str(i) + '.jpg', 'JPEG')
print('Printing labels on the ' + str(i) + 'th Picture')
i = i + 1
# Print the total number of the pictures
else:
print('***************************')
print(str(i - 1) + ' pictures completed')
print('***************************')
cursor.close()
cnx.close()
return 1
break
def _get_labels(uri):
client = vision.ImageAnnotatorClient()
image = vision.types.Image()
image.source.image_uri = uri
labels = client.label_detection(image=image).label_annotations
return {'labels': [l.description for l in labels]}
def detect_text(image_url):
image = build_image_url(image_url)
client = vision.ImageAnnotatorClient()
response = client.text_detection(image=image)
return response.text_annotations[0].description.split('\n')
def __init__(self):
self.storage_client = storage.Client()
self.vision_client = vision.ImageAnnotatorClient()
self.results = {}
def __init__(self):
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = os.path.join(
os.getcwd().strip('vision_api'), 'guestbook-93c84e7825ff.json')
self.client = vision.ImageAnnotatorClient()
self.called = False
def get_client_lazy(self, **kwargs):
from google.cloud import vision
return vision.ImageAnnotatorClient(credentials=self.get_credential())
def __init__(self, service_account_file, cache=True):
credentials = service_account.Credentials.from_service_account_file(
service_account_file)
self.client = vision.ImageAnnotatorClient(credentials=credentials)
self.cache = cache
ToggleButton:
text: 'Start / Stop'
on_press: camera.play = not camera.play
size_hint_y: None
height: '48dp'
Button:
text: 'Capture'
size_hint_y: None
height: '48dp'
on_press: root.capture()
''')
credentials = service_account.Credentials.from_service_account_file(
"credential-vision.json")
# Instantiates a client
client = vision.ImageAnnotatorClient(credentials=credentials)
def image_process(file_name):
with io.open(file_name, 'rb') as image_file:
content = image_file.read()
image = vision.types.Image(content=content)
response = client.text_detection(image=image)
texts = response.text_annotations
result = []
title = ""
max_area = 0
for text in texts:
s = text.description
def loadPdfText(fPath):
gcs_source_uri = "gs://revaise.appspot.com/" + fPath
print(gcs_source_uri)
#gcs_source_uri = "gs://revaise.appspot.com/images/picture-ScienceVideo Game Deep RL0.8575049874802985"
gcs_destination_uri = "gs://revaise.appspot.com/TextOutput/"
gcs_destination = vision.GcsSource(uri=gcs_destination_uri)
mime_type = "application/pdf"
# mime_type = "image/png"
# How many pages should be grouped into each json output file.
batch_size = 2
client = vision.ImageAnnotatorClient()
feature = vision.Feature(type_=vision.Feature.Type.DOCUMENT_TEXT_DETECTION)
print(gcs_source_uri)
gcs_source = vision.GcsSource(uri=gcs_source_uri)
input_config = vision.InputConfig(gcs_source=gcs_source,
mime_type=mime_type)
print(input_config)
gcs_destination = vision.GcsDestination(uri=gcs_destination_uri)
output_config = vision.OutputConfig(gcs_destination=gcs_destination,
batch_size=batch_size)
print(output_config)
import time
time.sleep(1)
async_request = vision.AsyncAnnotateFileRequest(
features=[feature],
input_config=input_config,
output_config=output_config)
operation = client.async_batch_annotate_files(requests=[async_request])
print('Waiting for the operation to finish.')
operation.result(timeout=420)
storage_client = storage.Client()
match = re.match(r'gs://([^/]+)/(.+)', gcs_destination_uri)
bucket_name = match.group(1)
prefix = match.group(2)
bucket = storage_client.get_bucket(bucket_name)
# List objects with the given prefix.
blob_list = list(bucket.list_blobs(prefix=prefix))
print('Output files:')
for blob in blob_list:
print(blob.name)
txt = ""
for output in blob_list:
json_string = output.download_as_string()
if json_string != b'':
response = json.loads(json_string)
# The actual response for the first page of the input file.
for j in response['responses']:
txt += j['fullTextAnnotation']['text'] + " "
with open("park.txt", "w+") as f:
f.write(txt)
for blob in blob_list:
blob.delete()
print(txt)
return txt
-- setting 에서 panads 모듈 추가
import os
import io
from google.cloud import vision
import pandas as pd
os.environ["GOOGLE_APPLICATION_CREDENTIALS"]=r'vision2020-290100-c6b16014803d.json'
client = vision.ImageAnnotatorClient()
file_name = 'setagaya_small.jpeg'
image_path = f'./resources/{file_name}'
with io.open(image_path, 'rb') as image_file:
content = image_file.read()
# construct an iamge instance
#image = vision.types.Image(content=content) #pip install google-cloud-vision == 1.0.0
image = vision.Image(content=content)
response = client.label_detection(image=image)
labels = response.label_annotations
df = pd.DataFrame(columns=['description', 'score', 'topicality'])
for label in labels:
df = df.append(
dict(
description=label.description,
score=label.score,
topicality=label.topicality
from sys import stdin, stdout
from json import loads, dumps
from decouple import config
from datauri import DataURI
from google.cloud import vision
from google.cloud.vision import types
from google.oauth2 import service_account
import os
import urllib.request
api_key = config("GOOGLE_APPLICATION_CREDENTIALS")
api_key = loads(api_key)
api_key = service_account.Credentials.from_service_account_info(api_key)
client = vision.ImageAnnotatorClient(credentials=api_key)
def transcribe(uri):
# Parses a URI and gets the encoded data string out
data = DataURI(uri).data
image = types.Image(content=data)
response = client.document_text_detection(image)
if response.text_annotations:
return response.text_annotations[0].description.replace('\n', ' ')
else:
return "No Text"
# To be replaced with a function that gets the metadata out of the function
def nothing(transcript):
return {"nada": "nothing"}
