def test_async_batch_annotate_files(self):
# Setup Expected Response
expected_response = {}
expected_response = image_annotator_pb2.AsyncBatchAnnotateFilesResponse(
**expected_response)
operation = operations_pb2.Operation(
name="operations/test_async_batch_annotate_files", done=True)
operation.response.Pack(expected_response)
# Mock the API response
channel = ChannelStub(responses=[operation])
patch = mock.patch("google.api_core.grpc_helpers.create_channel")
with patch as create_channel:
create_channel.return_value = channel
client = vision_v1p4beta1.ImageAnnotatorClient()
# Setup Request
requests = []
response = client.async_batch_annotate_files(requests)
result = response.result()
assert expected_response == result
assert len(channel.requests) == 1
expected_request = image_annotator_pb2.AsyncBatchAnnotateFilesRequest(
requests=requests)
actual_request = channel.requests[0][1]
assert expected_request == actual_request
def recognize_check(img_path, list_foods):
# Scale image
img = cv2.imread(img_path)
height, width = img.shape[:2]
img = cv2.resize(img, (800, int((height * 800) / width)))
img_path = os.path.join(SOURCE_PATH, "output.jpg")
cv2.imwrite(img_path, img)
# Recognize
client = vision.ImageAnnotatorClient()
image_context = types.ImageContext(language_hints=["ru"])
with io.open(img_path, 'rb') as image_file:
content = image_file.read()
image = vision.types.Image(content=content)
response = client.text_detection(image=image, image_context=image_context)
texts = response.text_annotations
food_res_list = []
for text in texts:
for line in list_foods:
if re.fullmatch(r'.?' + line + r'.?', text.description.lower()):
food_res_list.append(line)
food_res_list = [line.rstrip() for line in food_res_list]
for i in range(len(food_res_list)):
food_res_list[i] = food_res_list[i].title()
os.remove(img_path)
return food_res_list
def sample_batch_annotate_images():
# Create a client
client = vision_v1p4beta1.ImageAnnotatorClient()
# Initialize request argument(s)
request = vision_v1p4beta1.BatchAnnotateImagesRequest()
# Make the request
response = client.batch_annotate_images(request=request)
# Handle the response
print(response)
def test_batch_annotate_images_exception(self):
# Mock the API response
channel = ChannelStub(responses=[CustomException()])
patch = mock.patch("google.api_core.grpc_helpers.create_channel")
with patch as create_channel:
create_channel.return_value = channel
client = vision_v1p4beta1.ImageAnnotatorClient()
# Setup request
requests = []
with pytest.raises(CustomException):
client.batch_annotate_images(requests)
def recognize_food(img_path, list_foods):
# Scale image
img = cv2.imread(img_path)
height, width = img.shape[:2]
img = cv2.resize(img, (800, int((height * 800) / width)))
img_path = os.path.join(SOURCE_PATH, "output.jpg")
cv2.imwrite(img_path, img)
# Recognize
client = vision.ImageAnnotatorClient()
image_context = types.ImageContext(language_hints=["ru"])
with io.open(img_path, 'rb') as image_file:
content = image_file.read()
image = vision.types.Image(content=content)
response = client.label_detection(image=image)
labels = response.label_annotations
response = client.text_detection(image=image, image_context=image_context)
texts = response.text_annotations
food_res_list = []
is_find_img = False
for label in labels:
desc = label.description.lower()
score = round(label.score, 5)
if desc in list_foods and score > 0.7:
food_res_list.append(desc)
is_find_img = True
for text in texts:
for line in list_foods:
if re.fullmatch(r'.?' + line + r'.?', text.description.lower()):
food_res_list.append(line)
if is_find_img:
eng_rus_dict = codecs.open(
os.path.join(DICTIONARIES_PATH, "rec_eng_rus.dict"), 'r',
'utf_8_sig')
lines = eng_rus_dict.readlines()
for line in lines:
pair = line.split(':')
for i in range(len(food_res_list)):
if food_res_list[i] == pair[0]:
food_res_list[i] = pair[1]
food_res_list = [line.rstrip() for line in food_res_list]
for i in range(len(food_res_list)):
food_res_list[i] = food_res_list[i].title()
os.remove(img_path)
return food_res_list
def sample_async_batch_annotate_files():
# Create a client
client = vision_v1p4beta1.ImageAnnotatorClient()
# Initialize request argument(s)
request = vision_v1p4beta1.AsyncBatchAnnotateFilesRequest()
# Make the request
operation = client.async_batch_annotate_files(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
def test_async_batch_annotate_files_exception(self):
# Setup Response
error = status_pb2.Status()
operation = operations_pb2.Operation(
name="operations/test_async_batch_annotate_files_exception",
done=True)
operation.error.CopyFrom(error)
# Mock the API response
channel = ChannelStub(responses=[operation])
patch = mock.patch("google.api_core.grpc_helpers.create_channel")
with patch as create_channel:
create_channel.return_value = channel
client = vision_v1p4beta1.ImageAnnotatorClient()
# Setup Request
requests = []
response = client.async_batch_annotate_files(requests)
exception = response.exception()
assert exception.errors[0] == error
def test_batch_annotate_images(self):
# Setup Expected Response
expected_response = {}
expected_response = image_annotator_pb2.BatchAnnotateImagesResponse(
**expected_response)
# Mock the API response
channel = ChannelStub(responses=[expected_response])
patch = mock.patch("google.api_core.grpc_helpers.create_channel")
with patch as create_channel:
create_channel.return_value = channel
client = vision_v1p4beta1.ImageAnnotatorClient()
# Setup Request
requests = []
response = client.batch_annotate_images(requests)
assert expected_response == response
assert len(channel.requests) == 1
expected_request = image_annotator_pb2.BatchAnnotateImagesRequest(
requests=requests)
actual_request = channel.requests[0][1]
assert expected_request == actual_request
import os
from google.cloud import vision_v1p4beta1 as vision
from time import sleep
import time
import signal
import sys
import re
import socket
from process_image import process_image
from blackjack.blackjack_pi import BlackjackPi
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "./det_google_cloud.json"
client = vision.ImageAnnotatorClient()
image = 'test_image_hi.jpg'
blackjack = BlackjackPi()
def detect_hand(image):
"""Takes an image and uses GCV to detect valid cards in the image
Arguments:
image {Image} -- a GCV friendly image type
Returns:
[str] -- array of cards in hand
"""
