def custom_vision(image_name):
# Establishing key connections:
training_key = "{insert-training-key}"
prediction_key = "{insert-prediction-key}"
storage_account_url = "{insert-storage-account-url}"
ENDPOINT = "{insert-endpoint}"
pid = "{insert-pid}"
# For future re-training model purposes:
# trainer = CustomVisionTrainingClient(training_key, endpoint=training_endpoint)
predictor = CustomVisionPredictionClient(prediction_key, endpoint=ENDPOINT)
image_url = storage_account_url + image_name
print("Image url: " + image_url + "\n")
# Using the default iteration otherwise you can set iteration_id to something else
results = predictor.predict_image_url(project_id=pid, url=image_url)
return results
class ImageRater():
ENDPOINT = "https://southcentralus.api.cognitive.microsoft.com"
project_id = get_projectid_customvision()
def __init__(self):
self.predictor = CustomVisionPredictionClient(get_customvision_predictionkey(), endpoint=self.ENDPOINT)
def rate_image(self, img_url):
results = self.predictor.predict_image_url(self.project_id, url=img_url)
prediction = results.predictions[0]
probability = "{0:.2f} ".format(prediction.probability)
label = prediction.tag_name
friendly_results = dict()
friendly_results['label'] = label
friendly_results['confidence'] = probability
return friendly_results
def get_prediction(img_url):
from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient
ENDPOINT = "https://southcentralus.api.cognitive.microsoft.com"
project_id = '40412ce6-fe45-4fc2-b949-9bb6fe28abf2'
predictor = CustomVisionPredictionClient(
'57c2350f261b4cf0aec943bcf893e2d8', endpoint=ENDPOINT)
#import sys
#img_url = sys.argv[1]
#img_url = 'https://images.pexels.com/photos/259962/pexels-photo-259962.jpeg?auto=compress&cs=tinysrgb&h=350'
print(img_url)
results = predictor.predict_image_url(project_id, url=img_url)
#output_file_probabilities = open('prediction_result_probabilites.txt', 'w')
#output_file_labels = open('prediction_result_labels.txt', 'w')
prediction = results.predictions[0]
probability = "{0:.2f} ".format(prediction.probability)
label = prediction.tag_name
return [label, probability]
training_key = "8202cc602079498c8c0b5d002d422bc0"
prediction_key = "1bc26fdf4b984a54afae6d05074aeda4"
# Now there is a trained endpoint that can be used to make a prediction
predictor = CustomVisionPredictionClient(prediction_key, endpoint=ENDPOINT)
#test_img_url = "http://i1.adis.ws/i/jpl/bl_208555_a"
test_img_url1 = "https://www.popsci.com/sites/popsci.com/files/styles/655_1x_/public/images/2018/01/84711_fea.jpg?itok=nxNwehk-&fc=50,50"
test_img_url2 = "https://images-na.ssl-images-amazon.com/images/I/61%2BUrQmHDsL._UX679_.jpg"
project_id = "8bc27737-5835-4ba9-95f9-06984cd7d4e3"
iteration_id = "c4846c08-c001-47ae-bb5d-7c595dea16ad"
results = predictor.predict_image_url(project_id,
iteration_id,
url=test_img_url1)
# Display the results.
for prediction in results.predictions:
print("\t" + prediction.tag_name +
": {0:.2f}%".format(prediction.probability * 100))
results = predictor.predict_image_url(project_id,
iteration_id,
url=test_img_url2)
# Display the results.
for prediction in results.predictions:
print("\t" + prediction.tag_name +
": {0:.2f}%".format(prediction.probability * 100))
import matplotlib.pyplot as plt
from PIL import Image
# Needed to display matplotlib plots in Jupyter
get_ipython().run_line_magic('matplotlib', 'inline')
from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient
# Now there is a trained endpoint that can be used to make a prediction
predictor = CustomVisionPredictionClient(prediction_key, endpoint=ENDPOINT)
test_img1 = "https://image.sportsmansguide.com/adimgs/l/6/673942i3_ts.jpg"
test_img2 = "http://content.backcountry.com/images/items/900/MNT/MNT0012/MORBLUOR.jpg"
results1 = predictor.predict_image_url(project.id, iteration.id, url=test_img1)
results2 = predictor.predict_image_url(project.id, iteration.id, url=test_img2)
# Display the results.
for prediction in results1.predictions:
print("\t" + prediction.tag_name +
": {0:.2f}%".format(prediction.probability * 100))
for prediction in results2.predictions:
print("\t" + prediction.tag_name +
": {0:.2f}%".format(prediction.probability * 100))
# Create a figure to display the images
fig = plt.figure(figsize=(12, 16))
# Open it and add it to the figure (in a 4-row grid)
response = requests.get(test_img1, stream=True)
import matplotlib.pyplot as plt
from PIL import Image
from io import BytesIO
get_ipython().magic(u'matplotlib inline')
# Use two test images
test_img1_url = 'http://www.pachd.com/free-images/food-images/apple-01.jpg'
test_img2_url = 'http://www.pachd.com/free-images/food-images/carrot-01.jpg'
# Create an instance of prediction service
predictor = CustomVisionPredictionClient(PREDICTION_KEY, endpoint=ENDPOINT)
# Get prediction for image 1
result1 = predictor.predict_image_url(PROJECT_ID, url=test_img1_url)
# The results include a prediction for each tag, descending order of probability - so we'll get
prediction1 = result1.predictions[0].tag_name + ": {0:.2f}%".format(result1.predictions[0].probability)
# Get prediction for image 2
result2 = predictor.predict_image_url(PROJECT_ID, url=test_img2_url)
# The results include a prediction for each tag, descending order of probability - so we'll get
prediction2 = result2.predictions[0].tag_name + ": {0:.2f}%".format(result2.predictions[0].probability)
# Download images and show them
response = requests.get(test_img1_url)
img1 = Image.open(BytesIO(response.content))
response = requests.get(test_img2_url)
img2 = Image.open(BytesIO(response.content))
