def run_sample():
try:
# Create the BlockBlockService that is used to call the Blob service for the storage account
block_blob_service = BlockBlobService(
account_name='functionsimgpro9756',
account_key='****************************')
# Create a container called 'quickstartblobs'.
container_name = 'samples-workitems'
block_blob_service.create_container(container_name)
# Set the permission so the blobs are public.
block_blob_service.set_container_acl(
container_name, public_access=PublicAccess.Container)
local_file_name = "Mobile_camera_Feed.jpg"
full_path_to_file = "./Mobile_camera_Feed.jpg"
predictor = CustomVisionPredictionClient(prediction_key,
endpoint=ENDPOINT)
project = find_project()
while True:
print("HI!!!")
img_resp = requests.get(url)
img_arr = np.array(bytearray(img_resp.content), dtype=np.uint8)
time.sleep(0.1)
try:
img = cv2.imdecode(img_arr, -1)
except:
print("An exception occurred")
cv2.imwrite("Mobile_camera_Feed.jpg", img)
block_blob_service.create_blob_from_path(container_name,
local_file_name,
full_path_to_file)
#Make Prediction
print("Make prediction")
with open("Mobile_camera_Feed.jpg", mode="rb") as test_data:
results = predictor.predict_image(project.id, test_data.read())
waste_type = results.predictions[0].tag_name
# Display the results.
print(waste_type)
sendUpdateToArduino(waste_type)
# for prediction in results.predictions:
# # print(prediction.tag_name + ": {0:.2f}%".format(prediction.probability * 100))
# print(prediction.tag_name)
if cv2.waitKey(1) == 27:
break
sleep(10)
except Exception as e:
os.remove(full_path_to_file)
print(e)
def predict_project(prediction_key, project, iteration):
predictor = CustomVisionPredictionClient(prediction_key, endpoint=ENDPOINT)
# Open the sample image and get back the prediction results.
with open(os.path.join(IMAGES_FOLDER, "Test", "test_od_image.jpg"), mode="rb") as test_data:
results = predictor.predict_image(project.id, test_data, iteration.id)
# Display the results.
for prediction in results.predictions:
print ("\t" + prediction.tag_name + ": {0:.2f}%".format(prediction.probability * 100), prediction.bounding_box.left, prediction.bounding_box.top, prediction.bounding_box.width, prediction.bounding_box.height)
def predict_project(prediction_key, file_path):
predictor = CustomVisionPredictionClient(prediction_key, endpoint=ENDPOINT)
with open(os.path.join(IMAGES_FOLDER, file_path), mode="rb") as test_data:
results = predictor.predict_image(PROJECT_ID, test_data.read())
# Display the results.
for prediction in results.predictions:
print("\t" + prediction.tag_name +
": {0:.2f}%".format(prediction.probability * 100))
def predict_project(subscription_key):
predictor = CustomVisionPredictionClient(subscription_key,
endpoint=ENDPOINT)
# Find or train a new project to use for prediction.
project = find_or_train_project()
with open(os.path.join(IMAGES_FOLDER, "Test", "test_image.jpg"),
mode="rb") as test_data:
results = predictor.predict_image(project.id, test_data.read())
# Display the results.
for prediction in results.predictions:
print("\t" + prediction.tag_name +
": {0:.2f}%".format(prediction.probability * 100))
class PotDetector:
def __init__(self):
self.ENDPOINT = "https://southcentralus.api.cognitive.microsoft.com"
# Project keys
self.training_key = "dae03cb013f840658708cd62781d90c1"
self.prediction_key = "6212b24516c6492190c63d2b32084079"
self.project_id = "a97fb679-77e7-4e07-b946-81c752ee3112"
self.probability_min = 60
# Now there is a trained endpoint that can be used to make a prediction
self.predictor = CustomVisionPredictionClient(self.prediction_key,
endpoint=self.ENDPOINT)
def update(self, frame, stove):
# Open the sample image and get back the prediction results.
results = self.predictor.predict_image(self.project_id, frame)
center_x = 0.0
center_y = 0.0
stove.upper_right.pot_detected = False
stove.upper_left.pot_detected = False
stove.lower_left.pot_detected = False
stove.lower_right.pot_detected = False
# Display the results.
for prediction in results.predictions:
if (prediction.probability * 100 > self.probability_min):
center_x = prediction.bounding_box.left + (
(prediction.bounding_box.width) / 2)
center_y = prediction.bounding_box.top + (
(prediction.bounding_box.height) / 2)
if (center_x > 0.5 and center_y < 0.5):
stove.upper_right.pot_detected = True
elif (center_x < 0.5 and center_y < 0.5):
stove.upper_left.pot_detected = True
elif (center_x < 0.5 and center_y > 0.5):
stove.lower_left.pot_detected = True
elif (center_x > 0.5 and center_y > 0.5):
stove.lower_right.pot_detected = True
def predict_project(filename):
predictor = CustomVisionPredictionClient(PREDICTION_KEY, endpoint=ENDPOINT)
# Find or train a new project to use for prediction.
project = find_project()
if (project == False):
print("Run training first, project not found!")
return []
with open(filename, mode="rb") as test_data:
results = predictor.predict_image(project.id, test_data.read())
# Display the results.
for prediction in results.predictions:
print("\t" + prediction.tag_name +
": {0:.2f}%".format(prediction.probability * 100))
return results
if (proj.name == YOUR_PROJECT_NAME):
return proj
except Exception as e:
print(str(e))
print("Set path for folder containign image to predict")
IMAGES_FOLDER = os.path.dirname(os.path.realpath(__file__))
# print(str(IMAGES_FOLDER))
# print(str(os.path.join(IMAGES_FOLDER, "ElephantGiraffeTestImages", "testelephant2.jpg")))
# Get predictor and project objects
predictor = CustomVisionPredictionClient(prediction_key, endpoint=ENDPOINT)
project = find_project()
print("Make prediction")
try:
with open(os.path.join(IMAGES_FOLDER, "ElephantGiraffeTestImages",
"testgiraffe1.jpg"),
mode="rb") as test_data:
results = predictor.predict_image(project.id, test_data.read())
except Exception as e:
print(str(e))
input()
# Display the results.
for prediction in results.predictions:
print(prediction.tag_name +
": {0:.2f}%".format(prediction.probability * 100))
input()
seconds = stop - start
print(1 / seconds)
key = cv2.waitKey(1) & 0xFF
rawCapture.truncate(0)
if key == ord("q"):
break
else:
lab_img = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
l_c, a_c, b_c = cv2.split(lab_img)
t = ""
if (np.average(l_c)) > 100: #light threshold
t = "Daylight"
with open(image_path, mode="rb") as test_data:
results = predictor.predict_image(project_id, test_data,
iteration_id)
im_d = np.array(image, dtype=np.uint8)
res = np.asarray([[
p.tag_name, p.probability, p.bounding_box.left,
p.bounding_box.top, p.bounding_box.width, p.bounding_box.height
] for p in results.predictions])
classes = res[res[:, 1].astype(np.float) > thresh]
w, h, c = im_d.shape
else:
t = "Infrared"
with open(image_path, mode="rb") as test_data:
results = predictor_i.predict_image(project_id_i, test_data,
iterationId_i)
im_d = np.array(image, dtype=np.uint8)
print("Training...")
iteration = trainer.train_project(project.id)
while (iteration.status != "Completed"):
iteration = trainer.get_iteration(project.id, iteration.id)
print("Training status: " + iteration.status)
time.sleep(1)
# The iteration is now trained. Make it the default project endpoint
trainer.update_iteration(project.id, iteration.id, is_default=True)
print("Done!")
# Text model and show results
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)
# Open the sample image and get back the prediction results.
with open("images/Test/test_od_image.jpg", mode="rb") as test_data:
results = predictor.predict_image(project.id, test_data, iteration.id)
# Display the results.
for prediction in results.predictions:
print(
"\t" + prediction.tag_name +
": {0:.2f}%".format(prediction.probability * 100),
prediction.bounding_box.left, prediction.bounding_box.top,
prediction.bounding_box.width, prediction.bounding_box.height)
# Azure custom vision prediction service
from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient
# Change endpoint location according to Azure account
ENDPOINT = "https://southeastasia.api.cognitive.microsoft.com"
projectID = '47917e0f-ee76-4fc3-afe4-1eb02b94d6b0'
# Replace with a valid key
training_key = "6ad939516c234dfc8e7de03935037264"
prediction_key = "4b0ab4fa945a41b187c5fcb6c4ea5cdb"
# Now there is a trained endpoint that can be used to make a prediction
predictor = CustomVisionPredictionClient(prediction_key, endpoint=ENDPOINT)
img_path = input('Image path: ')
# Open the sample image and get back the prediction results.
with open(img_path, mode="rb") as test_data:
results = predictor.predict_image(projectID, test_data)
# Display the results.
for prediction in results.predictions:
print(
"\t" + prediction.tag_name +
": {0:.2f}%".format(prediction.probability * 100),
prediction.bounding_box.left, prediction.bounding_box.top,
prediction.bounding_box.width, prediction.bounding_box.height)
def Google_Microsoft_Fun(image_path, file_name_created):
# Google: Instantiates a google client
client = vision.ImageAnnotatorClient()
# Microsoft: a trained endpoint that can be used to make a prediction
prediction_key = "82399d964ace4ef8a3b8a8b5a2540540"
#predictor = prediction_endpoint.PredictionEndpoint(prediction_key)
ENDPOINT = "https://southcentralus.api.cognitive.microsoft.com"
predictor = CustomVisionPredictionClient(prediction_key, endpoint=ENDPOINT)
# iteration = trainer.train_project(3c2e78ba-4367-4941-8cf8-b668deb28726)
# Activity_Recognition_New
project_id = "d620a7d4-2aec-42a1-8ea3-810077cdcbc2"
"""create a list of file paths of images"""
path = image_path
dirs = os.listdir(path)
image_list = []
activity_highest = []
# print(dirs)
for f in dirs:
if f.endswith('jpg'):
image_list.append(os.path.join(path, f))
# sort the images with the number of image (P.S the convention of image name has to be whatever_1.jpg, whatever_2.jpg)
image_list = sorted(image_list,
key=lambda x: int(x.split('.')[-2].split('_')[-1]))
"""write the returned labels """
python_name = file_name_created
file = open(python_name, "w")
for f in image_list:
with open(python_name, "a") as file:
file.write(f)
file.write("\n")
with io.open(f, 'rb') as image_file:
# google_labels
content = image_file.read()
image = types.Image(content=content)
response = client.label_detection(image=image)
labels = response.label_annotations
for i in labels:
with open(python_name, "a") as file:
file.write("#")
file.write(i.description.encode("utf-8"))
file.write("\n")
# microsoft
with io.open(f, 'rb') as image_file:
results = predictor.predict_image(project_id, image_file)
count = 0
with open(python_name, "a") as file:
for prediction in results.predictions:
if count == 0:
str = prediction.tag_name.encode("utf-8")
# print("highest score is "+str)
file.write("!")
file.write(str)
file.write("\n")
# print ("\t" + prediction.tag_name + ": {0:.2f}%".format(prediction.probability * 100))
file.write("---" + prediction.tag_name +
": {0:.2f}%".format(prediction.probability *
100))
count = count + 1
file.write("\n")
file.close()
def get(self):
json_data = {"data": "open", "loc": ""}
video_capture = cv2.VideoCapture(0)
# Get predictor and project objects
predictor = CustomVisionPredictionClient(prediction_key,
endpoint=ENDPOINT)
project = find_project()
ret, frame = video_capture.read()
#print (ret)
cv2.imwrite('./color_img.jpg', frame)
#cv2.imshow('Video', frame)
try:
with open('./color_img.jpg', mode="rb") as test_data:
results = predictor.predict_image(project.id, test_data.read())
except Exception as e:
print(str(e))
input()
# Display the results.
for prediction in results.predictions:
#print(prediction.tag_name + ": {0:.2f}%".format(prediction.probability * 100))
if prediction.tag_name == CLOSED_EYES:
if ((prediction.probability * 100) > 0.01):
global count
count += 1
if count == 1:
# global count
count = 0
acc = '66ae978d-d815-4f75-8fe0-fc765dc5b829'
req = smartcar.get_vehicle_ids(acc)
vld = req['vehicles'][0]
v = smartcar.Vehicle(vld, acc)
print(v.location())
json_data = {
"data": "closed",
"loc": v.location()['data']
}
message(vld, v.location()['data'])
#request.put(json=json_data)
print("hazard!")
url = 'https://www.jsonstore.io/962b54063ad9a4019de7f1629eea83173b549ae39f2d064e1f9f724b35851731'
headers = {'Content-Type': 'application/json'}
requests.post(url, json=json_data, headers=headers)
else:
json_data = {"data": "open", "loc": ""}
url = 'https://www.jsonstore.io/962b54063ad9a4019de7f1629eea83173b549ae39f2d064e1f9f724b35851731'
headers = {'Content-Type': 'application/json'}
requests.post(url, json=json_data, headers=headers)
count = 0
#send_main(SRC, DST, "!!!")
# else:
# json_data = {"data":"allgood", "loc":""}
# #global count
# #count = 0
# input()
# if cv2.waitKey(1) & 0xFF == ord('q'):
# break
video_capture.release()
cv2.destroyAllWindows()
return json_data
# pip install azure-cognitiveservices-vision-customvision
from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient
# Now there is a trained endpoint that can be used to make a prediction
ENDPOINT = "https://southcentralus.api.cognitive.microsoft.com"
predictor = CustomVisionPredictionClient("Prediction key", endpoint=ENDPOINT)
# Open the sample image and get back the prediction results.
with open("test.jpg", mode="rb") as test_data:
results = predictor.predict_image("Project.id", test_data)
# Display the results.
for prediction in results.predictions:
print ("\t" + prediction.tag_name + ": {0:.2f}%".format(prediction.probability * 100), prediction.bounding_box.left, prediction.bounding_box.top, prediction.bounding_box.width, prediction.bounding_box.height)
