def resultado(request):
font = cv2.FONT_HERSHEY_SIMPLEX
fontScale = 0.7
fontColor = (0,0,255)
lineType = 2
name=request.GET["namefile"]
credentials = ApiKeyCredentials(in_headers={"Prediction-key": "<Prediction Key Aquí>"})
predictor = CustomVisionPredictionClient("<Zona regional aqui>", credentials)
blob = BlobClient.from_connection_string(conn_str=connection_string, container_name="images", blob_name=f"{name} training.png")
url = request.GET["link"]
urllib.request.urlretrieve(url, "python.png")
imagen=cv2.imread("python.png")
height, width, channels = imagen.shape
Resultado = predictor.detect_image_url("<Prediction Key>", "<Iteration>", url)
for prediction in Resultado.predictions:
if prediction.probability > 0.4:
bbox = prediction.bounding_box
tag = prediction.tag_name
probabilidad= int(prediction.probability * 100)
result_image = cv2.rectangle(imagen, (int(bbox.left * width), int(bbox.top * height)), (int((bbox.left + bbox.width) * width), int((bbox.top + bbox.height) * height)), (0, 255, 0), 3)
bottomLeftCornerOfText = (int(bbox.left*width),int(((bbox.top*height)+(bbox.height*height))))
cv2.putText(result_image,str(probabilidad)+"% "+tag,
bottomLeftCornerOfText,
font,
fontScale,
fontColor,
lineType)
cv2.imwrite('result.png', result_image)
with open("result.png","rb") as data:
blob.upload_blob(data)
return render(request,"resultado.html",{"imagen":blob.url,"namefile":name})
def call_custom_vision(image_url):
prediction_credentials = ApiKeyCredentials(
in_headers={"Prediction-key": custom_vision_prediction_key})
predictor = CustomVisionPredictionClient(custom_vision_endpoint,
prediction_credentials)
results = predictor.detect_image_url(project_id, publish_iteration_name,
image_url)
return results
