def apicall():
filename = ""
# Check if the 'test_image' key is present in the files section of the request.
# If it is that means that the android application (or whatever client sent this post request)
# has successfully sent an image file (of the sketch of the digit)
if 'test_image' in request.files:
# Get that image file and store it in file variable
file = request.files['test_image']
# Also save the filename
filename = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
# Now save this file on EC2 instance storage
file.save(filename)
else:
return (jsonify({
"prediction": "No Prediction",
"filename": "No Filename"
}))
test_image_path = filename
# Pass the path of this file which you just saved on EC2 Instance to your image processing function extract_number()
# It will process it and return the a list of file paths (these filepaths are for images of digits extracted from
# the original image).
all_numbers = extract_number(
test_image_path,
os.path.join(app.config['UPLOAD_FOLDER'],
filename.split('/')[-1] + "_"), "PROD")
digits = all_numbers[0]
X_coord = all_numbers[1]
predictions = []
for idx in np.argsort(X_coord):
# Recognizing digits using the trained model on the local machine itself
test_X = np.uint8(np.asarray(digits[idx]).reshape(1, -1))
pred_Y = predict_value(test_X)[0]
predictions.append(str(pred_Y))
# Return result as response to the application
return (jsonify({
"prediction": ''.join(predictions),
"filename": filename
}))
def apicall():
# Basepath - You need to changes this if you are not following the exact same folder structure.
# basepath = "/var/www/FlaskApplications/SampleApp/api"
filename = ""
# Check if the 'test_image' key is present in the files section of the request.
# If it is that means that the android application (or whatever client sent this post request)
# has successfully sent an image file (of the sketch of the digit)
if 'test_image' in request.files:
# Get that image file and store it in file variable
file = request.files['test_image']
# Also save the filename
filename = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
# Now save this file on EC2 instance storage
file.save(filename)
test_image_path = filename
# Pass the path of this file which you just saved on EC2 Instance to your image processing function extract_number()
# It will process it and return the a list of file paths (these filepaths are for images of digits extracted from
# the original image).
digits = extract_number(
test_image_path,
app.config['UPLOAD_FOLDER'] + filename.split('/')[-1] + "_")
predictions = []
for digitPath in digits:
# Convert the image of digit present on the filepath (digitPath) to pixel values
test_X = imageToPixel(digitPath).tolist()
# Recognizing digit using the trained model
pred_Y = predict_value(test_X)
predictions.append(pred_Y)
# Return result as response to the application
return (jsonify({"prediction": predictions[0][0], "filename": filename}))