def process_image(self, image):
file_converter = FileConverter()
#convert the image to byte string
image_bytes = file_converter.png_to_jpeg(image)
scanner = Scanner()
#scan the image and give it a birds eye view, returns a np of pixels that makes up the image
scan_np = scanner.scan(image_bytes)
#extract the individual answers from the scanned test
extractor = Extract()
answers = extractor.get_all_answers(scan_np, 5)
color = Color()
bw_answers = color.all_ans_to_bw(answers)
size = Size()
DIM = (28, 28)
shrunk_images = size.shrink_images(bw_answers, DIM)
#convert the answer images to a single array, which we used in training our model
answers_flat = file_converter.convert_images(
shrunk_images) #returns image as (1, 28, 28, 1) and as type float
#now that we have a list of images of the answers as bw 1D numpy arrays,
# we can run them through our model and grade them
# first we need to load our model
model_loader = ModelLoader()
MODEL_JSON = 'models/modified_model_98.json'
MODEL_WEIGHTS = 'models/modified_model_98.h5'
model = model_loader.load_model_2(MODEL_JSON, MODEL_WEIGHTS)
#compile model
model.compile(optimizer=RMSprop(lr=0.001),
loss='categorical_crossentropy',
metrics=['accuracy'])
grader = Grader()
answers = grader.get_answers(answers_flat, model)
#get the images as a 784 (28x28) length string so we can store the data in a database
ans_strings = file_converter.get_string_images(answers_flat)
compressed_images = file_converter.compress_images(ans_strings)
#add the images to database so we can create a large dataset of handwritten letters
# storage = Storage()
# storage.insert(answers, compressed_images)
return answers
