Module monk.tf_keras_1.testing.process

Functions

def process_single(img_name, return_raw, system_dict)

Run inference on a single image

Args

img_name : str

path to image

return_raw : bool

If True, then output dictionary contains image probability for every class in the set. Else, only the most probable class score is returned back.

Returns

str

predicted class

float

prediction score

Expand source code

def process_single(img_name, return_raw, system_dict):
    '''
    Run inference on a single image

    Args:
        img_name (str): path to image
        return_raw (bool): If True, then output dictionary contains image probability for every class in the set.
                            Else, only the most probable class score is returned back.
                            

    Returns:
        str: predicted class
        float: prediction score
    '''
    input_size = system_dict["dataset"]["params"]["input_size"];
    normalize = system_dict["local"]["normalize"];
    mean_subtract = system_dict["local"]["mean_subtract"];
    mean = system_dict["local"]["transforms_test"]["mean"];
    std = system_dict["local"]["transforms_test"]["std"];

    img = cv2.imread(img_name, 1);
    img = cv2.resize(img, (input_size, input_size))
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB);
    img = img.astype(np.float32);

    if(normalize):
        img = img-mean;
        img = img/std;
    elif(mean_subtract):
        img = img-mean;
    img = np.expand_dims(img, axis=0)



    output = system_dict["local"]["model"].predict(img);

    if(system_dict["dataset"]["params"]["classes"]):
        class_names = list(system_dict["dataset"]["params"]["classes"].keys());
        prediction = class_names[np.argmax(output)];
    else:
        prediction = np.argmax(output);

    score = output[0][np.argmax(output)];

    if(return_raw):
        return prediction, score, output[0];
    else:
        return prediction, score, "";