inspectSection.py

# Import library of helper functions
import ImgPrcLib as IPL

# Import other required packages
import os
import tensorflow as tf
import numpy as np

# Constants
bitmask_directory = "./BitmaskImages"
network_model_filepath = "./NeuralNetwork/model.json"
network_weights_filepath = "./NeuralNetwork/model.h5"



def inspectSection(input_image, pose_id):
    """
        inspectSection: Takes a raw image of airfoil, applies bitmasking and
            cropping procedure based on the given pose_id, then evaluates the 
            image to determine if the part is OK/NOT-OK.
    
        Parameters
        ----------
        input_image : MxNx3 uint8 array
            Original RGB image directly from camera.
        pose_id : int
            Pose number that image was taken from
    
        Returns
        -------
        result: boolean
            True if image is OK, False if image is Not-OK
            
    """  
    
    
    # ----- Load Bitmask -----
    bitmask_filepath = os.path.join(bitmask_directory, 'POSE' + str(pose_id) + '.JPG')
    bitmask = IPL.loadBitmaskImage(bitmask_filepath)
    
    # ----- Apply Bitmask and Crop Image -----
    processed_image = IPL.processImage(input_image, bitmask)
    #IPL.imshow(processed_image)
    
    # ----- Resize Image -----
    downsampled_img = tf.image.resize_with_pad(processed_image, 224, 224)
    
    # ----- Load NeuralNetwork -----
    print("Starting to load model")
    network = IPL.loadNetwork(network_model_filepath, network_weights_filepath)
    print("Model Loaded.")
    
    
    
    # ----- Evaluate Image -----
    result = network(np.expand_dims(downsampled_img,0))
    
    # ----- Determine Class -----
    # argmax=0 --> NOT-OK
    # argmax=1 --> OK
    predicted_class = np.argmax(result)
    
    result = (predicted_class == 1)
    
    return result