Python Photo Classifier (Starter Version)

photo_classifier.py

from keras.models import load_model  # TensorFlow is required for Keras to work
from PIL import Image, ImageOps  # Install pillow instead of PIL
import numpy as np
import os
import shutil

# ----------------------------
# Setup Model
# ----------------------------
# Disable scientific notation for clarity
np.set_printoptions(suppress=True)

# Load the model
model = load_model("keras_model.h5", compile=False)
# Load the labels
class_names = open("labels.txt", "r").readlines()


# ----------------------------
# File Operations
# ----------------------------
def list_files(directory):
    files = []
    for f in os.listdir(directory):
        if os.path.isfile(os.path.join(directory, f)):
            files.append(f)
    return files


def copy_to_class_folder(src_image, name, output_class_path):
    if not os.path.exists(output_class_path):
        os.makedirs(output_class_path)

    output_file = os.path.join(output_class_path, name)

    # copy the image to the output path
    shutil.copy(src_image, output_file)


# ----------------------------
# Predict Label & Score From Image
# ----------------------------
def predict_image_label(image_path):
    
    # Action: Predict the label and score from the image
    # Hint: Use the code from photo_classifier_test.py

    final_label = "???"
    confidence_score = 0.0

    # return tuple
    return (final_label, confidence_score)


# ----------------------------
# Classify and Copy Image to Classified Folder 
# ----------------------------
file_counter = 0
def classify_image(image_path, output_path):
    global file_counter
    file_counter = file_counter + 1

    (class_name, score) = predict_image_label(image_path)

    print("Class:", class_name, " score: ", score, " source: ", image_path)

    # copy the image to the output path
    # Hint: Define out filename with class_name & filter_counter
    #       Use copy_to_class_folder to copy file 


# ----------------------------
# Main 
# ----------------------------
# Specify the directory path
input_path = "./input"
output_path = "./output"
files = list_files(input_path)
for fname in files:
    
    full_name = os.path.join(input_path, fname)
    print ("File: ", full_name)
    classify_image(full_name, output_path)