Bulk Predict and Move Files - Keras Teacheable Machine

Bulk Classifier & Organizer - Teacheable Machine (Keras).ipynb

Bulk Predict - Keras Teacheable Machine.py

import tensorflow.keras
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
from keras.preprocessing import image
import numpy as np
import shutil
import os

## Set up Parameters
# Image folder to predict
folder_path = r'C:/Users/Pichau/RPLAN-Toolbox/output_80K_B_ORGANIZED/test'
# Path to model
model_path = r'C:/Users/Pichau/Downloads/converted_keras/keras_model.h5'
# Dimensions of images
img_width, img_height = 224, 224
## Change where each class will be moved to
Class_Folder_00 = r'C:/Users/Pichau/RPLAN-Toolbox/output_80K_B_ORGANIZED/7_Room'
Class_Folder_01 = r'C:/Users/Pichau/RPLAN-Toolbox/output_80K_B_ORGANIZED/8_Room'
Class_Folder_02 = r'C:/Users/Pichau/RPLAN-Toolbox/output_80K_B_ORGANIZED/6_Room'



# load the trained model
model = load_model(model_path, compile = False)
model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])
print("Model Loaded")



## PREDICT IMAGES AND MOVE FILES
images = []
for img in os.listdir(folder_path):
    file_name = img
    file_path = os.path.join(folder_path, img)
    
    img = os.path.join(folder_path, img)
    img = image.load_img(img, target_size=(img_width, img_height))
    img = image.img_to_array(img)
    img = np.expand_dims(img, axis=0)
    images.append(img)
    prediction = model.predict_classes(img)
    #print("Predicted to class ==", prediction)
    
    if prediction == int("0"):
        filename0 = os.path.join(Class_Folder_00, file_name)
        dest = shutil.move(file_path, filename0)
        print("Predicted to class = 0 and moved to",filename0)
        
    if prediction == int("1"):
        filename1 = os.path.join(Class_Folder_01, file_name)
        dest = shutil.move(file_path, filename1)
        print("Predicted to class = 1 and moved to",filename1)
        
    if prediction == int("2"):
        filename2 = os.path.join(Class_Folder_02, file_name)
        dest = shutil.move(file_path, filename2)
        print("Predicted to class = 2 and moved to",filename2)