tyre

predictions.py

tyre = ["Flat Tyre","Full Tyre","No Tyre"]
def Single_Image_Prediction(file):
    #image = load_img(file, color_mode='rgb', target_size=(128, 128))
    image= file
    plt.imshow(image,cmap= 'gray')
    plt.show()
    print(image.shape)
#     cv.imshow('image',file)
#     cv.waitKey(0)
#     cv.destroyAllWimdows()
#     image = cv.cvtColor(image, cv.COLOR_RGB2GRAY)
    img_arr = img_to_array(image)
#     img_arr = img_arr/255.
    np_image = np.expand_dims(img_arr, axis=0)
    return np_image
  
image = Single_Image_Prediction(val_generator[0][0][11])
pred_value = model.predict(image)
print(pred_value)
index_value = np.argmax(pred_value,axis=1) #For categorical model
print(tyre[index_value[0]])

image = Single_Image_Prediction(val_generator[0][0][12])
pred_value = model.predict(image)
print(pred_value)
index_value = np.argmax(pred_value,axis=1) #For categorical model
print(tyre[index_value[0]])

image = Single_Image_Prediction(val_generator[0][0][0])
pred_value = model.predict(image)
print(pred_value)
index_value = np.argmax(pred_value,axis=1) #For categorical model
print(tyre[index_value[0]])