| ''' | |
| import streamlit as st | |
| import cv2 | |
| # import tensorflow as tf | |
| import torch | |
| import torchvision | |
| import io | |
| from PIL import Image | |
| # import tensorflow_addons as tfa | |
| import numpy as np | |
| # from autocrop import Cropper | |
| def get_bbox(): | |
| pass | |
| def adjust_gamma(image, gamma=1.0): | |
| invGamma = 1.0 / gamma | |
| table = np.array([((i / 255.0) ** invGamma) * 255 | |
| for i in np.arange(0, 256)]).astype("uint8") | |
| return cv2.LUT(image, table) | |
| def loadtest(image,cropornot=False): | |
| # if cropornot: | |
| # Percent = st.sidebar.slider('Zoom adjust', min_value=50, max_value=100,value=50,step=5) | |
| # cropper = Cropper(face_percent=Percent) | |
| # | |
| # # Get a Numpy array of the cropped image | |
| # image_crop = cropper.crop(image) | |
| # if image_crop is not None: | |
| # image_crop = cv2.cvtColor(image_crop, cv2.COLOR_BGR2RGB) | |
| # image = tf.convert_to_tensor(image_crop, dtype=tf.float32) | |
| # else: | |
| # st.write('Cannot find your face to crop') | |
| image = (tf.cast(image, tf.float32) /255.0 *2) -1 | |
| image = tf.image.resize(image, | |
| [256, 256], | |
| method=tf.image.ResizeMethod.NEAREST_NEIGHBOR) | |
| image = tf.expand_dims(image, 0) | |
| return image | |
| def loadframe(image): | |
| image = tf.convert_to_tensor(image, dtype=tf.float32) | |
| image = (tf.cast(image, tf.float32) /255.0 *2) -1 | |
| image = tf.image.resize(image, | |
| [256, 256], | |
| method=tf.image.ResizeMethod.NEAREST_NEIGHBOR) | |
| image = tf.expand_dims(image, 0) | |
| return image | |
| ''' |