| import tensorflow as tf | |
| from tensorflow.keras.layers import Dense, Dropout, MaxPooling2D, Conv2D, UpSampling2D, concatenate, Input | |
| from tensorflow.keras.optimizers import Adam | |
| from tensorflow.keras.models import Model | |
| from tensorflow.keras.callbacks import TensorBoard, EarlyStopping, ModelCheckpoint | |
| from tensorflow.keras import backend as K | |
| import os | |
| import cv2 | |
| import gradio as gr | |
| import numpy as np | |
| import time | |
| SIZE_IMG = 128 | |
| DATA_DIR = '/content/drive/MyDrive/Dataset/' | |
| def dice(y_true, y_pred, smooth=1.): | |
| y_true_f = K.flatten(y_true) | |
| y_pred_f = K.flatten(y_pred) | |
| intersection = K.sum(y_true_f * y_pred_f) | |
| return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth) | |
| model = tf.keras.models.load_model('mymodel-pretrain.h5', custom_objects = {'dice':dice}) | |
| def process(img, r,g,b): | |
| img_raw = img | |
| img_org = img_raw.copy() | |
| img = cv2.cvtColor(img_raw, cv2.COLOR_BGR2GRAY) | |
| h,w = img.shape | |
| img = cv2.resize(img,(128,128)) | |
| arr_img = np.array(img)/255.0 | |
| arr_img = arr_img.reshape(-1, 128,128,1) | |
| res = model.predict(arr_img) | |
| mask = np.array(cv2.resize(res[0]*255,(w,h)), np.uint8) | |
| def changeValue(img, mask, max_range, min_range, axis = 2): | |
| value = img[:,:,axis] | |
| x = value[mask>50] | |
| if (len(x)==0): | |
| return img | |
| if (x.max() == x.min()): | |
| index = np.where(mask>50) | |
| for i,j in zip(index[0],index[1]): | |
| img[i,j,axis] = (max_range + min_range)/2 | |
| return np.array(img ,np.uint8) | |
| else: | |
| index = np.where(mask>50) | |
| ratio = (max_range-min_range)/(x.max()-x.min()) | |
| x = x*ratio + min_range - x.min()*ratio | |
| n = 0 | |
| for i,j in zip(index[0],index[1]): | |
| img[i,j,axis] = x[n] | |
| n += 1 | |
| return np.array(img ,np.uint8) | |
| def changeColor(img , mask, img_org, r = 0, g = 0, b = 0): | |
| img = changeValue(img, mask, r, 0,2) | |
| img = changeValue(img, mask, g, 0,1) | |
| img = changeValue(img, mask, b, 0,0) | |
| return img | |
| return changeColor(img_raw, mask, img_org, r,g, b) | |
| def image_classifier(i,color): | |
| (b,g,r) = tuple(int(color[i:i+2], 16) for i in (1, 3, 5)) | |
| return process(i, r, g, b) | |
| inputs = ["image", gr.ColorPicker(label="color")] | |
| demo = gr.Interface(fn=image_classifier, inputs=inputs, outputs="image") | |
| demo.launch() |