images added

Author: khanfarhan10

assets/images/screen.jpg

@@ -0,0 +1,128 @@
+"""
+deactivate
+conda.bat deactivate
+LungUNETCPUEnv\Scripts\activate
+python lungunetmodel_new.py
+"""
+import numpy as np # linear algebra
+import os
+import cv2
+import warnings
+warnings.filterwarnings('ignore')
+import tensorflow as tf
+
+"""
+from keras.models import *
+from keras.layers import *
+from keras.optimizers import *
+"""
+from keras import backend as keras
+from keras.preprocessing.image import ImageDataGenerator
+from keras.callbacks import ModelCheckpoint, LearningRateScheduler
+
+
+def dice_coef(y_true, y_pred):
+    y_true_f = tf.keras.flatten(y_true)
+    y_pred_f = tf.keras.flatten(y_pred)
+    intersection = tf.keras.sum(y_true_f * y_pred_f)
+    return (2. * intersection + 1) / (tf.keras.sum(y_true_f) + tf.keras.sum(y_pred_f) + 1)
+
+def dice_coef_loss(y_true, y_pred):
+    return -dice_coef(y_true, y_pred)
+
+def unet(input_size=(256,256,1)):
+    inputs = Input(input_size)
+    
+    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
+    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
+    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
+
+    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
+    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
+    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
+
+    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
+    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
+    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
+
+    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
+    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
+    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
+
+    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
+    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)
+
+    up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv5), conv4], axis=3)
+    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
+    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)
+
+    up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6), conv3], axis=3)
+    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
+    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)
+
+    up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv7), conv2], axis=3)
+    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
+    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)
+
+    up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv8), conv1], axis=3)
+    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
+    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)
+
+    conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)
+
+    return Model(inputs=[inputs], outputs=[conv10])
+
+model = unet(input_size=(512,512,1))
+model.compile(optimizer=Adam(lr=1e-5), loss=dice_coef_loss,
+                  metrics=[dice_coef, 'binary_accuracy'])
+# model.summary()
+
+weight_path="cxr_reg_weights.best.hdf5"
+model_weights_path = os.path.join(ROOT_DIR,"Weights",weight_path)
+model.load_weights(model_weights_path)
+
+"""
+Shapes that you wish to resize to
+"""
+
+Shape_X,Shape_Y=512,512
+
+def read_image(img_path):
+    image = cv2.imread(img_path)
+    image = cv2.resize(image,(Shape_Y,Shape_X))
+    return image
+
+
+def get_preds(image):
+    prep_unet_input_img_1 = image.reshape(1,Shape_X,Shape_Y,1)
+    prep_unet_input_img = (prep_unet_input_img_1-127.0)/127.0
+    pred_img = model.predict(prep_unet_input_img)
+    pred_img_preprocessed_1 = np.squeeze(pred_img)
+    pred_img_preprocessed = (pred_img_preprocessed_1*255>127).astype(np.int8)
+    res = cv2.bitwise_and(image,image,mask = pred_img_preprocessed)
+    return res,pred_img_preprocessed
+cv2.imwrite(save_path,res)
+
+
+if __name__ == '__main__':
+    INP = os.path.join(ROOT_DIR,"Sample_Inputs")
+    INP_RESHAPED = os.path.join(ROOT_DIR,"Sample_Inputs_Reshaped")
+    RES = os.path.join(ROOT_DIR,"Sample_Masked_Results")
+    MASK_PATH = os.path.join(ROOT_DIR,"Sample_Lung_Masks")
+    
+    
+    create_folders([INP,INP_RESHAPED,RES])
+    """
+    Images Output :
+    Original Reshaped Image
+    Superimposed Lungs Segmentation
+    """
+    input_files = os.listdir(INP)
+    for i,f in enumerate(input_files):
+        img = read_image(os.path.join(INP,f))
+        reshaped_img = deepcopy(img)
+        segmented_output,mask = get_preds(reshaped_img)
+        
+        cv2.imwrite(os.path.join(INP_RESHAPED,f),reshaped_img )
+        cv2.imwrite(os.path.join(RES,f),segmented_output)
+        cv2.imwrite(os.path.join(MASK_PATH ,f),mask)

assets/images/screen.png

@@ -1,6 +1,11 @@
 numpy
-tensorflow-cpu==2.4.1
-keras
+tensorflow-cpu==2.5.0
+# tensorflow-cpu==2.1.0 # ImportError: Keras requires TensorFlow 2.2 or higher. Install TensorFlow via `pip install tensorflow`
+# tensorflow-cpu==2.4.1 # AttributeError: module 'tensorflow.compat.v2.__internal__' has no attribute 'tf2'
+# tensorflow-cpu==2.4.0 # AttributeError: module 'tensorflow.compat.v2.__internal__' has no attribute 'tf2'
+
+# keras AttributeError: module 'keras.utils.generic_utils' has no attribute 'populate_dict_with_module_objects'
+keras==2.4.0
 # tensorflow-cpu==2.5.0
 # tensorflow==2.1.0
 # tensorflow==2.4.0