# This Python 3 environment comes with many helpful analytics libraries installed

# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python

# For example, here's several helpful packages to load in 

import numpy as np # linear algebra

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the "../input/" directory.

# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
import os, sys

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import skimage.io

from skimage.transform import resize

#from imgaug import augmenters as iaa

from tqdm import tqdm

import PIL

from PIL import Image, ImageOps

import cv2

from sklearn.utils import class_weight, shuffle

from keras.losses import binary_crossentropy

from keras.applications.resnet50 import preprocess_input

import keras.backend as K

import tensorflow as tf

from sklearn.metrics import f1_score, fbeta_score

from keras.utils import Sequence

from keras.utils import to_categorical

from sklearn.model_selection import train_test_split

WORKERS = 2

CHANNEL = 3

import warnings

warnings.filterwarnings("ignore")

IMG_SIZE = 512

NUM_CLASSES = 5

SEED = 77

TRAIN_NUM = 1000 # use 1000 when you just want to explore new idea, use -1 for full train
df_train = pd.read_csv('F:\\kaggleDataSet\\diabeticRetinopathy\\trainLabels19.csv')

df_test = pd.read_csv('F:\\kaggleDataSet\\diabeticRetinopathy\\testImages19.csv')

x = df_train['id_code']

y = df_train['diagnosis']

x, y = shuffle(x, y, random_state=SEED)
train_x, valid_x, train_y, valid_y = train_test_split(x, y, test_size=0.15,stratify=y, random_state=SEED)

print(train_x.shape, train_y.shape, valid_x.shape, valid_y.shape)

train_y.hist()

valid_y.hist()

%%time

fig = plt.figure(figsize=(25, 16))

# display 10 images from each class

for class_id in sorted(train_y.unique()):

    for i, (idx, row) in enumerate(df_train.loc[df_train['diagnosis'] == class_id].sample(5, random_state=SEED).iterrows()):

        ax = fig.add_subplot(5, 5, class_id * 5 + i + 1, xticks=[], yticks=[])

        path="F:\\kaggleDataSet\\diabeticRetinopathy\\resized train 19\\"+str(row['id_code'])+".jpg"

        image = cv2.imread(path)

        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        image = cv2.resize(image, (IMG_SIZE, IMG_SIZE))

        plt.imshow(image)

        ax.set_title('Label: %d-%d-%s' % (class_id, idx, row['id_code']) )

%%time

fig = plt.figure(figsize=(25, 16))

for class_id in sorted(train_y.unique()):

    for i, (idx, row) in enumerate(df_train.loc[df_train['diagnosis'] == class_id].sample(5, random_state=SEED).iterrows()):

        ax = fig.add_subplot(5, 5, class_id * 5 + i + 1, xticks=[], yticks=[])

        path="F:\\kaggleDataSet\\diabeticRetinopathy\\resized train 19\\"+str(row['id_code'])+".jpg"

        image = cv2.imread(path)

        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

#         image=cv2.addWeighted ( image, 0 , cv2.GaussianBlur( image , (0 ,0 ) , 10) ,-4 ,128)

        image = cv2.resize(image, (IMG_SIZE, IMG_SIZE))

        plt.imshow(image, cmap='gray')

        ax.set_title('Label: %d-%d-%s' % (class_id, idx, row['id_code']) )

dpi = 80 #inch

# path=f"../input/aptos2019-blindness-detection/train_images/5c7ab966a3ee.png" # notice upper part

path="F:\\kaggleDataSet\\diabeticRetinopathy\\resized train 19\\cd54d022e37d.jpg" # lower-right, this still looks not so severe, can be class3

image = cv2.imread(path)

image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

height, width = image.shape

print(height, width)

SCALE=2

figsize = (width / float(dpi))/SCALE, (height / float(dpi))/SCALE

fig = plt.figure(figsize=figsize)

plt.imshow(image, cmap='gray')

%%time

fig = plt.figure(figsize=(25, 16))

for class_id in sorted(train_y.unique()):

    for i, (idx, row) in enumerate(df_train.loc[df_train['diagnosis'] == class_id].sample(5, random_state=SEED).iterrows()):

        ax = fig.add_subplot(5, 5, class_id * 5 + i + 1, xticks=[], yticks=[])

        path="F:\\kaggleDataSet\\diabeticRetinopathy\\resized train 19\\"+str(row['id_code'])+".jpg"

        image = cv2.imread(path)

        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

#         image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        image = cv2.resize(image, (IMG_SIZE, IMG_SIZE))

        image=cv2.addWeighted ( image,4, cv2.GaussianBlur( image , (0,0) , IMG_SIZE/10) ,-4 ,128) # the trick is to add this line

        plt.imshow(image, cmap='gray')

        ax.set_title('Label: %d-%d-%s' % (class_id, idx, row['id_code']) )

def crop_image1(img,tol=7):

    # img is image data

    # tol  is tolerance

    mask = img>tol

    return img[np.ix_(mask.any(1),mask.any(0))]

def crop_image_from_gray(img,tol=7):

    if img.ndim ==2:

        mask = img>tol

        return img[np.ix_(mask.any(1),mask.any(0))]

    elif img.ndim==3:

        gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

        mask = gray_img>tol

        check_shape = img[:,:,0][np.ix_(mask.any(1),mask.any(0))].shape[0]

        if (check_shape == 0): # image is too dark so that we crop out everything,

            return img # return original image

        else:

            img1=img[:,:,0][np.ix_(mask.any(1),mask.any(0))]

            img2=img[:,:,1][np.ix_(mask.any(1),mask.any(0))]

            img3=img[:,:,2][np.ix_(mask.any(1),mask.any(0))]

    #         print(img1.shape,img2.shape,img3.shape)

            img = np.stack([img1,img2,img3],axis=-1)

    #         print(img.shape)

        return img
def load_ben_color(path, sigmaX=10):

    image = cv2.imread(path)

    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    image = crop_image_from_gray(image)

    image = cv2.resize(image, (IMG_SIZE, IMG_SIZE))

    image=cv2.addWeighted ( image,4, cv2.GaussianBlur( image , (0,0) , sigmaX) ,-4 ,128)

    return image
%%time

NUM_SAMP=7

fig = plt.figure(figsize=(25, 16))

for class_id in sorted(train_y.unique()):

    for i, (idx, row) in enumerate(df_train.loc[df_train['diagnosis'] == class_id].sample(NUM_SAMP, random_state=SEED).iterrows()):

        ax = fig.add_subplot(5, NUM_SAMP, class_id * NUM_SAMP + i + 1, xticks=[], yticks=[])

        path="F:\\kaggleDataSet\\diabeticRetinopathy\\resized train 19\\"+str(row['id_code'])+".jpg"

        image = load_ben_color(path,sigmaX=30)

        plt.imshow(image)

        ax.set_title('%d-%d-%s' % (class_id, idx, row['id_code']) )

def circle_crop(img, sigmaX=10):

    """

    Create circular crop around image centre

    """

    img = cv2.imread(img)

    img = crop_image_from_gray(img)

    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    height, width, depth = img.shape

    x = int(width/2)

    y = int(height/2)

    r = np.amin((x,y))

    circle_img = np.zeros((height, width), np.uint8)

    cv2.circle(circle_img, (x,y), int(r), 1, thickness=-1)

    img = cv2.bitwise_and(img, img, mask=circle_img)

    img = crop_image_from_gray(img)

    img=cv2.addWeighted ( img,4, cv2.GaussianBlur( img , (0,0) , sigmaX) ,-4 ,128)

    return img 
%%time

## try circle crop

NUM_SAMP=7

fig = plt.figure(figsize=(25, 16))

for class_id in sorted(train_y.unique()):

    for i, (idx, row) in enumerate(df_train.loc[df_train['diagnosis'] == class_id].sample(NUM_SAMP, random_state=SEED).iterrows()):

        ax = fig.add_subplot(5, NUM_SAMP, class_id * NUM_SAMP + i + 1, xticks=[], yticks=[])

        path="F:\\kaggleDataSet\\diabeticRetinopathy\\resized train 19\\"+str(row['id_code'])+".jpg"

        image = circle_crop(path,sigmaX=30)

        plt.imshow(image)

        ax.set_title('%d-%d-%s' % (class_id, idx, row['id_code']) )

dpi = 80 #inch

path="F:\\kaggleDataSet\\diabeticRetinopathy\\resized train 19\\cd54d022e37d.jpg"

image = load_ben_color(path,sigmaX=10)

height, width = IMG_SIZE, IMG_SIZE

print(height, width)

SCALE=1

figsize = (width / float(dpi))/SCALE, (height / float(dpi))/SCALE

fig = plt.figure(figsize=figsize)

plt.imshow(image, cmap='gray')

%%time

NUM_SAMP=10

fig = plt.figure(figsize=(25, 16))

for jj in range(5):

    for i, (idx, row) in enumerate(df_test.sample(NUM_SAMP,random_state=SEED+jj).iterrows()):

        ax = fig.add_subplot(5, NUM_SAMP, jj * NUM_SAMP + i + 1, xticks=[], yticks=[])

        path="F:\\kaggleDataSet\\diabeticRetinopathy\\resized test 19\\"+str(row['id_code'])+".jpg"

        image = load_ben_color(path,sigmaX=30)

        plt.imshow(image)

        ax.set_title('%d-%s' % (idx, row['id_code']) )

%%time

NUM_SAMP=10

fig = plt.figure(figsize=(25, 16))

for jj in range(5):

    for i, (idx, row) in enumerate(df_test.sample(NUM_SAMP,random_state=SEED+jj).iterrows()):

        ax = fig.add_subplot(5, NUM_SAMP, jj * NUM_SAMP + i + 1, xticks=[], yticks=[])

        path="F:\\kaggleDataSet\\diabeticRetinopathy\\resized test 19\\"+str(row['id_code'])+".jpg"

        image = load_ben_color(path,sigmaX=50)

        plt.imshow(image, cmap='gray')

        ax.set_title('%d-%s' % (idx, row['id_code']) )

df_old = pd.read_csv('F:\\kaggleDataSet\\diabeticRetinopathy\\trainLabels.csv')

df_old.head()

NUM_SAMP=10

fig = plt.figure(figsize=(25, 16))

for class_id in sorted(train_y.unique()):

    for i, (idx, row) in enumerate(df_old.loc[df_old['level'] == class_id].sample(NUM_SAMP, random_state=SEED).iterrows()):

        ax = fig.add_subplot(5, NUM_SAMP, class_id * NUM_SAMP + i + 1, xticks=[], yticks=[])

        path="F:\\kaggleDataSet\\diabeticRetinopathy\\resized_train\\"+row['image']+".jpeg"

        image = load_ben_color(path,sigmaX=30)

        plt.imshow(image)

        ax.set_title('%d-%d-%s' % (class_id, idx, row['image']) )

NUM_SAMP=10

fig = plt.figure(figsize=(25, 16))

for class_id in sorted(train_y.unique()):

    for i, (idx, row) in enumerate(df_old.loc[df_old['level'] == class_id].sample(NUM_SAMP, random_state=SEED).iterrows()):

        ax = fig.add_subplot(5, NUM_SAMP, class_id * NUM_SAMP + i + 1, xticks=[], yticks=[])

        path="F:\\kaggleDataSet\\diabeticRetinopathy\\resized_train\\"+row['image']+".jpeg"

        image = cv2.imread(path)

        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        image = cv2.resize(image, (IMG_SIZE, IMG_SIZE))

        plt.imshow(image, cmap='gray')

        ax.set_title('%d-%d-%s' % (class_id, idx, row['image']) )

dpi = 80 #inch

path=f"F:\\kaggleDataSet\\diabeticRetinopathy\\resized_train\\31590_right.jpeg" # too many vessels?

image = load_ben_color(path,sigmaX=30)

# image = cv2.imread(path)

# image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# image = crop_image1(image)

# image = cv2.resize(image, (IMG_SIZE, IMG_SIZE))

# image=cv2.addWeighted ( image,4, cv2.GaussianBlur( image , (0,0) , IMG_SIZE/10) ,-4 ,128)

height, width = IMG_SIZE, IMG_SIZE

print(height, width)

SCALE=1

figsize = (width / float(dpi))/SCALE, (height / float(dpi))/SCALE

fig = plt.figure(figsize=figsize)

plt.imshow(image, cmap='gray')

dpi = 80 #inch

path_jpg=f"F:\\kaggleDataSet\\diabeticRetinopathy\\resized_train_cropped\\18017_left.jpeg" # too many vessels?

path_png=f"F:\\kaggleDataSet\\diabeticRetinopathy\\rescaled_train_896\\18017_left.png" # details are lost

image = cv2.imread(path_png)

image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

image = cv2.resize(image, (IMG_SIZE, IMG_SIZE))

image2 =  cv2.imread(path_jpg)

image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2RGB)

image2 = cv2.resize(image2, (IMG_SIZE, IMG_SIZE))

height, width = IMG_SIZE, IMG_SIZE

print(height, width)

SCALE=1/4

figsize = (width / float(dpi))/SCALE, (height / float(dpi))/SCALE

fig = plt.figure(figsize=figsize)

ax = fig.add_subplot(2, 2, 1, xticks=[], yticks=[])

ax.set_title('png format original' )

plt.imshow(image, cmap='gray')

ax = fig.add_subplot(2, 2, 2, xticks=[], yticks=[])

ax.set_title('jpg format original' )

plt.imshow(image2, cmap='gray')

image = load_ben_color(path_png,sigmaX=30)

image2 = load_ben_color(path_jpg,sigmaX=30)

ax = fig.add_subplot(2, 2, 3, xticks=[], yticks=[])

ax.set_title('png format transformed' )

plt.imshow(image, cmap='gray')

ax = fig.add_subplot(2, 2, 4, xticks=[], yticks=[])

ax.set_title('jpg format transformed' )

plt.imshow(image2, cmap='gray')

import json

import math

import os

import cv2

from PIL import Image

import numpy as np

from keras import layers

from keras.applications import DenseNet121

from keras.callbacks import Callback, ModelCheckpoint

from keras.preprocessing.image import ImageDataGenerator

from keras.models import Sequential

from keras.optimizers import Adam

import matplotlib.pyplot as plt

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.metrics import cohen_kappa_score, accuracy_score

import scipy

from tqdm import tqdm

%matplotlib inline
train_df = pd.read_csv('F:\\kaggleDataSet\\diabeticRetinopathy\\trainLabels19.csv')

test_df = pd.read_csv('F:\\kaggleDataSet\\diabeticRetinopathy\\testImages19.csv')

print(train_df.shape)

print(test_df.shape)

test_df.head()

def get_pad_width(im, new_shape, is_rgb=True):

    pad_diff = new_shape - im.shape[0], new_shape - im.shape[1]

    t, b = math.floor(pad_diff[0]/2), math.ceil(pad_diff[0]/2)

    l, r = math.floor(pad_diff[1]/2), math.ceil(pad_diff[1]/2)

    if is_rgb:

        pad_width = ((t,b), (l,r), (0, 0))

    else:

        pad_width = ((t,b), (l,r))

    return pad_width

def preprocess_image(image_path, desired_size=224):

    im = Image.open(image_path)

    im = im.resize((desired_size, )*2, resample=Image.LANCZOS)

    return im
N = test_df.shape[0]

x_test = np.empty((N, 224, 224, 3), dtype=np.uint8)

for i, image_id in enumerate(tqdm(test_df['id_code'])):

    x_test[i, :, :, :] = preprocess_image("F:\\kaggleDataSet\\diabeticRetinopathy\\resized test 19\\"+str(image_id)+".jpg")

# model.summary()

def load_image_ben_orig(path,resize=True,crop=False,norm255=True,keras=False):

    image = cv2.imread(path)

    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    image=cv2.addWeighted( image,4, cv2.GaussianBlur( image , (0,0) ,  10) ,-4 ,128)

    if norm255:

        return image/255

    elif keras:

        #see https://github.com/keras-team/keras-applications/blob/master/keras_applications/imagenet_utils.py for mode

        #see https://github.com/keras-team/keras-applications/blob/master/keras_applications/xception.py for inception,xception mode

        #the use of tf based preprocessing (- and / by 127 respectively) will results in [-1,1] so it will not visualize correctly (directly)

        image = np.expand_dims(image, axis=0)

        return preprocess_input(image)[0]

    else:

        return image.astype(np.int16)

    return image

def transform_image_ben(img,resize=True,crop=False,norm255=True,keras=False):

    image=cv2.addWeighted( img,4, cv2.GaussianBlur( img , (0,0) ,  10) ,-4 ,128)

    if norm255:

        return image/255

    elif keras:

        image = np.expand_dims(image, axis=0)

        return preprocess_input(image)[0]

    else:

        return image.astype(np.int16)

    return image
def display_samples(df, columns=5, rows=2, Ben=True):

    fig=plt.figure(figsize=(5*columns, 4*rows))

    for i in range(columns*rows):

        image_path = df.loc[i,'id_code']

        path = f"F:\\kaggleDataSet\\diabeticRetinopathy\\resized test 19\\"+str(image_path)+".jpg"

        if Ben:

            img = load_image_ben_orig(path)

        else:

            img = cv2.imread(path)

            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

        fig.add_subplot(rows, columns, i+1)

        plt.imshow(img)

    plt.tight_layout()

display_samples(test_df, Ben=False)

display_samples(test_df, Ben=True)

from keras import layers

from keras.models import Model

import keras.backend as K
K.clear_session()

densenet = DenseNet121(weights=None,include_top=False,input_shape=(None,None,3))
GAP_layer = layers.GlobalAveragePooling2D()

drop_layer = layers.Dropout(0.5)

dense_layer = layers.Dense(5, activation='sigmoid', name='final_output')
def build_model_sequential():

    model = Sequential()

    model.add(densenet)

    model.add(GAP_layer)

    model.add(drop_layer)

    model.add(dense_layer)

    return model
modelA = build_model_sequential()

modelA.load_weights('F:\\kaggleDataSet\\diabeticRetinopathy\\dense_xhlulu_731.h5')

modelA.summary()
model = build_model_functional() # with pretrained weights, and layers we want

model.summary()

y_test = model.predict(x_test) > 0.5

y_test = y_test.astype(int).sum(axis=1) - 1
import seaborn as sns

import cv2

SIZE=224

def create_pred_hist(pred_level_y,title='NoTitle'):

    results = pd.DataFrame({'diagnosis':pred_level_y})

    f, ax = plt.subplots(figsize=(7, 4))

    ax = sns.countplot(x="diagnosis", data=results, palette="GnBu_d")

    sns.despine()

    plt.title(title)

    plt.show()

create_pred_hist(y_test,title='predicted level distribution in test set')

def gen_heatmap_img(img, model0, layer_name='last_conv_layer',viz_img=None,orig_img=None):

    preds_raw = model0.predict(img[np.newaxis])

    preds = preds_raw > 0.5 # use the same threshold as @xhlulu original kernel

    class_idx = (preds.astype(int).sum(axis=1) - 1)[0]

    class_output_tensor = model0.output[:, class_idx]

    viz_layer = model0.get_layer(layer_name)

    grads = K.gradients(class_output_tensor ,viz_layer.output)[0] # gradients of viz_layer wrt output_tensor of predicted class

    pooled_grads=K.mean(grads,axis=(0,1,2))

    iterate=K.function([model0.input],[pooled_grads, viz_layer.output[0]])

    pooled_grad_value, viz_layer_out_value = iterate([img[np.newaxis]])

    for i in range(pooled_grad_value.shape[0]):

        viz_layer_out_value[:,:,i] *= pooled_grad_value[i]

    heatmap = np.mean(viz_layer_out_value, axis=-1)

    heatmap = np.maximum(heatmap,0)

    heatmap /= np.max(heatmap)

    viz_img=cv2.resize(viz_img,(img.shape[1],img.shape[0]))

    heatmap=cv2.resize(heatmap,(viz_img.shape[1],viz_img.shape[0]))

    heatmap_color = cv2.applyColorMap(np.uint8(heatmap*255), cv2.COLORMAP_SPRING)/255

    heated_img = heatmap_color*0.5 + viz_img*0.5

    print('raw output from model : ')

    print_pred(preds_raw)

    if orig_img is None:

        show_Nimages([img,viz_img,heatmap_color,heated_img])

    else:

        show_Nimages([orig_img,img,viz_img,heatmap_color,heated_img])

    plt.show()

    return heated_img
def show_image(image,figsize=None,title=None):

    if figsize is not None:

        fig = plt.figure(figsize=figsize)

    if image.ndim == 2:

        plt.imshow(image,cmap='gray')

    else:

        plt.imshow(image)

    if title is not None:

        plt.title(title)

def show_Nimages(imgs,scale=1):

    N=len(imgs)

    fig = plt.figure(figsize=(25/scale, 16/scale))

    for i, img in enumerate(imgs):

        ax = fig.add_subplot(1, N, i + 1, xticks=[], yticks=[])

        show_image(img)

def print_pred(array_of_classes):

    xx = array_of_classes

    s1,s2 = xx.shape

    for i in range(s1):

        for j in range(s2):

            print('%.3f ' % xx[i,j],end='')

        print('')
NUM_SAMP=10

SEED=77

layer_name = 'relu' #'conv5_block16_concat'

for i, (idx, row) in enumerate(test_df[:NUM_SAMP].iterrows()):

    path=f"F:\\kaggleDataSet\\diabeticRetinopathy\\resized test 19\\"+str(row["id_code"])+".jpg"

    ben_img = load_image_ben_orig(path)

    input_img = np.empty((1,224, 224, 3), dtype=np.uint8)

    input_img[0,:,:,:] = preprocess_image(path)

    print('test pic no.%d' % (i+1))

    _ = gen_heatmap_img(input_img[0],model, layer_name=layer_name,viz_img=ben_img)

from albumentations import *

import time

IMG_SIZE = (224,224)

'''Use case from https://www.kaggle.com/alexanderliao/image-augmentation-demo-with-albumentation/'''

def albaugment(aug0, img):

    return aug0(image=img)['image']

idx=8

image1=x_test[idx]

'''1. Rotate or Flip'''

aug1 = OneOf([Rotate(p=0.99, limit=160, border_mode=0,value=0), Flip(p=0.5)],p=1)

'''2. Adjust Brightness or Contrast'''

aug2 = RandomBrightnessContrast(brightness_limit=0.45, contrast_limit=0.45,p=1)

h_min=np.round(IMG_SIZE[1]*0.72).astype(int)

h_max= np.round(IMG_SIZE[1]*0.9).astype(int)

print(h_min,h_max)

'''3. Random Crop and then Resize'''

#w2h_ratio = aspect ratio of cropping

aug3 = RandomSizedCrop((h_min, h_max),IMG_SIZE[1],IMG_SIZE[0], w2h_ratio=IMG_SIZE[0]/IMG_SIZE[1],p=1)

'''4. CutOut Augmentation'''

max_hole_size = int(IMG_SIZE[1]/10)

aug4 = Cutout(p=1,max_h_size=max_hole_size,max_w_size=max_hole_size,num_holes=8 )#default num_holes=8

'''5. SunFlare Augmentation'''

aug5 = RandomSunFlare(src_radius=max_hole_size,num_flare_circles_lower=10,num_flare_circles_upper=20,p=1)

'''6. Ultimate Augmentation -- combine everything'''

final_aug = Compose([aug1,aug2,aug3,aug4,aug5],p=1)

img1 = albaugment(aug1,image1)

img2 = albaugment(aug1,image1)

print('Rotate or Flip')

show_Nimages([image1,img1,img2],scale=2)

# time.sleep(1)

img1 = albaugment(aug2,image1)

img2 = albaugment(aug2,image1)

img3 = albaugment(aug2,image1)

print('Brightness or Contrast')

show_Nimages([img3,img1,img2],scale=2)

# time.sleep(1)

img1 = albaugment(aug3,image1)

img2 = albaugment(aug3,image1)

img3 = albaugment(aug3,image1)

print('Rotate and Resize')

show_Nimages([img3,img1,img2],scale=2)

print(img1.shape,img2.shape)

# time.sleep(1)

img1 = albaugment(aug4,image1)

img2 = albaugment(aug4,image1)

img3 = albaugment(aug4,image1)

print('CutOut')

show_Nimages([img3,img1,img2],scale=2)

# time.sleep(1)

img1 = albaugment(aug5,image1)

img2 = albaugment(aug5,image1)

img3 = albaugment(aug5,image1)

print('Sun Flare')

show_Nimages([img3,img1,img2],scale=2)

# time.sleep(1)

img1 = albaugment(final_aug,image1)

img2 = albaugment(final_aug,image1)

img3 = albaugment(final_aug,image1)

print('All above combined')

show_Nimages([img3,img1,img2],scale=2)

print(img1.shape,img2.shape)

aug_list = [aug5, aug2, aug3, aug4, aug1, final_aug]

aug_name = ['SunFlare', 'brightness or contrast', 'crop and resized', 'CutOut', 'rotate or flip', 'Everything Combined']

idx=8

layer_name = 'relu'

for i in range(len(aug_list)):

    path=f"F:\\kaggleDataSet\\diabeticRetinopathy\\resized test 19\\"+str(row["id_code"])+".jpg"

    input_img = np.empty((1,224, 224, 3), dtype=np.uint8)

    input_img[0,:,:,:] = preprocess_image(path)

    aug_img = albaugment(aug_list[i],input_img[0,:,:,:])

    ben_img = transform_image_ben(aug_img)

    print('test pic no.%d -- augmentation: %s' % (i+1, aug_name[i]))

    _ = gen_heatmap_img(aug_img,model, layer_name=layer_name,viz_img=ben_img,orig_img=input_img[0])

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