# 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])
原文地址:https://www.cnblogs.com/tszr/p/11238752.html
时间: 2024-10-07 22:13:32