From cd6677732568a84e820a58f6aaa2dc10820263f3 Mon Sep 17 00:00:00 2001 From: CCl14 <128645571+CCl14@users.noreply.github.com> Date: Fri, 1 Mar 2024 10:12:39 +0800 Subject: [PATCH 1/3] Add files via upload --- data.py | 14 +++++++++++--- main.py | 7 ++++--- model.py | 2 +- 3 files changed, 16 insertions(+), 7 deletions(-) diff --git a/data.py b/data.py index 956497d3f..6e82dddc7 100644 --- a/data.py +++ b/data.py @@ -5,6 +5,7 @@ import glob import skimage.io as io import skimage.transform as trans +import cv2 Sky = [128,128,128] Building = [128,0,0] @@ -119,6 +120,13 @@ def labelVisualize(num_class,color_dict,img): def saveResult(save_path,npyfile,flag_multi_class = False,num_class = 2): - for i,item in enumerate(npyfile): - img = labelVisualize(num_class,COLOR_DICT,item) if flag_multi_class else item[:,:,0] - io.imsave(os.path.join(save_path,"%d_predict.png"%i),img) \ No newline at end of file + for i, item in enumerate(npyfile): + img = labelVisualize(num_class, COLOR_DICT, item) if flag_multi_class else item[:, :, 0] + #print(img) ## ex. [[0.16509348 0.08227982 ... 0.07113015] + img = (img > 0.5).astype(np.uint8) # .reshape(256, 256) + #print(img) ## ex. [[ 0 0 0 0 ... 1 1 1 ]] + img = (img * 255) + #print(img) ## ex. [[0 0 00 ... 255 255 255]] + # img = labelVisualize(num_class,COLOR_DICT,item) if flag_multi_class else item[:,:,0] + # io.imsave(os.path.join(save_path,"%d_predict.png"%i),img) + cv2.imwrite(os.path.join(save_path, "%d_predict.png" % i), img) \ No newline at end of file diff --git a/main.py b/main.py index f6041b875..f599eede5 100644 --- a/main.py +++ b/main.py @@ -1,5 +1,7 @@ from model import * from data import * +import warnings +warnings.filterwarnings('ignore') #os.environ["CUDA_VISIBLE_DEVICES"] = "0" @@ -15,8 +17,7 @@ model = unet() model_checkpoint = ModelCheckpoint('unet_membrane.hdf5', monitor='loss',verbose=1, save_best_only=True) -model.fit_generator(myGene,steps_per_epoch=300,epochs=1,callbacks=[model_checkpoint]) - +model.fit_generator(myGene,steps_per_epoch=300,epochs=5,callbacks=[model_checkpoint]) testGene = testGenerator("data/membrane/test") results = model.predict_generator(testGene,30,verbose=1) -saveResult("data/membrane/test",results) \ No newline at end of file +saveResult("data/membrane/result",results) \ No newline at end of file diff --git a/model.py b/model.py index 7df5b6889..6c2365158 100644 --- a/model.py +++ b/model.py @@ -52,7 +52,7 @@ def unet(pretrained_weights = None,input_size = (256,256,1)): conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9) conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9) - model = Model(input = inputs, output = conv10) + model = Model(inputs,conv10) model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy']) From 549899c9b35f2ae0f1101d5d535aa0515abcb29a Mon Sep 17 00:00:00 2001 From: CCl14 <128645571+CCl14@users.noreply.github.com> Date: Mon, 4 Mar 2024 13:12:49 +0800 Subject: [PATCH 2/3] README.md --- README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/README.md b/README.md index b74c98e8a..a1b9f5b61 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,11 @@ The architecture was inspired by [U-Net: Convolutional Networks for Biomedical I --- +Lab work + +Modified from zhixuhao version, the current version can be directly deployed locally, debugs the network, etc.... + + ## Overview ### Data From afecdef6813bd9c4fbadaf55de1b09b5560a65f7 Mon Sep 17 00:00:00 2001 From: CCl14 <128645571+CCl14@users.noreply.github.com> Date: Mon, 4 Mar 2024 13:20:02 +0800 Subject: [PATCH 3/3] Add files via upload --- For Comparison Use.pdf | Bin 0 -> 1182705 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 For Comparison Use.pdf diff --git a/For Comparison Use.pdf b/For Comparison Use.pdf new file mode 100644 index 0000000000000000000000000000000000000000..b8be3fd91ed20ddcbf78800b03e50f07500336ca GIT binary patch literal 1182705 zcmeFX1DEA&yDnO`ZQHhO+qUg4blEn$Y}@QE+qP|x^%oKXCRONk;PE=5^h9 zW)e~b5iwduIyP9+-My(fSSWUO0tNzmBP&=Q9(ox|J5xg^OD|Is0!Dfn0!Bs-CUyo^ zdU*nNPI^%SW@a`f4kmgz0#MTcfbOjjOGl^S4L^CsPwkV;6fT0!Ai2K6)`r8y8b2dNCVA 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