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.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+font/NanumGothic.ttf filter=lfs diff=lfs merge=lfs -text
+font/NanumGothicBold.ttf filter=lfs diff=lfs merge=lfs -text
+font/NanumGothicExtraBold.ttf filter=lfs diff=lfs merge=lfs -text
+font/NanumGothicLight.ttf filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,28 @@
+
+import gradio as gr
+
+from libs import *
+
+
+with gr.Blocks(title="수업 성적 확인") as demo:
+    gr.Markdown("수업 성적 확인을 위해, 수업과 이름 학번을 입력하세요.")
+
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(type='numpy',label="이미지 입력", image_mode='RGB')
+            
+        with gr.Column():
+            guild_image = gr.Image(os.path.join("Image", "0.jpg"),type='numpy', label="정답 이미지", interactive=False)
+
+    with gr.Row():
+        image_button = gr.Button("이미지 유사도 평가")
+
+    with gr.Row():
+        data_output = gr.Text(label="유사도", interactive=False)
+        
+        
+    
+    image_button.click(check_img,inputs=[input_image, guild_image], outputs = [data_output])
+    
+
+demo.launch()

font/NanumGothic.ttf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:48a28e97b34fc8e5b157657633670cd1b7de126cfc414da65ce9c3d5bc8be733
+size 4691820

font/NanumGothicBold.ttf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:454eb3f503f377782a99eb84fc4bb7dde22a0f075d41b47427737f2985053bc9
+size 4642244

font/NanumGothicExtraBold.ttf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e0288775c8f021f327fd77f9eb251eea80e4601bbbb068478c7878caa23e8f92
+size 4526920

font/NanumGothicLight.ttf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:da39544c39be58124a4fcb296eafc2bf73530b3dbd1b19aa802c5c3fd227e31c
+size 1523188

libs/__init__.py

@@ -0,0 +1,2 @@
+
+from .utils import *

libs/utils.py

@@ -0,0 +1,57 @@
+import os
+import cv2
+import lpips
+import torch 
+import numpy as np
+import gradio as gr
+
+
+
+SPATIAL = True   
+ALEXT = lpips.LPIPS(net='alex', spatial=SPATIAL)
+VGG   = lpips.LPIPS(net='vgg',  spatial=SPATIAL)
+
+def normalize(img, factor=255./2., cent= 1.):
+    r"""
+    Image Normalizagion
+    [H, W, C], scale : [0 - 1]
+    """
+    img = (img / factor - cent)
+    return img
+
+
+def resize(img, resize=256):
+    r"""
+    Image Resize using cv2
+    """
+    return cv2.resize(img, (resize, resize))
+
+def to_tensor(img):
+    r"""
+    make numpy to tensor
+    """
+    img = torch.Tensor(img[..., np.newaxis].transpose((3, 2, 0, 1)))
+    return img
+
+
+
+
+def img_preprocessing(img):
+    img = resize(img)
+    img = normalize(img)
+    img = to_tensor(img)
+    return img
+
+def check_img(input_img, gt_img):
+
+    # Preprocessing
+    input_img = img_preprocessing(input_img)
+    gt_img    = img_preprocessing(gt_img)
+   
+    # predict similarytiy
+    similarity = ALEXT.forward(gt_img, input_img)
+
+    if SPATIAL:    
+        return f"{(1 - similarity.mean().item()) *100:.4f}%"
+    else:
+        return f"{(1 - similarity.item()) *100:.4f}%"

requirements.txt

@@ -0,0 +1,4 @@
+lpips
+torch
+opencv-python
+matplotlib