added dropdown, made subdir

DeePixBiS.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ec25c8b21634d42ae47f030f16a3d1501111ad0b24abeec0a01ce2d36b25dbee
-size 12968981

Test.py → DeePixBiS/Test.py

@@ -3,9 +3,9 @@ import torch
 import torch.nn as nn
 from torchvision import transforms
 import numpy as np
-from Model import DeePixBiS
-from Loss import PixWiseBCELoss
-from Metrics import predict, test_accuracy, test_loss
+from DeePixBis.Model import DeePixBiS
+from DeePixBis.Loss import PixWiseBCELoss
+from DeePixBis.Metrics import predict, test_accuracy, test_loss
 
 model = DeePixBiS(pretrained=False)
 model.load_state_dict(torch.load('./DeePixBiS.pth'))

Train.py → DeePixBiS/Train.py

@@ -3,11 +3,11 @@ import torch.nn as nn
 from torchvision.transforms import Compose, ToTensor, RandomHorizontalFlip, Normalize, Resize, RandomRotation
 import numpy as np
 from torch.utils.data import DataLoader
-from Dataset import PixWiseDataset
-from Model import DeePixBiS
-from Loss import PixWiseBCELoss
-from Metrics import predict, test_accuracy, test_loss
-from Trainer import Trainer
+from DeePixBis.Dataset import PixWiseDataset
+from DeePixBis.Model import DeePixBiS
+from DeePixBis.Loss import PixWiseBCELoss
+from DeePixBis.Metrics import predict, test_accuracy, test_loss
+from DeePixBis.Trainer import Trainer
 
 model = DeePixBiS()
 model.load_state_dict(torch.load('./DeePixBiS.pth'))

Trainer.py → DeePixBiS/Trainer.py

@@ -1,6 +1,6 @@
 import torch
 import torch.nn as nn
-from Metrics import test_accuracy, test_loss
+from DeePixBis.Metrics import test_accuracy, test_loss
 
 
 class Trainer():

app.py

@@ -1,17 +1,20 @@
 import gradio as gr
+from gradio.components import Dropdown
 import cv2 as cv
 import torch
 from torchvision import transforms
-from Model import DeePixBiS
+from DeePixBiS.Model import DeePixBiS
 
 
 labels = ['Live', 'Spoof']
 thresh = 0.45
 examples = [
-    'examples/1_1_21_2_33_scene_fake.jpg', 'examples/frame150_real.jpg',
-    'examples/1_2.avi_125_real.jpg', 'examples/1_3.avi_25_fake.jpg']
+    ['examples/1_1_21_2_33_scene_fake.jpg', "DeePixBiS"],
+    ['examples/frame150_real.jpg', "DeePixBiS"],
+    ['examples/1_2.avi_125_real.jpg', "DeePixBiS"],
+    ['examples/1_3.avi_25_fake.jpg', "DeePixBiS"]]
 device = torch.device("cpu")
-faceClassifier = cv.CascadeClassifier('Classifiers/haarface.xml')
+faceClassifier = cv.CascadeClassifier('./DeePixBiS/Classifiers/haarface.xml')
 tfms = transforms.Compose([
     transforms.ToPILImage(),
     transforms.Resize((224, 224)),
@@ -19,7 +22,7 @@ tfms = transforms.Compose([
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
 ])
 model = DeePixBiS(pretrained=False)
-model.load_state_dict(torch.load('./DeePixBiS.pth'))
+model.load_state_dict(torch.load('./DeePixBiS/DeePixBiS.pth'))
 model.eval()
 
 
@@ -35,39 +38,42 @@ def find_largest_face(faces):
     return largest_face
 
 
-def inference(img):
-    grey = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
-    faces = faceClassifier.detectMultiScale(
-        grey, scaleFactor=1.1, minNeighbors=4)
-    face = find_largest_face(faces)
+def inference(img, model_name):
     confidences = {}
-    if face is not None:
-        x, y, w, h = face
-        faceRegion = img[y:y + h, x:x + w]
-        faceRegion = cv.cvtColor(faceRegion, cv.COLOR_BGR2RGB)
-        faceRegion = tfms(faceRegion)
-        faceRegion = faceRegion.unsqueeze(0)
-        mask, binary = model.forward(faceRegion)
-        res = torch.mean(mask).item()
-        if res < thresh:
-            cls = 'Spoof'
-            color = (0, 0, 255)
-            res = 1 - res
-        else:
-            cls = 'Real'
-            color = (0, 255, 0)
-        label = f'{cls} {res:.2f}'
-        cv.rectangle(img, (x, y), (x + w, y + h), color, 2)
-        cv.putText(img, label, (x, y + h + 30),
-                   cv.FONT_HERSHEY_COMPLEX, 1, color)
-        confidences = {label: res}
+    if model_name == 'DeePixBiS':
+        grey = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+        faces = faceClassifier.detectMultiScale(
+            grey, scaleFactor=1.1, minNeighbors=4)
+        face = find_largest_face(faces)
+        
+        if face is not None:
+            x, y, w, h = face
+            faceRegion = img[y:y + h, x:x + w]
+            faceRegion = cv.cvtColor(faceRegion, cv.COLOR_BGR2RGB)
+            faceRegion = tfms(faceRegion)
+            faceRegion = faceRegion.unsqueeze(0)
+            mask, binary = model.forward(faceRegion)
+            res = torch.mean(mask).item()
+            if res < thresh:
+                cls = 'Spoof'
+                color = (0, 0, 255)
+                res = 1 - res
+            else:
+                cls = 'Real'
+                color = (0, 255, 0)
+            label = f'{cls} {res:.2f}'
+            cv.rectangle(img, (x, y), (x + w, y + h), color, 2)
+            cv.putText(img, label, (x, y + h + 30),
+                    cv.FONT_HERSHEY_COMPLEX, 1, color)
+            confidences = {label: res}
     return img, confidences
 
 
 if __name__ == '__main__':
     demo = gr.Interface(
         fn=inference,
-        inputs=[gr.Image(source='webcam', shape=None, type='numpy')],
+        inputs=[gr.Image(source='webcam', shape=None, type='numpy'),
+                Dropdown(["DeePixBiS", "DSDG"], value="DeePixBiS")],
         outputs=["image", gr.Label(num_top_classes=2)],
         examples=examples).queue(concurrency_count=2)
     demo.launch(share=False)

data/Test

@@ -1 +0,0 @@
-<<Test>>