added sequential processing

app.py

@@ -104,8 +104,7 @@ def find_largest_face(faces):
     return largest_face
 
 
-def inference(img, model_name):
-    confidences = {}
+def inference(img):
     grey = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
     faces = faceClassifier.detectMultiScale(
         grey, scaleFactor=1.1, minNeighbors=4)
@@ -118,60 +117,71 @@ def inference(img, model_name):
         faceRegion = tfms(faceRegion)
         faceRegion = faceRegion.unsqueeze(0)
 
-        if model_name == 'DeePixBiS':
-            mask, binary = deepix_model.forward(faceRegion)
-            res = torch.mean(mask).item()
-            cls = 'Real' if res >= pix_threshhold else 'Spoof'
-            res = 1 - res
-
-        else:
-            dense_flag = True
-            boxes = list(face)
-            boxes.append(1)
-            param_lst, roi_box_lst = tddfa(img, [boxes])
+        # if model_name == 'DeePixBiS':
+        mask, binary = deepix_model.forward(faceRegion)
+        res_deepix = torch.mean(mask).item()
+        cls_deepix = 'Real' if res_deepix >= pix_threshhold else 'Spoof'
+        
+        label_deepix = f'{cls_deepix} {res_deepix:.2f}'
+        confidences_deepix = {label_deepix: res_deepix}
+        color_deepix = (0, 255, 0) if cls_deepix == 'Real' else (255, 0, 0)
+        img_deepix = cv.rectangle(img.copy(), (x, y), (x + w, y + h), color_deepix, 2)
+        cv.putText(img_deepix, label_deepix, (x, y + h + 30),
+                    cv.FONT_HERSHEY_COMPLEX, 1, color_deepix)
+
+        # else:
+        dense_flag = True
+        boxes = list(face)
+        boxes.append(1)
+        param_lst, roi_box_lst = tddfa(img, [boxes])
+        
+        ver_lst = tddfa.recon_vers(param_lst, roi_box_lst, dense_flag=dense_flag)
+        depth_img = depth(img, ver_lst, tddfa.tri, with_bg_flag=False)
+        with torch.no_grad():
+            map_score_list = []
+            image_x, map_x = prepare_data([img], [list(face)], [depth_img])
+            # get the inputs
+            image_x = image_x.unsqueeze(0)
+            map_x = map_x.unsqueeze(0)
+            inputs = image_x.to(device)
+            test_maps = map_x.to(device)
+            optimizer.zero_grad()
             
-            ver_lst = tddfa.recon_vers(param_lst, roi_box_lst, dense_flag=dense_flag)
-            depth_img = depth(img, ver_lst, tddfa.tri, with_bg_flag=False)
-            with torch.no_grad():
-                map_score_list = []
-                image_x, map_x = prepare_data([img], [list(face)], [depth_img])
-                # get the inputs
-                image_x = image_x.unsqueeze(0)
-                map_x = map_x.unsqueeze(0)
-                inputs = image_x.to(device)
-                test_maps = map_x.to(device)
-                optimizer.zero_grad()
-                
-                map_score = 0.0
-                for frame_t in range(inputs.shape[1]):
-                    mu, logvar, map_x, x_concat, x_Block1, x_Block2, x_Block3, x_input = cdcn_model(inputs[:, frame_t, :, :, :])
-
-                    score_norm = torch.sum(mu) / torch.sum(test_maps[:, frame_t, :, :])
-                    map_score += score_norm
-                map_score = map_score / inputs.shape[1]
-                map_score_list.append(map_score)
-
-            res = map_score_list[0].item()
-            if res > 10:
-                res = 0.0
-            cls = 'Real' if res >= dsdg_threshold else 'Spoof'
-            res = res * 100
-
-        label = f'{cls} {res:.2f}'
-        confidences = {label: res}
-        color = color = (0, 255, 0) if cls == 'Real' else (255, 0, 0)
-        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)
-
-    return img, confidences
+            map_score = 0.0
+            for frame_t in range(inputs.shape[1]):
+                mu, logvar, map_x, x_concat, x_Block1, x_Block2, x_Block3, x_input = cdcn_model(inputs[:, frame_t, :, :, :])
+
+                score_norm = torch.sum(mu) / torch.sum(test_maps[:, frame_t, :, :])
+                map_score += score_norm
+            map_score = map_score / inputs.shape[1]
+            map_score_list.append(map_score)
+
+        res_dsdg = map_score_list[0].item()
+        if res_dsdg > 10:
+            res_dsdg = 0.0
+        cls_dsdg = 'Real' if res_dsdg >= dsdg_threshold else 'Spoof'
+        res_dsdg = res_dsdg * 100
+
+        label_dsdg = f'{cls_dsdg} {res_dsdg:.2f}'
+        confidences_dsdg = {label_dsdg: res_deepix}
+        color_dsdg = (0, 255, 0) if cls_dsdg == 'Real' else (255, 0, 0)
+        img_dsdg = cv.rectangle(img.copy(), (x, y), (x + w, y + h), color_dsdg, 2)
+        cv.putText(img_dsdg, label_dsdg, (x, y + h + 30),
+                    cv.FONT_HERSHEY_COMPLEX, 1, color_dsdg)
+
+        return img_deepix, confidences_deepix, img_dsdg, confidences_dsdg
+    else:
+        return img, {}, img, {}
 
 
 if __name__ == '__main__':
     demo = gr.Interface(
         fn=inference,
-        inputs=[gr.Image(source='webcam', shape=None, type='numpy'),
-                Dropdown(["DeePixBiS", "DSDG"], value="DeePixBiS")],
-        outputs=["image", gr.Label(num_top_classes=2)],
+        inputs=[gr.Image(source='webcam', shape=None, type='numpy')],
+        outputs=[
+            gr.outputs.Image(label='DeePixBiS'),
+            gr.Label(num_top_classes=2, label='DeePixBiS'),
+            gr.outputs.Image(label='DSDG'),
+            gr.Label(num_top_classes=2, label='DSDG')],
         examples=examples).queue(concurrency_count=2)
     demo.launch(share=False)