Create app.py

app.py

@@ -0,0 +1,53 @@
+from train import AnimeSegmentation
+import cv2
+import numpy as np
+from loadimg import load_img
+import gradio as gr
+
+
+model = AnimeSegmentation.from_pretrained("skytnt/anime-seg") 
+
+device = "cuda"
+model.eval()
+model.to(device)
+img_size = model._hub_mixin_config["img_size"]
+
+
+def get_mask(model, input_img, use_amp=True, s=640):
+    input_img = (input_img / 255).astype(np.float32)
+    h, w = h0, w0 = input_img.shape[:-1]
+    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
+    ph, pw = s - h, s - w
+    img_input = np.zeros([s, s, 3], dtype=np.float32)
+    img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(input_img, (w, h))
+    img_input = np.transpose(img_input, (2, 0, 1))
+    img_input = img_input[np.newaxis, :]
+    tmpImg = torch.from_numpy(img_input).type(torch.FloatTensor).to(model.device)
+    with torch.no_grad():
+        if use_amp:
+            with amp.autocast():
+                pred = model(tmpImg)
+            pred = pred.to(dtype=torch.float32)
+        else:
+            pred = model(tmpImg)
+        pred = pred.cpu().numpy()[0]
+        pred = np.transpose(pred, (1, 2, 0))
+        pred = pred[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
+        pred = cv2.resize(pred, (w0, h0))[:, :, np.newaxis]
+        return pred
+
+
+def process(img):
+    path = load_img(img,output_type="str")
+    img = cv2.cvtColor(cv2.imread(path, cv2.IMREAD_COLOR), cv2.COLOR_BGR2RGB)
+    mask = get_mask(HF_model, img, use_amp= False, s=img_size)
+    img = mask * img
+    out = load_img(img)
+    return out 
+
+
+    
+    
+demo = gr.Interface(process,"image","image")
+
+demo.launch(debug=True)