Update app.py

app.py

@@ -9,130 +9,126 @@ from torch.autograd import Variable
 from PIL import Image
 
 
-def build_model(hypar, device):
-    net = hypar["model"]  # GOSNETINC(3,1)
-
-    # convert to half precision
-    if hypar["model_digit"] == "half":
-        net.half()
-        for layer in net.modules():
-            if isinstance(layer, nn.BatchNorm2d):
-                layer.float()
-
-    net.to(device)
-
-    if hypar["restore_model"] != "":
-        net.load_state_dict(
-            torch.load(
-                hypar["model_path"] + "/" + hypar["restore_model"],
-                map_location=device,
-            )
-        )
-        net.to(device)
-    net.eval()
-    return net
-
-
-if not os.path.exists("saved_models"):
-    os.mkdir("saved_models")
-    os.mkdir("git")
-    os.system("git clone https://github.com/xuebinqin/DIS git/xuebinqin/DIS")
-    hf_hub_download(
-        repo_id="NimaBoscarino/IS-Net_DIS-general-use",
-        filename="isnet-general-use.pth",
-        local_dir="saved_models",
-    )
-    os.system("rm -r git/xuebinqin/DIS/IS-Net/__pycache__")
-    os.system("mv git/xuebinqin/DIS/IS-Net/* .")
-
-import data_loader_cache
-import models
-
-device = "cpu"
-ISNetDIS = models.ISNetDIS
-normalize = data_loader_cache.normalize
-im_preprocess = data_loader_cache.im_preprocess
-
-# Set Parameters
-hypar = {}  # paramters for inferencing
-
-# load trained weights from this path
-hypar["model_path"] = "./saved_models"
-# name of the to-be-loaded weights
-hypar["restore_model"] = "isnet-general-use.pth"
-# indicate if activate intermediate feature supervision
-hypar["interm_sup"] = False
-
-# choose floating point accuracy --
-# indicates "half" or "full" accuracy of float number
-hypar["model_digit"] = "full"
-hypar["seed"] = 0
-
-# cached input spatial resolution, can be configured into different size
-hypar["cache_size"] = [1024, 1024]
-
-# data augmentation parameters ---
-# mdoel input spatial size, usually use the same value hypar["cache_size"], which means we don't further resize the images
-hypar["input_size"] = [1024, 1024]
-# random crop size from the input, it is usually set as smaller than hypar["cache_size"], e.g., [920,920] for data augmentation
-hypar["crop_size"] = [1024, 1024]
-
-hypar["model"] = ISNetDIS()
-
-# Build Model
-net = build_model(hypar, device)
-
-
-def predict(net, inputs_val, shapes_val, hypar, device):
-    """
-    Given an Image, predict the mask
-    """
-    net.eval()
-
-    if hypar["model_digit"] == "full":
-        inputs_val = inputs_val.type(torch.FloatTensor)
-    else:
-        inputs_val = inputs_val.type(torch.HalfTensor)
-
-    inputs_val_v = Variable(inputs_val, requires_grad=False).to(
-        device
-    )  # wrap inputs in Variable
-
-    ds_val = net(inputs_val_v)[0]  # list of 6 results
-
-    # B x 1 x H x W    # we want the first one which is the most accurate prediction
-    pred_val = ds_val[0][0, :, :, :]
-
-    # recover the prediction spatial size to the orignal image size
-    pred_val = torch.squeeze(
-        F.upsample(
-            torch.unsqueeze(pred_val, 0),
-            (shapes_val[0][0], shapes_val[0][1]),
-            mode="bilinear",
+def removeBackground(image):
+    if not os.path.exists("saved_models"):
+        os.mkdir("saved_models")
+        os.mkdir("git")
+        os.system("git clone https://github.com/xuebinqin/DIS git/xuebinqin/DIS")
+        hf_hub_download(
+            repo_id="NimaBoscarino/IS-Net_DIS-general-use",
+            filename="isnet-general-use.pth",
+            local_dir="saved_models",
         )
-    )
+        os.system("rm -r git/xuebinqin/DIS/IS-Net/__pycache__")
+        os.system("mv git/xuebinqin/DIS/IS-Net/* .")
 
-    ma = torch.max(pred_val)
-    mi = torch.min(pred_val)
-    pred_val = (pred_val - mi) / (ma - mi)  # max = 1
+    def build_model(hypar, device):
+        net = hypar["model"]  # GOSNETINC(3,1)
 
-    if device == "cpu":
-        torch.cpu.empty_cache()
-    # it is the mask we need
-    return (pred_val.detach().cpu().numpy() * 255).astype(np.uint8)
+        # convert to half precision
+        if hypar["model_digit"] == "half":
+            net.half()
+            for layer in net.modules():
+                if isinstance(layer, nn.BatchNorm2d):
+                    layer.float()
 
+        net.to(device)
 
-def load_image(im_pil, hypar):
-    im = np.array(im_pil)
-    im, im_shp = im_preprocess(im, hypar["cache_size"])
-    im = torch.divide(im, 255.0)
-    shape = torch.from_numpy(np.array(im_shp))
-    # make a batch of image, shape
-    aa = normalize(im, [0.5, 0.5, 0.5], [1.0, 1.0, 1.0])
-    return aa.unsqueeze(0), shape.unsqueeze(0)
+        if hypar["restore_model"] != "":
+            net.load_state_dict(
+                torch.load(
+                    hypar["model_path"] + "/" + hypar["restore_model"],
+                    map_location=device,
+                )
+            )
+            net.to(device)
+        net.eval()
+        return net
+
+    import data_loader_cache
+    import models
+
+    device = "cpu"
+    ISNetDIS = models.ISNetDIS
+    normalize = data_loader_cache.normalize
+    im_preprocess = data_loader_cache.im_preprocess
+
+    # Set Parameters
+    hypar = {}  # paramters for inferencing
+
+    # load trained weights from this path
+    hypar["model_path"] = "./saved_models"
+    # name of the to-be-loaded weights
+    hypar["restore_model"] = "isnet-general-use.pth"
+    # indicate if activate intermediate feature supervision
+    hypar["interm_sup"] = False
+
+    # choose floating point accuracy --
+    # indicates "half" or "full" accuracy of float number
+    hypar["model_digit"] = "full"
+    hypar["seed"] = 0
+
+    # cached input spatial resolution, can be configured into different size
+    hypar["cache_size"] = [1024, 1024]
+
+    # data augmentation parameters ---
+    # mdoel input spatial size, usually use the same value hypar["cache_size"], which means we don't further resize the images
+    hypar["input_size"] = [1024, 1024]
+    # random crop size from the input, it is usually set as smaller than hypar["cache_size"], e.g., [920,920] for data augmentation
+    hypar["crop_size"] = [1024, 1024]
+
+    hypar["model"] = ISNetDIS()
+
+    # Build Model
+    net = build_model(hypar, device)
+
+    def predict(net, inputs_val, shapes_val, hypar, device):
+        """
+        Given an Image, predict the mask
+        """
+        net.eval()
+
+        if hypar["model_digit"] == "full":
+            inputs_val = inputs_val.type(torch.FloatTensor)
+        else:
+            inputs_val = inputs_val.type(torch.HalfTensor)
+
+        inputs_val_v = Variable(inputs_val, requires_grad=False).to(
+            device
+        )  # wrap inputs in Variable
+
+        ds_val = net(inputs_val_v)[0]  # list of 6 results
+
+        # B x 1 x H x W    # we want the first one which is the most accurate prediction
+        pred_val = ds_val[0][0, :, :, :]
+
+        # recover the prediction spatial size to the orignal image size
+        pred_val = torch.squeeze(
+            F.upsample(
+                torch.unsqueeze(pred_val, 0),
+                (shapes_val[0][0], shapes_val[0][1]),
+                mode="bilinear",
+            )
+        )
 
+        ma = torch.max(pred_val)
+        mi = torch.min(pred_val)
+        pred_val = (pred_val - mi) / (ma - mi)  # max = 1
+
+        if device == "cuda":
+            torch.cuda.empty_cache()
+        # it is the mask we need
+        return (pred_val.detach().cpu().numpy() * 255).astype(np.uint8)
+
+    def load_image(im_pil, hypar):
+        im = np.array(im_pil)
+        im, im_shp = im_preprocess(im, hypar["cache_size"])
+        im = torch.divide(im, 255.0)
+        shape = torch.from_numpy(np.array(im_shp))
+        # make a batch of image, shape
+        aa = normalize(im, [0.5, 0.5, 0.5], [1.0, 1.0, 1.0])
+        return aa.unsqueeze(0), shape.unsqueeze(0)
 
-def remove_background(image):
     image_tensor, orig_size = load_image(image, hypar)
     mask = predict(net, image_tensor, orig_size, hypar, "cpu")
 
@@ -141,8 +137,12 @@ def remove_background(image):
 
     cropped = im_rgb.copy()
     cropped.putalpha(mask)
+
     return cropped
 
+def remove_background(image):
+    return removeBackground(image)
+
 
 inputs = gr.inputs.Image()
 outputs = gr.outputs.Image(type="pil")