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.gitattributes

@@ -33,3 +33,6 @@ 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
+examples/input/1.jpeg filter=lfs diff=lfs merge=lfs -text
+examples/input/2.jpeg filter=lfs diff=lfs merge=lfs -text
+examples/input/3.jpeg filter=lfs diff=lfs merge=lfs -text

main.py

@@ -0,0 +1,162 @@
+import gradio as gr
+import os
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from huggingface_hub import hf_hub_download
+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",
+        )
+    )
+
+    ma = torch.max(pred_val)
+    mi = torch.min(pred_val)
+    pred_val = (pred_val - mi) / (ma - mi)  # max = 1
+
+    if device == "cpu":
+        torch.cpu.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")
+
+    mask = Image.fromarray(mask).convert("L")
+    im_rgb = image.convert("RGB")
+
+    cropped = im_rgb.copy()
+    cropped.putalpha(mask)
+    return cropped
+
+
+inputs = gr.inputs.Image()
+outputs = gr.outputs.Image(type="pil")
+interface = gr.Interface(
+    fn=remove_background,
+    inputs=inputs,
+    outputs=outputs,
+    title="Remove Background",
+    description="This App removes the background from an image",
+    examples=[
+        "examples/input/1.jpeg",
+        "examples/input/2.jpeg",
+        "examples/input/3.jpeg",
+    ],
+    cache_examples=True,
+)
+interface.launch(enable_queue=True)

requirements.txt

@@ -0,0 +1,5 @@
+gradio==3.14.0
+Pillow
+huggingface-hub
+torch
+numpy