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app.py

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+import gradio as gr
+import albumentations as albu
+from pylab import imshow
+import numpy as np
+import cv2
+import torch
+import albumentations as albu
+from iglovikov_helper_functions.utils.image_utils import load_rgb, pad, unpad
+from iglovikov_helper_functions.dl.pytorch.utils import tensor_from_rgb_image
+from collections import namedtuple
+from tempfile import NamedTemporaryFile
+import os
+from people_segmentation.pre_trained_models import create_model
+model = create_model("Unet_2020-07-20")
+model.eval()
+# Define model
+import matplotlib.pyplot as plt
+from pylab import imshow
+
+
+def segment_people(image):
+    transform = albu.Compose([albu.Normalize(p=1)], p=1)
+    padded_image, pads = pad(image, factor=32, border=cv2.BORDER_CONSTANT)
+    x = transform(image=padded_image)["image"]
+    x = torch.unsqueeze(tensor_from_rgb_image(x), 0)
+    with torch.no_grad():
+        prediction = model(x)[0][0]
+
+    mask = (prediction > 0).cpu().numpy().astype(np.uint8)
+    mask = unpad(mask, pads)
+    dst = cv2.addWeighted(image, 1, (cv2.cvtColor(mask, cv2.COLOR_GRAY2RGB) * (0, 255, 0)).astype(np.uint8), 0.5, 0)
+    
+    return dst
+
+
+# Create Gradio app
+def gradio_segmentation(image_path):
+   
+    image = load_rgb(image_path) 
+    mask = segment_people(image)
+    return mask
+
+examples = [
+    [ "76.jpg"],
+     [ "69.jpg"],
+      [ "80.jpg"]
+]
+
+description = """
+# People Segmentation
+This application segments people from the input image. Upload an image to see the segmented output.
+"""
+
+gr.Interface(
+    fn=gradio_segmentation,
+    inputs=gr.Image(type="filepath"),
+    outputs=gr.Image(type="numpy"),
+    examples=examples,
+    title="People Segmentation",
+    description=description,
+    ).launch()

requirements.txt

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+people_segmentation
+albumentations
+iglovikov_helper_functions
+gradio