Duplicate from keras-io/Human-Part-Segmentation

Co-authored-by: Satpal Singh Rathore <[email protected]>

.gitattributes

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README.md

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+---
+title: Human Part Segmentation
+emoji: 👤
+colorFrom: gray
+colorTo: purple
+sdk: gradio
+app_file: app.py
+pinned: false
+tags:
+- computer-vision
+- image-segmentation
+license: cc0-1.0
+duplicated_from: keras-io/Human-Part-Segmentation
+---
+
+# Configuration
+
+`title`: _string_
+Display title for the Space
+
+`emoji`: _string_
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_
+Can be either `gradio`, `streamlit`, or `static`
+
+`sdk_version` : _string_
+Only applicable for `streamlit` SDK.  
+See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
+
+`app_file`: _string_
+Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
+Path is relative to the root of the repository.
+
+`models`: _List[string]_
+HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
+Will be parsed automatically from your code if not specified here.
+
+`datasets`: _List[string]_
+HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
+Will be parsed automatically from your code if not specified here.
+
+`pinned`: _boolean_
+Whether the Space stays on top of your list.

app.py

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+import numpy as np
+import tensorflow as tf
+import gradio as gr
+from huggingface_hub import from_pretrained_keras
+import cv2
+
+model = from_pretrained_keras("keras-io/deeplabv3p-resnet50")
+
+colormap = np.array([[0,0,0], [31,119,180], [44,160,44], [44, 127, 125], [52, 225, 143],
+                    [217, 222, 163], [254, 128, 37], [130, 162, 128], [121, 7, 166], [136, 183, 248],
+                    [85, 1, 76], [22, 23, 62], [159, 50, 15], [101, 93, 152], [252, 229, 92],
+                    [167, 173, 17], [218, 252, 252], [238, 126, 197], [116, 157, 140], [214, 220, 252]], dtype=np.uint8)
+                    
+img_size = 512
+                    
+def read_image(image):
+    image = tf.convert_to_tensor(image)
+    image.set_shape([None, None, 3])
+    image = tf.image.resize(images=image, size=[img_size, img_size])
+    image = image / 127.5 - 1
+    return image
+
+def infer(model, image_tensor):
+    predictions = model.predict(np.expand_dims((image_tensor), axis=0))
+    predictions = np.squeeze(predictions)
+    predictions = np.argmax(predictions, axis=2)
+    return predictions
+
+def decode_segmentation_masks(mask, colormap, n_classes):
+    r = np.zeros_like(mask).astype(np.uint8)
+    g = np.zeros_like(mask).astype(np.uint8)
+    b = np.zeros_like(mask).astype(np.uint8)
+    for l in range(0, n_classes):
+        idx = mask == l
+        r[idx] = colormap[l, 0]
+        g[idx] = colormap[l, 1]
+        b[idx] = colormap[l, 2]
+    rgb = np.stack([r, g, b], axis=2)
+    return rgb
+
+def get_overlay(image, colored_mask):
+    image = tf.keras.preprocessing.image.array_to_img(image)
+    image = np.array(image).astype(np.uint8)
+    overlay = cv2.addWeighted(image, 0.35, colored_mask, 0.65, 0)
+    return overlay
+
+def segmentation(input_image):
+    image_tensor = read_image(input_image)
+    prediction_mask = infer(image_tensor=image_tensor, model=model)
+    prediction_colormap = decode_segmentation_masks(prediction_mask, colormap, 20)
+    overlay = get_overlay(image_tensor, prediction_colormap)
+    return (overlay, prediction_colormap)
+
+i = gr.inputs.Image()
+o = [gr.outputs.Image(), gr.outputs.Image()]
+
+examples = [["example_image_2.jpeg"], ["example_image_3.jpeg"]]
+title = "Human Part Segmentation"
+description = "Upload an image or select from examples to segment out different human parts."
+
+article = "<div style='text-align: center;'><a href='https://twitter.com/SatpalPatawat' target='_blank'>Space by Satpal Singh Rathore</a><br><a href='https://keras.io/examples/vision/deeplabv3_plus/' target='_blank'>Keras example by Soumik Rakshit</a></div>"
+gr.Interface(segmentation, i, o, examples=examples, allow_flagging=False, analytics_enabled=False,
+  title=title, description=description, article=article).launch(enable_queue=True)

requirements.txt

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+tensorflow
+numpy
+opencv-python-headless