add st interface

app.py

@@ -0,0 +1,87 @@
+import numpy as np
+import tensorflow as tf
+import streamlit as st
+from PIL import Image
+from huggingface_hub import from_pretrained_keras
+import cv2
+
+st.header("Segmentacion de partes del cuerpo humano")
+
+st.markdown("Sube una imagen o selecciona un ejemplo para segmentar las distintas partes del cuerpo humano")
+
+file_imagen = st.file_uploader("Sube aqui tu imagen", type=["png", "jpg", "jpeg"])
+
+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('pil'), gr.outputs.Image('pil')]
+
+examples = [["example_image_2.jpeg"], ["example_image_2.jpg"], ["example_image_3.jpeg"]]
+
+col1, col2, col3 = st.columns(3)
+with col1:
+    ex1 = Image.open(examples[0])
+    st.image(ex1, width=200)
+    if st.button("Corre ejemplo 1"):
+        file_imagen = examples[0]
+
+with col2:
+    ex2 = Image.open(examples[1])
+    st.image(ex2, width=200)
+    if st.button("Corre ejemplo 1"):
+        file_imagen = examples[1]S
+
+with col3:
+    ex3 = Image.open(examples[2])
+    st.image(ex3, width=200)
+    if st.button("Corre ejemplo 1"):
+        file_imagen = examples[2]
+
+article = "<div style='text-align: center;'><a href='https://keras.io/examples/vision/deeplabv3_plus/' target='_blank'>Keras example by Praveen Kaushik</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)