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''' ----------------------------------------
* Creation Time : Sun Aug 28 21:38:58 2022
* Last Modified : Sun Aug 28 21:41:36 2022
* Author : Charles N. Christensen
* Github : github.com/charlesnchr
----------------------------------------'''
from turtle import title
import gradio as gr
from huggingface_hub import from_pretrained_keras
import tensorflow as tf
import numpy as np
from PIL import Image
import io
import base64
model = tf.keras.models.load_model("./tf_model.h5")
def predict(image):
img = np.array(image)
original_shape = img.shape[:2]
im = tf.image.resize(img, (128, 128))
im = tf.cast(im, tf.float32) / 255.0
pred_mask = model.predict(im[tf.newaxis, ...])
# take the best performing class for each pixel
# the output of argmax looks like this [[1, 2, 0], ...]
pred_mask_arg = tf.argmax(pred_mask, axis=-1)
# convert the prediction mask into binary masks for each class
binary_masks = {}
# when we take tf.argmax() over pred_mask, it becomes a tensor object
# the shape becomes TensorShape object, looking like this TensorShape([128])
# we need to take get shape, convert to list and take the best one
rows = pred_mask_arg[0][1].get_shape().as_list()[0]
cols = pred_mask_arg[0][2].get_shape().as_list()[0]
for cls in range(pred_mask.shape[-1]):
binary_masks[f"mask_{cls}"] = np.zeros(shape = (pred_mask.shape[1], pred_mask.shape[2])) #create masks for each class
for row in range(rows):
for col in range(cols):
if pred_mask_arg[0][row][col] == cls:
binary_masks[f"mask_{cls}"][row][col] = 1
else:
binary_masks[f"mask_{cls}"][row][col] = 0
mask = binary_masks[f"mask_{cls}"]
mask *= 255
mask = np.array(Image.fromarray(mask).convert("L"))
mask = tf.image.resize(mask[..., tf.newaxis], original_shape)
mask = tf.cast(mask, tf.uint8)
mask = mask.numpy().squeeze()
return mask
title = '<h1 style="text-align: center;">Segment Pets</h1>'
description = """
## About
This space demonstrates the use of a semantic segmentation model to segment pets and classify them
according to the pixels.
## 🚀 To run
Upload a pet image and hit submit or select one from the given examples
"""
inputs = gr.inputs.Image(label="Upload a pet image", type = 'pil', optional=False)
outputs = [
gr.outputs.Image(label="Segmentation")
# , gr.outputs.Textbox(type="auto",label="Pet Prediction")
]
examples = [
"./examples/dogcat.jpeg",
]
interface = gr.Interface(fn=predict,
inputs=inputs,
outputs=outputs,
title = title,
description=description,
examples=examples
)
interface.launch()
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