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from huggingface_hub import from_pretrained_keras
import gradio as gr
from PIL import Image
import tensorflow as tf
import numpy as np
import os
# load model
model = tf.keras.models.load_model('./tf_model.h5') # from keras-io/semantic-segmentation
# gradio components
inputs = gr.inputs.Image()
outputs = gr.outputs.Image()
# inference fn
def predict(inputs):
# convert img to numpy array, resize and normalize to make the prediction
img = np.array(inputs)
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)
labels = []
# convert the prediction mask into binary masks for each class
binary_masks = {}
mask_codes = {}
# 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
img = Image.fromarray(mask.astype(np.int8), mode="L")
return img
gr.Interface(predict, inputs = inputs, outputs = outputs).launch() |