Spaces:
Runtime error
Runtime error
File size: 2,772 Bytes
ed73811 47d1a17 ed73811 fdf1e29 ed73811 47d1a17 ed73811 47d1a17 ed73811 47d1a17 ed73811 47d1a17 ed73811 47d1a17 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
from huggingface_hub import from_pretrained_keras
import gradio as gr
import tensorflow as tf
import numpy as np
import os
model = tf.keras.models.load.model(os.path.join
inputs = gr.inputs.Image()
output = gr.output.Image()
def predict(image_input):
img = np.array(inputs)
pass
class PreTrainedPipeline():
def __init__(self, path: str):
# load the model
self.model = keras.models.load_model(os.path.join(path, "tf_model.h5"))
def __call__(self, inputs: "Image.Image")-> List[Dict[str, Any]]:
# 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 = self.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")
# we need to make it readable for the widget
with io.BytesIO() as out:
img.save(out, format="PNG")
png_string = out.getvalue()
mask = base64.b64encode(png_string).decode("utf-8")
mask_codes[f"mask_{cls}"] = mask
# widget needs the below format, for each class we return label and mask string
labels.append({
"label": f"LABEL_{cls}",
"mask": mask_codes[f"mask_{cls}"],
"score": 1.0,
})
return labels |