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9a85d12 | 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 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 | import numpy as np
import pandas as pd
import gradio as gr
import cv2
from tensorflow import keras as k
# ============================== variables ==============================
image_size = 256
num_classes = 3
in_channel_tool = 3
in_channel_spec = 9
img_rows, img_cols = image_size, image_size
# ============================== paths ==============================
model_class_path = f"Models/minape_base_multi_ts.h5"
# dataset
csv_path = "Dataset/labels_sample.csv"
tool_path = "Dataset/tool"
spec_path = "Dataset/spec"
# ============================== load/prepare dataset ==============================
df = pd.read_csv(csv_path)
df["tool"] = df.id.map(lambda id: f"{tool_path}/{id}.jpg")
df["spec_x"] = df.id.map(lambda id: f"{spec_path}/x/{id}.jpg")
df["spec_y"] = df.id.map(lambda id: f"{spec_path}/y/{id}.jpg")
df["spec_z"] = df.id.map(lambda id: f"{spec_path}/z/{id}.jpg")
# ============================== examples ===========================================
exs = []
for i in range(len(df)):
row = df.iloc[i,:]
tool_id = row.id
image_label = row.image_label
tool = row.tool
spec_x = row.spec_x
spec_y = row.spec_y
spec_z = row.spec_z
example = [tool_id, image_label, tool, spec_x, spec_y, spec_z]
exs.append(example)
# ============================== preprocess ===========================================
def process_img(img, img_rows, img_cols, channels):
"""
Reads the spectogram files from disk and normalizes the pixel values
@params:
img - Data of the image
img_rows - The image height.
img_cols - The image width.
as_grey - Read the image as Greyscale or RGB.
channels - Number of channels.
@returns:
The created and compiled model (Model)
"""
img = cv2.imread(img)
img = cv2.resize(img, dsize=(img_rows, img_cols), interpolation=cv2.INTER_CUBIC)
img = np.asarray(img, dtype=np.float32)
# normalize
#print(np.max(img))
img = img / 255.0 #np.max(img)
# reshape to match Keras expectaions
img = img.reshape(img_rows, img_cols, channels)
return img
def process_specs(img_x, img_y, img_z, img_rows, img_cols, channels):
img_x = cv2.imread(img_x)
img_y = cv2.imread(img_y)
img_z = cv2.imread(img_z)
img_x = cv2.resize(img_x, dsize=(img_rows, img_cols), interpolation=cv2.INTER_CUBIC)
img_y = cv2.resize(img_y, dsize=(img_rows, img_cols), interpolation=cv2.INTER_CUBIC)
img_z = cv2.resize(img_z, dsize=(img_rows, img_cols), interpolation=cv2.INTER_CUBIC)
img = np.concatenate([img_x, img_y, img_z], axis=2)
img = np.asarray(img, dtype=np.float32)
# normalize
img = img / 255.0 #np.max(img)
return img
# ============================== load model ==============================================
model_class = k.models.load_model(model_class_path, compile=False)
# ============================== app =====================================================
def predict(tool_id, label, tool, spec_x, spec_y, spec_z):
labels = ['sharp', 'used', 'dulled']
tool = process_img(tool, img_rows, img_cols, in_channel_tool)
spec = process_specs(spec_x, spec_y, spec_z, img_rows, img_cols, in_channel_spec)
inputs = [np.array([tool,]), np.array([spec,])]
y_score = model_class.predict(inputs)
y_pred = {label:float(score) for label, score in zip(labels, y_score[0])}
return [
gr.Label(value=label, label="Actual Label", visible=True), gr.Label(value=y_pred, label="Predicted Label", visible=True),
]
# ============================== blocks ========================================================
title = r"""
<h1 align="center">Minape</h1>
"""
description = r"""
<b>Official 🤗 Gradio demo</b> for <a href='https://github.com/hubtru/Minape'
target='_blank'><b>Multimodal, Isotropic Neural Architecture with Patch Embedding for Recognition of Device State</b></a>.<br>
"""
with gr.Blocks() as demo:
gr.Markdown(value=title)
gr.Markdown(description)
with gr.Row():
with gr.Column():
with gr.Row():
tool_id = gr.Textbox("T1R2B1", label="Tool")
label_input = gr.Textbox("Sharp", label="Label")
with gr.Row():
tool = gr.Image(label="Tool", type="filepath")
with gr.Row():
spec_x = gr.Image(label="Spec_x", type="filepath")
spec_y = gr.Image(label="Spec_y", type="filepath")
spec_z = gr.Image(label="Spec_z", type="filepath")
submit_btn = gr.Button("Submit", variant="primary")
with gr.Column():
output_labels = [
gr.Label("Sharp", label="Actual Label"),
gr.Label("Sharp", label="Predicted Label"),
]
examples = gr.Examples(examples=exs, inputs=[tool_id, label_input, tool, spec_x, spec_y, spec_z])
submit_btn.click(fn=predict, inputs=[tool_id, label_input, tool, spec_x, spec_y, spec_z], outputs=output_labels)
demo.launch() |