initial commit

.gitignore

@@ -0,0 +1,161 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# tests and logs
+tests/fixtures/cached_*_text.txt
+logs/
+lightning_logs/
+lang_code_data/
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# vscode
+.vs
+.vscode
+
+# Pycharm
+.idea
+
+# TF code
+tensorflow_code
+
+# Models
+proc_data
+
+# examples
+runs
+/runs_old
+/wandb
+/examples/runs
+/examples/**/*.args
+/examples/rag/sweep
+
+# data
+/data
+serialization_dir
+
+# emacs
+*.*~
+debug.env
+
+# vim
+.*.swp
+
+#ctags
+tags
+
+# pre-commit
+.pre-commit*
+
+# .lock
+*.lock

app.py

@@ -0,0 +1,85 @@
+import os
+import gradio as gr
+import numpy as np
+import glob
+import warnings
+import pandas as pd
+from .utils import OrthogonalRegularizer
+
+from huggingface_hub.keras_mixin import from_pretrained_keras
+
+# load model
+model = from_pretrained_keras("keras-io/pointnet_segmentation",  custom_objects={"OrthogonalRegularizer": OrthogonalRegularizer})
+
+# Examples
+samples = []
+input_images = glob.glob('asset/source/*.png')
+examples = [[im, f"asset/source/{im.split('/')[-1].split('.')[0]}.csv", f'asset/ground_truth/{im_name}.png'] for im in input_images]
+LABELS = ['wing', 'body', 'tail', 'engine']
+    
+def visualize_data(point_cloud, labels, output_path=None):
+    df = pd.DataFrame(
+        data={
+            "x": point_cloud[:, 0],
+            "y": point_cloud[:, 1],
+            "z": point_cloud[:, 2],
+            "label": labels,
+        }
+    )
+    fig = plt.figure(figsize=(15, 10))
+    ax = plt.axes(projection="3d")
+    for index, label in enumerate(LABELS):
+        c_df = df[df["label"] == label]
+        try:
+            ax.scatter(
+                c_df["x"], c_df["y"], c_df["z"], label=label, alpha=0.5, c=COLORS[index]
+            )
+        except IndexError:
+            pass
+    ax.legend()
+    if output_path:
+        os.makedirs(os.path.dirname(output_path), exist_ok=True)
+        plt.savefig(output_path)
+
+def inference(im_path,
+              truth_path,
+              file_obj,
+              output_path = 'asset/output',
+              cpu = False,
+              ):
+
+    csv_path = file_obj.name
+    im_name = csv_path.split('/')[-1].split('.')[0]
+    
+    if os.path.exists(csv_path):
+        df = pd.read_csv(csv_path, index_col=None)
+        inputs = df[['x', 'y', 'z']].values
+        y_test = df.iloc[:, 3:].values
+    else:
+        warnings.warn(f'{csv_path} not found for {im_path}')
+        return
+
+
+    preds = model.predict(np.expand_dims(inputs, 0))[0]
+    label_map = LABELS + ["none"]
+    visualize_data(inputs, [label_map[np.argmax(label)] for label in preds], f'{output_path}/{im_name}.png')
+    return f'{output_path}/{im_name}.png'
+
+article = "<div style='text-align: center;'><a href='https://nouamanetazi.me/' target='_blank'>Space by Nouamane Tazi</a><br><a href='https://keras.io/examples/vision/pointnet_segmentation' target='_blank'>Keras example by Soumik Rakshit, Sayak Paul</a></div>"
+
+iface = gr.Interface(
+    inference, # main function
+    inputs = [ 
+        gr.inputs.Image(label='Image', type="filepath"),
+        gr.inputs.Image(label='Ground Truth', type="filepath"),
+        "file"
+        
+    ],
+    outputs = [
+        gr.outputs.Image(label='result'), # generated image
+    ], 
+    
+    title = 'Point cloud segmentation with PointNet',
+    article = article,
+    examples = examples,
+).launch(enable_queue=True, cache_examples=True)

asset/source/sample_0.csv

@@ -0,0 +1,1025 @@
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requirements.txt

@@ -0,0 +1 @@
+tensorflow

utils.py

@@ -0,0 +1,22 @@
+from tensorflow import keras
+
+class OrthogonalRegularizer(keras.regularizers.Regularizer):
+    """Reference: https://keras.io/examples/vision/pointnet/#build-a-model"""
+
+    def __init__(self, num_features, l2reg=0.001):
+        self.num_features = num_features
+        self.l2reg = l2reg
+        self.identity = tf.eye(num_features)
+
+    def __call__(self, x):
+        identity = tf.cast(self.identity, x.dtype)
+        x = tf.reshape(x, (tf.shape(x)[0], self.num_features, self.num_features))
+        xxt = tf.tensordot(x, x, axes=(2, 2))
+        xxt = tf.reshape(
+            xxt, (tf.shape(x)[0] * tf.shape(x)[0], self.num_features, self.num_features)
+        )
+        return tf.reduce_sum(self.l2reg * tf.square(xxt - identity))
+
+    def get_config(self):
+        config = {"num_features": self.num_features, "l2reg": self.l2reg}
+        return config