Update app.py

app.py

@@ -1,114 +1,114 @@
-import math
-import numpy as np
-import pandas as pd
-
-import os
-import glob
-import trimesh
-import tensorflow as tf
-from tensorflow import keras
-from tensorflow.keras import layers
-from matplotlib import pyplot as plt
-
-import gradio as gr
-from huggingface_hub import from_pretrained_keras
-
-
-
-def conv_bn(x, filters):
-    x = layers.Conv1D(filters, kernel_size=1, padding="valid")(x)
-    x = layers.BatchNormalization(momentum=0.0)(x)
-    return layers.Activation("relu")(x)
-
-
-def dense_bn(x, filters):
-    x = layers.Dense(filters)(x)
-    x = layers.BatchNormalization(momentum=0.0)(x)
-    return layers.Activation("relu")(x)
-
-# @keras.utils.register_keras_serializable
-class OrthogonalRegularizer(keras.regularizers.Regularizer):
-    def __init__(self, num_features, l2reg=0.001, **kwarg):
-        super(OrthogonalRegularizer, self).__init__(**kwargs)
-        self.num_features = num_features
-        self.l2reg = l2reg
-        self.eye = tf.eye(num_features)
-
-    def __call__(self, x):
-        x = tf.reshape(x, (-1, self.num_features, self.num_features))
-        xxt = tf.tensordot(x, x, axes=(2, 2))
-        xxt = tf.reshape(xxt, (-1, self.num_features, self.num_features))
-        return tf.reduce_sum(self.l2reg * tf.square(xxt - self.eye))
-
-    def get_config(self):
-        return {'l2reg': float(self.l2reg)}
-
-def tnet(inputs, num_features):
-
-    # Initalise bias as the indentity matrix
-    bias = keras.initializers.Constant(np.eye(num_features).flatten())
-    reg = OrthogonalRegularizer(num_features)
-
-    x = conv_bn(inputs, 32)
-    x = conv_bn(x, 64)
-    x = conv_bn(x, 512)
-    x = layers.GlobalMaxPooling1D()(x)
-    x = dense_bn(x, 256)
-    x = dense_bn(x, 128)
-    x = layers.Dense(
-        num_features * num_features,
-        kernel_initializer="zeros",
-        bias_initializer=bias,
-        activity_regularizer=reg,
-    )(x)
-    feat_T = layers.Reshape((num_features, num_features))(x)
-    # Apply affine transformation to input features
-    return layers.Dot(axes=(2, 1))([inputs, feat_T])
-
-EXAMPLES_PATH = './examples'
-model = from_pretrained_keras('keras-io/PointNet')
-
-CLASS_MAP = {0: 'chair',
- 1: 'sofa',
- 2: 'desk',
- 3: 'bed',
- 4: 'dresser',
- 5: 'night_stand',
- 6: 'toilet',
- 7: 'bathtub',
- 8: 'monitor',
- 9: 'table'}
-
-def infer(img_path):
-    mesh = trimesh.load(img_path.name)
-    points = mesh.sample(2048)
-    points = np.expand_dims(np.asarray(points), axis=0)
-
-    # run test data through model
-    preds = model.predict(points)
-    preds = tf.math.argmax(preds, -1)
-
-    # plot points with predicted class and label
-    fig = plt.figure(figsize=(4, 6))
-    ax = fig.add_subplot(2, 1, 1, projection="3d")
-    ax.scatter(points[0, :, 0], points[0, :, 1], points[0, :, 2])
-    ax.set_title(f"This is {CLASS_MAP[preds[0].numpy()]}")
-    ax.set_axis_off()
-    # plt.imshow(image)
-    return plt.gcf()
-
-# get the inputs
-inputs = gr.File()
-
-# the app outputs two segmented images
-output = gr.Plot()
-
-
-# it's good practice to pass examples, description and a title to guide users
-title = 'PointNet Classification and Segmentation'
-description = 'Classify images using point cloud Segmentation'
-article = "Author: <a href=\"https://huggingface.co/geninhu\">Nhu Hoang</a>. "
-examples = [f'{EXAMPLES_PATH}/{f}' for f in os.listdir(EXAMPLES_PATH)]
-
-gr.Interface(infer, inputs, output, examples= examples, allow_flagging='never',
-             title=title, description=description, article=article, live=False).launch(enable_queue=True, debug=False, inbrowser=False)
+import math
+import numpy as np
+import pandas as pd
+
+import os
+import glob
+import trimesh
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from matplotlib import pyplot as plt
+
+import gradio as gr
+from huggingface_hub import from_pretrained_keras
+
+
+
+def conv_bn(x, filters):
+    x = layers.Conv1D(filters, kernel_size=1, padding="valid")(x)
+    x = layers.BatchNormalization(momentum=0.0)(x)
+    return layers.Activation("relu")(x)
+
+
+def dense_bn(x, filters):
+    x = layers.Dense(filters)(x)
+    x = layers.BatchNormalization(momentum=0.0)(x)
+    return layers.Activation("relu")(x)
+
+# @keras.utils.register_keras_serializable
+class OrthogonalRegularizer(keras.regularizers.Regularizer):
+    def __init__(self, num_features, l2reg=0.001, **kwarg):
+        super(OrthogonalRegularizer, self).__init__(**kwargs)
+        self.num_features = num_features
+        self.l2reg = l2reg
+        self.eye = tf.eye(num_features)
+
+    def __call__(self, x):
+        x = tf.reshape(x, (-1, self.num_features, self.num_features))
+        xxt = tf.tensordot(x, x, axes=(2, 2))
+        xxt = tf.reshape(xxt, (-1, self.num_features, self.num_features))
+        return tf.reduce_sum(self.l2reg * tf.square(xxt - self.eye))
+
+    def get_config(self):
+        return {'l2reg': float(self.l2reg)}
+
+def tnet(inputs, num_features):
+
+    # Initalise bias as the indentity matrix
+    bias = keras.initializers.Constant(np.eye(num_features).flatten())
+    reg = OrthogonalRegularizer(num_features)
+
+    x = conv_bn(inputs, 32)
+    x = conv_bn(x, 64)
+    x = conv_bn(x, 512)
+    x = layers.GlobalMaxPooling1D()(x)
+    x = dense_bn(x, 256)
+    x = dense_bn(x, 128)
+    x = layers.Dense(
+        num_features * num_features,
+        kernel_initializer="zeros",
+        bias_initializer=bias,
+        activity_regularizer=reg,
+    )(x)
+    feat_T = layers.Reshape((num_features, num_features))(x)
+    # Apply affine transformation to input features
+    return layers.Dot(axes=(2, 1))([inputs, feat_T])
+
+EXAMPLES_PATH = 'examples'
+model = from_pretrained_keras('keras-io/PointNet')
+
+CLASS_MAP = {0: 'chair',
+ 1: 'sofa',
+ 2: 'desk',
+ 3: 'bed',
+ 4: 'dresser',
+ 5: 'night_stand',
+ 6: 'toilet',
+ 7: 'bathtub',
+ 8: 'monitor',
+ 9: 'table'}
+
+def infer(img_path):
+    mesh = trimesh.load(img_path.name)
+    points = mesh.sample(2048)
+    points = np.expand_dims(np.asarray(points), axis=0)
+
+    # run test data through model
+    preds = model.predict(points)
+    preds = tf.math.argmax(preds, -1)
+
+    # plot points with predicted class and label
+    fig = plt.figure(figsize=(4, 6))
+    ax = fig.add_subplot(2, 1, 1, projection="3d")
+    ax.scatter(points[0, :, 0], points[0, :, 1], points[0, :, 2])
+    ax.set_title(f"This is {CLASS_MAP[preds[0].numpy()]}")
+    ax.set_axis_off()
+    # plt.imshow(image)
+    return plt.gcf()
+
+# get the inputs
+inputs = gr.File()
+
+# the app outputs two segmented images
+output = gr.Plot()
+
+
+# it's good practice to pass examples, description and a title to guide users
+title = 'PointNet Classification and Segmentation'
+description = 'Classify images using point cloud Segmentation'
+article = "Author: <a href=\"https://huggingface.co/geninhu\">Nhu Hoang</a>. "
+examples = [f'{EXAMPLES_PATH}/{f}' for f in os.listdir(EXAMPLES_PATH)]
+
+gr.Interface(infer, inputs, output, examples= examples, allow_flagging='never',
+             title=title, description=description, article=article, live=False).launch(enable_queue=True, debug=False, inbrowser=False)