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amitkaps/vis.py

Created July 30, 2018 03:49
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Visualisation helpers for DL Workshop
Raw
vis.py
import numpy as np
import pandas as pd
import altair as alt
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from keras.preprocessing.image import ImageDataGenerator
# Helper to get the labels for each class of Fashion Mnist
def fashion_mnist_label():
labels = {
0: "T-shirt/top", 1:"Trouser", 2:"Pullover", 3:"Dress", 4:"Coat",
5:"Sandal", 6:"Shirt", 7:"Sneaker", 8:"Bag", 9:"Ankle boot"}
return labels
# Show an image from an nparray
def imshow(X, label="", colormap="greys"):
"""
Shows an image output from a numpy input
X : array_like, shape (n, m)
label: A string value for the label of the image (default is blank)
colormap: A color map scheme to apply to the image (default is "grey")
"""
img = pd.DataFrame(X).reset_index().melt("index")
img.columns = ["h" , "w", "value"]
image = alt.Chart(img).mark_rect().encode(
alt.X('w:N', axis=None),
alt.Y('h:N', axis=None),
alt.Color("value", legend=None, sort="descending", scale=alt.Scale(scheme=colormap)),
tooltip = ["value"]
).properties(
width = 350,
height = 350,
title = label
)
return image
# Show first unique value from numpy dataset
def imshow_unique(X, y, labels):
"""
Shows an image output from a numpy input
X : array_like, shape (count x width x height)
y : array_like, shape (count)
labels: A dictionary of labels for y
"""
u, indices = np.unique(y, return_index=True)
plt.figure(figsize = (16,7))
for i in u:
plt.subplot(2,5,i+1)
plt.imshow(X[indices[i]], cmap="gray")
plt.title(labels[y[indices[i]]])
plt.axis('off')
# Create a sprite from the numpy dataset
def imshow_sprite(X):
"""
Returns a sprite image consisting of images passed as argument.
Images should be count x width x height
"""
if isinstance(X, list):
X = np.array(X)
img_h = X.shape[1]
img_w = X.shape[2]
n_plots = int(np.ceil(np.sqrt(X.shape[0])))
spriteimage = np.ones((img_h * n_plots ,img_w * n_plots ))
for i in range(n_plots):
for j in range(n_plots):
this_filter = i * n_plots + j
if this_filter < X.shape[0]:
this_img = X[this_filter]
spriteimage[i * img_h:(i + 1) * img_h,
j * img_w:(j + 1) * img_w] = this_img
plt.figure(figsize = (10,10))
plt.imshow(spriteimage,cmap='gray')
plt.axis('off')
# Plot a 3d
def plot3d(X,Y,Z):
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, color='y')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.show()
# Visualise the metrics from the model
def metrics(history):
df = pd.DataFrame(history)
df.reset_index()
df["batch"] = df.index + 1
df = df.melt("batch", var_name="name")
df["val"] = df.name.str.startswith("val")
df["type"] = df["val"]
df["metrics"] = df["val"]
df.loc[df.val == False, "type"] = "training"
df.loc[df.val == True, "type"] = "validation"
df.loc[df.val == False, "metrics"] = df.name
df.loc[df.val == True, "metrics"] = df.name.str.split("val_", expand=True)[1]
df = df.drop(["name", "val"], axis=1)
base = alt.Chart().encode(
x = "batch:Q",
y = "value:Q",
color = "type"
).properties(width = 300, height = 300)
layers = base.mark_circle(size = 50).encode(tooltip = ["batch", "value"]) + base.mark_line()
chart = layers.facet(column='metrics:N', data=df).resolve_scale(y='independent')
return chart
def predict(proba, actual, labels):
"""
Shows a probability output from an probability run
proba : array of probability for each class
actual: an int for the actual class
labels: a dictionary of labels for each class
"""
df = pd.DataFrame({"proba": proba})
df['labels'] = df.index
df['labels'] = df['labels'].map(labels)
df["actual"] = df.index
df.loc[df.index == actual, "actual"] = True
df.loc[df.index != actual, "actual"] = False
predicted_class = df.proba.idxmax()
chart = alt.Chart(df).mark_bar().encode(
alt.X('proba:Q', scale=alt.Scale(domain=[0,1])),
alt.Y('labels:N'),
alt.Color("actual"),
tooltip = ["proba"]
).properties(
width = 350,
height = 350,
title = "Prediction: " + labels[predicted_class]
)
return chart
def show_images(images, labels):
"""
Shows the set of batch image output from a numpy input
images : A set of images with count * width * height * channel
index: An index for the label for the categorical images
"""
num = len(images)
columns = 5
rows = num//5
i = 0
plt.figure(figsize = (16,7))
for img in images:
plt.subplot(rows,columns,i+1)
plt.imshow(img)
label = "label=" + str(labels[i])
plt.title(label)
plt.axis('off')
i = i + 1
def show_single_image_gen(gen, image, num):
"""
Shows the set of image augmented images for a single image
gen: generator object for image augemtation
image: image to be augmented
num: number of augmented images
"""
image_array = np.expand_dims(image, axis=0)
gen.fit(image_array)
samples = gen.flow(image_array)
images = samples.next()
for i in range(num-1):
img = samples.next()
images = np.r_[images, img]
columns = 5
rows = num//5
i = 0
plt.figure(figsize = (16,7))
for img in images:
plt.subplot(rows,columns,i+1)
plt.imshow(img)
plt.axis('off')
i = i + 1
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