Computer Vision

Computer Vision enables machines to interpret and understand visual information from the world. At its core is the Convolutional Neural Network (CNN) — an architecture specifically designed to process grid-like data such as images.

The Convolution Operation

A convolution slides a small filter (kernel) across an image, computing a dot product at each position. This produces a feature map — a compact representation that highlights edges, textures, or patterns the filter was designed to detect.

import numpy as np
from scipy.signal import convolve2d

image = np.array([
    [1,1,1,0,0],
    [0,1,1,1,0],
    [0,0,1,1,1],
    [0,0,1,1,0],
    [0,1,1,0,0]
])

# Edge-detection filter (Sobel-like)
kernel = np.array([
    [1, 0, 1],
    [0, 1, 0],
    [1, 0, 1]
])

feature_map = convolve2d(image, kernel, mode='valid')
print(feature_map)

Filter Effect Gallery

Apply classic image filters to a small pixel canvas. Draw your own pattern, then see how each kernel transforms it — edge detection, blur, sharpening, and emboss.

CNN Architecture

A CNN stacks several types of layers. Each layer learns progressively more abstract features — from edges in early layers to shapes and objects in later layers.

Image Classification — Cats vs Dogs

A binary CNN classifier trained on 64×64 RGB images. Achieves strong accuracy on the Cats vs. Dogs dataset using data augmentation and early stopping.

Build the Model

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import (Conv2D, MaxPool2D, BatchNormalization,
                                     Flatten, Dense, Dropout)

def build_cnn(input_shape=(64, 64, 3)):
    model = Sequential([
        Conv2D(32, (3,3), activation='relu', input_shape=input_shape),
        BatchNormalization(),
        MaxPool2D(2, 2),

        Conv2D(64, (3,3), activation='relu'),
        BatchNormalization(),
        MaxPool2D(2, 2),

        Conv2D(128, (3,3), activation='relu'),
        MaxPool2D(2, 2),

        Flatten(),
        Dense(256, activation='relu'),
        Dropout(0.5),
        Dense(1, activation='sigmoid')   # binary output
    ])
    model.compile(optimizer='adam',
                  loss='binary_crossentropy',
                  metrics=['accuracy'])
    return model

model = build_cnn()
model.summary()

Train with Data Augmentation

from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import EarlyStopping

datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=20,
    horizontal_flip=True,
    zoom_range=0.2,
    validation_split=0.2
)

train_gen = datagen.flow_from_directory(
    'data/', target_size=(64,64), batch_size=32,
    class_mode='binary', subset='training'
)
val_gen = datagen.flow_from_directory(
    'data/', target_size=(64,64), batch_size=32,
    class_mode='binary', subset='validation'
)

early_stop = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)

history = model.fit(train_gen, validation_data=val_gen,
                    epochs=30, callbacks=[early_stop])

Live Demo — Colour Classifier

A CNN trained on 64×64 colour swatches classifies any RGB colour into one of 10 categories. Pick a colour below — the model fills a virtual 64×64 image with that colour, runs it through the CNN entirely in your browser, and returns the predicted class.

Model trained with python scripts/export_colour_cnn.py — see 5 Computer Vision/Colour_CNN.ipynb for the full training walkthrough.

Knowledge Check

Test your understanding — pick the best answer, then click Check.