Computer Vision

Overview

Computer vision enables machines to understand visual information from images and videos, powering applications like autonomous driving, medical imaging, and surveillance.

When to Use

Image classification and object recognition tasks

Object detection and localization in images

Semantic or instance segmentation projects

Pose estimation and human activity recognition

Face recognition and biometric systems

Medical imaging analysis and diagnostics

Computer Vision Tasks

Image Classification

Categorizing images into classes

Object Detection

Locating and classifying objects in images

Semantic Segmentation

Pixel-level classification

Instance Segmentation

Detecting individual object instances

Pose Estimation

Identifying human body joints

Face Recognition

Identifying individuals in images

Popular Architectures

Classification

ResNet, VGG, EfficientNet, Vision Transformer

Detection

YOLO, Faster R-CNN, SSD, RetinaNet

Segmentation

U-Net, DeepLab, Mask R-CNN

Pose

OpenPose, PoseNet, HRNet Python Implementation import numpy as np import pandas as pd import matplotlib . pyplot as plt import matplotlib . patches as patches from PIL import Image , ImageDraw import torch import torch . nn as nn from torch . utils . data import DataLoader , TensorDataset from torchvision import transforms , models , datasets import tensorflow as tf from tensorflow import keras from tensorflow . keras import layers import cv2 from sklearn . metrics import accuracy_score , confusion_matrix import seaborn as sns import warnings warnings . filterwarnings ( 'ignore' ) print ( "=== 1. Image Classification CNN ===" )

Define image classification model

class ImageClassifierCNN ( nn . Module ) : def init ( self , num_classes = 10 ) : super ( ) . init ( ) self . features = nn . Sequential ( nn . Conv2d ( 3 , 32 , kernel_size = 3 , padding = 1 ) , nn . ReLU ( inplace = True ) , nn . BatchNorm2d ( 32 ) , nn . MaxPool2d ( 2 , 2 ) , nn . Conv2d ( 32 , 64 , kernel_size = 3 , padding = 1 ) , nn . ReLU ( inplace = True ) , nn . BatchNorm2d ( 64 ) , nn . MaxPool2d ( 2 , 2 ) , nn . Conv2d ( 64 , 128 , kernel_size = 3 , padding = 1 ) , nn . ReLU ( inplace = True ) , nn . BatchNorm2d ( 128 ) , nn . MaxPool2d ( 2 , 2 ) , ) self . classifier = nn . Sequential ( nn . Linear ( 128 * 4 * 4 , 256 ) , nn . ReLU ( inplace = True ) , nn . Dropout ( 0.5 ) , nn . Linear ( 256 , num_classes ) ) def forward ( self , x ) : x = self . features ( x ) x = x . view ( x . size ( 0 ) , - 1 ) x = self . classifier ( x ) return x model = ImageClassifierCNN ( num_classes = 10 ) print ( f"Model parameters: { sum ( p . numel ( ) for p in model . parameters ( ) ) : , } " )

2. Object Detection setup

print ( "\n=== 2. Object Detection Framework ===" ) class ObjectDetector ( nn . Module ) : def init ( self ) : super ( ) . init ( )

Backbone

self . backbone = nn . Sequential ( nn . Conv2d ( 3 , 32 , 3 , padding = 1 ) , nn . ReLU ( ) , nn . MaxPool2d ( 2 , 2 ) , nn . Conv2d ( 32 , 64 , 3 , padding = 1 ) , nn . ReLU ( ) , nn . MaxPool2d ( 2 , 2 ) , )

Bounding box regression

self . bbox_head = nn . Sequential ( nn . Linear ( 64 * 8 * 8 , 128 ) , nn . ReLU ( ) , nn . Linear ( 128 , 4 )

x, y, w, h

)

Class prediction

self . class_head = nn . Sequential ( nn . Linear ( 64 * 8 * 8 , 128 ) , nn . ReLU ( ) , nn . Linear ( 128 , 10 )

10 classes

) def forward ( self , x ) : features = self . backbone ( x ) features_flat = features . view ( features . size ( 0 ) , - 1 ) bboxes = self . bbox_head ( features_flat ) classes = self . class_head ( features_flat ) return bboxes , classes detector = ObjectDetector ( ) print ( f"Detector parameters: { sum ( p . numel ( ) for p in detector . parameters ( ) ) : , } " )

3. Semantic Segmentation

print ( "\n=== 3. Semantic Segmentation U-Net ===" ) class UNet ( nn . Module ) : def init ( self , num_classes = 5 ) : super ( ) . init ( )

Encoder

self . enc1 = self . _conv_block ( 3 , 32 ) self . pool1 = nn . MaxPool2d ( 2 , 2 ) self . enc2 = self . _conv_block ( 32 , 64 ) self . pool2 = nn . MaxPool2d ( 2 , 2 )

Bottleneck

self . bottleneck = self . _conv_block ( 64 , 128 )

Decoder

self . upconv2 = nn . ConvTranspose2d ( 128 , 64 , 2 , stride = 2 ) self . dec2 = self . _conv_block ( 128 , 64 ) self . upconv1 = nn . ConvTranspose2d ( 64 , 32 , 2 , stride = 2 ) self . dec1 = self . _conv_block ( 64 , 32 )

Final output

self . out = nn . Conv2d ( 32 , num_classes , 1 ) def _conv_block ( self , in_channels , out_channels ) : return nn . Sequential ( nn . Conv2d ( in_channels , out_channels , 3 , padding = 1 ) , nn . ReLU ( inplace = True ) , nn . Conv2d ( out_channels , out_channels , 3 , padding = 1 ) , nn . ReLU ( inplace = True ) ) def forward ( self , x ) : enc1 = self . enc1 ( x ) enc2 = self . enc2 ( self . pool1 ( enc1 ) ) bottleneck = self . bottleneck ( self . pool2 ( enc2 ) ) dec2 = self . dec2 ( torch . cat ( [ self . upconv2 ( bottleneck ) , enc2 ] , 1 ) ) dec1 = self . dec1 ( torch . cat ( [ self . upconv1 ( dec2 ) , enc1 ] , 1 ) ) return self . out ( dec1 ) unet = UNet ( num_classes = 5 ) print ( f"U-Net parameters: { sum ( p . numel ( ) for p in unet . parameters ( ) ) : , } " )

4. Transfer Learning

print ( "\n=== 4. Transfer Learning with Pre-trained Models ===" ) try :

Load pre-trained ResNet18

pretrained_model

models . resnet18 ( pretrained = True ) num_ftrs = pretrained_model . fc . in_features pretrained_model . fc = nn . Linear ( num_ftrs , 10 ) print ( f"Pre-trained ResNet18 adapted for 10 classes" ) print ( f"Parameters: { sum ( p . numel ( ) for p in pretrained_model . parameters ( ) ) : , } " ) except : print ( "Pre-trained models not available" )

5. Image preprocessing and augmentation

print ( "\n=== 5. Image Preprocessing and Augmentation ===" ) transform_basic = transforms . Compose ( [ transforms . Resize ( ( 224 , 224 ) ) , transforms . ToTensor ( ) , transforms . Normalize ( mean = [ 0.485 , 0.456 , 0.406 ] , std = [ 0.229 , 0.224 , 0.225 ] ) ] ) transform_augmented = transforms . Compose ( [ transforms . RandomRotation ( 20 ) , transforms . RandomHorizontalFlip ( ) , transforms . ColorJitter ( brightness = 0.2 , contrast = 0.2 ) , transforms . RandomAffine ( degrees = 0 , translate = ( 0.1 , 0.1 ) ) , transforms . Resize ( ( 224 , 224 ) ) , transforms . ToTensor ( ) , transforms . Normalize ( mean = [ 0.485 , 0.456 , 0.406 ] , std = [ 0.229 , 0.224 , 0.225 ] ) ] ) print ( "Augmentation transforms defined" )

6. Synthetic image data

print ( "\n=== 6. Synthetic Image Data Creation ===" ) def create_synthetic_images ( num_images = 100 , img_size = 32 ) : """Create synthetic images with shapes""" images = [ ] labels = [ ] for _ in range ( num_images ) : img = np . ones ( ( img_size , img_size , 3 ) ) * 255

Randomly draw shapes

shape_type

np . random . randint ( 0 , 3 ) if shape_type == 0 :

Circle

center

( np . random . randint ( 5 , img_size - 5 ) , np . random . randint ( 5 , img_size - 5 ) ) radius = np . random . randint ( 3 , 10 ) cv2 . circle ( img , center , radius , ( 0 , 0 , 0 ) , - 1 ) labels . append ( 0 ) elif shape_type == 1 :

Rectangle

pt1

( np . random . randint ( 0 , img_size - 10 ) , np . random . randint ( 0 , img_size - 10 ) ) pt2 = ( pt1 [ 0 ] + np . random . randint ( 5 , 15 ) , pt1 [ 1 ] + np . random . randint ( 5 , 15 ) ) cv2 . rectangle ( img , pt1 , pt2 , ( 0 , 0 , 0 ) , - 1 ) labels . append ( 1 ) else :

Triangle

pts

np . array ( [ [ np . random . randint ( 0 , img_size ) , np . random . randint ( 0 , img_size ) ] , [ np . random . randint ( 0 , img_size ) , np . random . randint ( 0 , img_size ) ] , [ np . random . randint ( 0 , img_size ) , np . random . randint ( 0 , img_size ) ] ] ) cv2 . drawContours ( img , [ pts ] , 0 , ( 0 , 0 , 0 ) , - 1 ) labels . append ( 2 ) images . append ( img . astype ( np . float32 ) / 255.0 ) return np . array ( images ) , np . array ( labels ) X_images , y_labels = create_synthetic_images ( num_images = 300 , img_size = 32 ) print ( f"Synthetic dataset: { X_images . shape } , Labels: { y_labels . shape } " ) print ( f"Class distribution: { np . bincount ( y_labels ) } " )

7. Visualization

print ( "\n=== 7. Visualization ===" ) fig , axes = plt . subplots ( 3 , 3 , figsize = ( 12 , 10 ) )

Display synthetic images

for i in range ( 9 ) : idx = i % len ( X_images ) axes [ i // 3 , i % 3 ] . imshow ( X_images [ idx ] ) axes [ i // 3 , i % 3 ] . set_title ( f"Class { y_labels [ idx ] } " ) axes [ i // 3 , i % 3 ] . axis ( 'off' ) plt . suptitle ( "Synthetic Image Dataset" , fontsize = 14 , fontweight = 'bold' ) plt . tight_layout ( ) plt . savefig ( 'synthetic_images.png' , dpi = 100 , bbox_inches = 'tight' ) print ( "Synthetic images saved as 'synthetic_images.png'" )

8. Model architectures comparison

print ( "\n=== 8. Architecture Comparison ===" ) architectures_info = { 'CNN' : ImageClassifierCNN ( ) , 'ObjectDetector' : ObjectDetector ( ) , 'U-Net' : UNet ( ) , } arch_data = { 'Architecture' : list ( architectures_info . keys ( ) ) , 'Parameters' : [ sum ( p . numel ( ) for p in m . parameters ( ) ) for m in architectures_info . values ( ) ] , 'Use Case' : [ 'Classification' , 'Object Detection' , 'Segmentation' ] } arch_df = pd . DataFrame ( arch_data ) print ( "\nArchitecture Comparison:" ) print ( arch_df . to_string ( index = False ) )

Visualization

fig , axes = plt . subplots ( 1 , 2 , figsize = ( 14 , 5 ) )

Parameters comparison

axes [ 0 ] . barh ( arch_df [ 'Architecture' ] , arch_df [ 'Parameters' ] , color = 'steelblue' ) axes [ 0 ] . set_xlabel ( 'Number of Parameters' ) axes [ 0 ] . set_title ( 'Model Complexity Comparison' ) axes [ 0 ] . set_xscale ( 'log' )

Use cases

use_cases

[ 'Classification' , 'Detection' , 'Segmentation' , 'Classification' , 'Detection' , 'Segmentation' ] colors_map = { 'Classification' : 'green' , 'Detection' : 'orange' , 'Segmentation' : 'red' } bar_colors = [ colors_map [ uc ] for uc in arch_df [ 'Use Case' ] ] axes [ 1 ] . bar ( arch_df [ 'Architecture' ] , [ 1 , 1 , 1 ] , color = bar_colors , alpha = 0.7 ) axes [ 1 ] . set_ylabel ( 'Primary Task' ) axes [ 1 ] . set_title ( 'Architecture Use Cases' ) axes [ 1 ] . set_ylim ( [ 0 , 1.5 ] ) plt . tight_layout ( ) plt . savefig ( 'cv_architecture_comparison.png' , dpi = 100 , bbox_inches = 'tight' ) print ( "\nArchitecture comparison saved as 'cv_architecture_comparison.png'" )

9. Bounding box visualization

print ( "\n=== 9. Bounding Box Visualization ===" ) fig , ax = plt . subplots ( figsize = ( 10 , 8 ) ) ax . imshow ( X_images [ 0 ] )

Draw sample bounding boxes

bboxes

[ ( 5 , 5 , 15 , 15 ) ,

x1, y1, x2, y2

(

18

,

10

,

28

,

20

)

,

(

8

,

20

,

18

,

28

)

]

for

bbox

in

bboxes

:

rect

=

patches

.

Rectangle

(

(

bbox

[

0

]

,

bbox

[

1

]

)

,

bbox

[

2

]

-

bbox

[

0

]

,

bbox

[

3

]

-

bbox

[

1

]

,

linewidth

=

2

,

edgecolor

=

'red'

,

facecolor

=

'none'

)

ax

.

add_patch

(

rect

)

ax

.

set_title

(

'Bounding Box Detection Example'

)

ax

.

axis

(

'off'

)

plt

.

savefig

(

'bounding_boxes.png'

,

dpi

=

100

,

bbox_inches

=

'tight'

)

print

(

"Bounding box visualization saved as 'bounding_boxes.png'"

)

print

(

"\nComputer vision setup completed!"

)

Common CV Architectures

Classification

ResNet, EfficientNet, Vision Transformer

Detection

YOLO v5, Faster R-CNN, RetinaNet

Segmentation

U-Net, DeepLab v3, Mask R-CNN

Tracking

SORT, DeepSORT, ByteTrack

Image Preprocessing

Resizing to standard dimensions

Normalization with ImageNet stats

Data augmentation (rotation, flip, crop)

Color space conversion

Evaluation Metrics

Classification

Accuracy, Precision, Recall, F1

Detection

mAP (mean Average Precision), IoU

Segmentation

IoU, Dice coefficient, Hausdorff distance Deliverables Trained vision model Inference pipeline Performance evaluation Visualization results Model optimization report Deployment guide