Computer Vision

SKILL.md

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