computer-vision

name	Computer Vision
description	Implement computer vision tasks including image classification, object detection, segmentation, and pose estimation using PyTorch and TensorFlow

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