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Computer Vision Fundamentals

Computer Vision Fundamentals - Lesson 5: Object Detection and Recognition

Overview:

This lesson covers object detection and recognition techniques, which are fundamental for identifying and classifying objects within images. Students will learn about various methods and algorithms used for detecting and recognizing objects in computer vision tasks.

Objectives:

By the end of this lesson, students should be able to:

  • Understand the key concepts and algorithms for object detection and recognition.
  • Implement object detection using traditional methods and modern deep learning approaches.
  • Evaluate and compare different object detection techniques.

Topics Covered:

  1. Introduction to Object Detection and Recognition:

    • Definition and importance of object detection and recognition.
    • Applications in real-world scenarios (e.g., surveillance, autonomous vehicles).

  2. Traditional Object Detection Methods:

    • Haar Cascades:

      • Overview and use cases.
      • Code implementation and example.

      Code Example:

      import cv2

# Load the Haar cascade classifier
cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Load an image
image = cv2.imread('image.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Detect objects
objects = cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

# Draw rectangles around detected objects
for (x, y, w, h) in objects:
    cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)

# Display results
cv2.imshow('Detected Objects', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

    • Histogram of Oriented Gradients (HOG):

      • Concept and application.
      • Code implementation and example.

      Code Example:

      from skimage.feature import hog
from skimage import exposure
import matplotlib.pyplot as plt
import cv2

# Load an image
image = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)

# Compute HOG features and visualization
features, hog_image = hog(image, visualize=True, multichannel=False)
hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 10))

# Display the HOG image
plt.figure(figsize=(10, 5))
plt.subplot(121)
plt.imshow(image, cmap='gray')
plt.title('Input Image')
plt.subplot(122)
plt.imshow(hog_image_rescaled, cmap='gray')
plt.title('HOG Image')
plt.show()

  3. Deep Learning-Based Object Detection:

    • YOLO (You Only Look Once):

      • Concept and architecture.
      • Code implementation using pre-trained YOLO models.

      Code Example:

      import cv2
import numpy as np

# Load YOLO model
net = cv2.dnn.readNet('yolov3.weights', 'yolov3.cfg')
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# Load image
image = cv2.imread('image.jpg')
height, width, channels = image.shape

# Prepare image for YOLO
blob = cv2.dnn.blobFromImage(image, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)

# Post-process the detections
class_ids = []
confidences = []
boxes = []

for out in outs:
    for detection in out:
        for obj in detection:
            scores = obj[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > 0.5:
                center_x = int(obj[0] * width)
                center_y = int(obj[1] * height)
                w = int(obj[2] * width)
                h = int(obj[3] * height)
                x = int(center_x - w / 2)
                y = int(center_y - h / 2)
                boxes.append([x, y, w, h])
                confidences.append(float(confidence))
                class_ids.append(class_id)

# Apply non-max suppression to remove overlapping boxes
indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

for i in indices:
    x, y, w, h = boxes[i[0]]
    cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)

# Display results
cv2.imshow('YOLO Object Detection', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

    • SSD (Single Shot MultiBox Detector) and Faster R-CNN:

      • Overview and comparisons with YOLO.
      • Brief code examples using pre-trained models.

  4. Object Recognition:

    • Introduction to Object Recognition:

      • Concepts and approaches.
      • Key algorithms (e.g., template matching, feature-based recognition).

    • Template Matching:

      • Concept and application.
      • Code implementation and example.

      Code Example:

      import cv2

# Load images
image = cv2.imread('image.jpg')
template = cv2.imread('template.jpg')

# Convert to grayscale
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray_template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)

# Template matching
result = cv2.matchTemplate(gray_image, gray_template, cv2.TM_CCOEFF_NORMED)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)

# Draw rectangle around the detected object
top_left = max_loc
bottom_right = (top_left[0] + template.shape[1], top_left[1] + template.shape[0])
cv2.rectangle(image, top_left, bottom_right, (0, 255, 0), 2)

# Display results
cv2.imshow('Template Matching', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

  5. Evaluating Object Detection and Recognition Models:

    • Metrics: Precision, recall, F1 score, and mean average precision (mAP).
    • Choosing the right model for specific applications.

  6. Challenges in Object Detection and Recognition:

    • Handling occlusions, varying lighting, and complex backgrounds.

Activities and Quizzes:

  • Activity: Implement object detection using YOLO or SSD on a given dataset. Visualize and analyze the results.
  • Quiz: Multiple-choice questions on object detection algorithms and recognition techniques.

Assignments:

  • Assignment 5: Develop a project where you implement object detection and recognition on a custom dataset (e.g., detecting and classifying objects in a series of images). Submit the code along with a detailed report discussing the techniques used and the performance of your system.

This lesson provides students with a comprehensive understanding of object detection and recognition, equipping them with the skills needed to tackle complex computer vision problems.

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