Computer Vision with Python: Analyze Images Using OpenCV and Deep Learning

 

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🧠 Part 7: Computer Vision with OpenCV and Deep Learning

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👁️ What Is Computer Vision?

Computer Vision (CV) enables machines to “see” and understand images or videos. It’s used in:

• Face detection

• Object recognition

• Medical imaging

• Self-driving cars

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🧰 Tools We'll Use

• OpenCV – Image processing library

• TensorFlow/Keras – Deep learning models (CNNs)

• Pre-trained Models – For fast and accurate image classification

Install with:

pip install opencv-python tensorflow

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🖼️ Step-by-Step: Image Classification with a CNN

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🗂️ Step 1: Load & Preprocess the CIFAR-10 Dataset

import tensorflow as tf
from tensorflow.keras.datasets import cifar10
import matplotlib.pyplot as plt

(X_train, y_train), (X_test, y_test) = cifar10.load_data()
X_train, X_test = X_train / 255.0, X_test / 255.0

# Show a sample image
plt.imshow(X_train[0])
plt.title(f"Label: {y_train[0][0]}")
plt.show()

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🧠 Step 2: Build a Convolutional Neural Network (CNN)

model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3)),
    tf.keras.layers.MaxPooling2D((2,2)),
    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2,2)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

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⚙️ Step 3: Compile & Train the Model

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(X_train, y_train, epochs=10, validation_split=0.2)

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📈 Step 4: Evaluate and Predict

test_loss, test_acc = model.evaluate(X_test, y_test)
print(f"Test Accuracy: {test_acc:.2f}")

predictions = model.predict(X_test)
print("Predicted class:", predictions[0].argmax())

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🧰 Bonus: Load and Process Custom Images with OpenCV

import cv2

image = cv2.imread('your_image.jpg')
resized = cv2.resize(image, (32, 32)) / 255.0
reshaped = resized.reshape(1, 32, 32, 3)

prediction = model.predict(reshaped)
print("Predicted class:", prediction.argmax())

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🔄 Alternative: Use Pretrained Model (MobileNet)

from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions

model = MobileNetV2(weights='imagenet')

from tensorflow.keras.preprocessing import image
import numpy as np

img = image.load_img('your_image.jpg', target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

preds = model.predict(x)
print("Predicted:", decode_predictions(preds, top=1)[0])

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🧪 Practice Challenge

• Try using other datasets like Fashion MNIST or CelebA

• Detect faces using cv2.CascadeClassifier

• Implement edge detection with OpenCV

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🎓 What You’ve Learned:

• Image classification using CNNs

• Using OpenCV for image processing

• How to use pre-trained models for instant predictions

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🧭 What’s Next?

In Part 8, we’ll explore Reinforcement Learning (RL)—where agents learn to make decisions through trial and error using rewards and penalties.

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Deep Learning with Python: Build Neural Networks Using TensorFlow and Keras

 

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🧠 Part 5: Deep Learning and Neural Networks with TensorFlow and Keras

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🔍 What Is Deep Learning?

Deep Learning is a subfield of machine learning that uses artificial neural networks—inspired by the human brain—to recognize patterns and make decisions.

It's especially effective in handling:

• Images

• Audio

• Text

• Complex data with high dimensionality

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🧠 What Is a Neural Network?

A neural network is made up of layers of interconnected "neurons":

• Input Layer – takes in raw data (e.g., pixels)

• Hidden Layers – extract patterns using weights and activation functions

• Output Layer – makes predictions (e.g., class label)

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🚀 Setting Up TensorFlow and Keras

Install TensorFlow (Keras is included):

pip install tensorflow

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📊 Project: Image Classification with MNIST Dataset

The MNIST dataset is a set of 70,000 handwritten digits (0–9), perfect for beginners.

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✅ Step 1: Load Data

import tensorflow as tf
from tensorflow.keras.datasets import mnist

(X_train, y_train), (X_test, y_test) = mnist.load_data()

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🧼 Step 2: Preprocess Data

# Normalize pixel values to [0, 1]
X_train = X_train / 255.0
X_test = X_test / 255.0

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🧠 Step 3: Build the Neural Network

model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),   # Input layer
    tf.keras.layers.Dense(128, activation='relu'),   # Hidden layer
    tf.keras.layers.Dense(10, activation='softmax')  # Output layer
])

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🛠️ Step 4: Compile the Model

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

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🎯 Step 5: Train the Model

model.fit(X_train, y_train, epochs=5)

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📈 Step 6: Evaluate Performance

test_loss, test_acc = model.evaluate(X_test, y_test)
print(f"Test accuracy: {test_acc:.2f}")

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🔮 Step 7: Make Predictions

predictions = model.predict(X_test)
import numpy as np

# Predict and show the first test digit
print("Predicted digit:", np.argmax(predictions[0]))

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💡 Practice Challenge

Try changing the network architecture:

• Add another hidden layer

• Use different activation functions (sigmoid, tanh)

• Increase or decrease the number of neurons

# Add more layers and experiment

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🎓 What You’ve Learned:

• What neural networks are and how they work

• How to build, train, and evaluate a deep learning model using Keras

• How to classify images with high accuracy

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🧭 What’s Next?

In Part 6, we’ll explore Natural Language Processing (NLP) using Python. You’ll learn how to process text, analyze sentiment, and even build a basic chatbot.

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