import cv2
import numpy as np

# Load the image
image = cv2.imread('image.jpg')

# Display the original image
cv2.imshow('Original Image', image)
cv2.waitKey(0)

# Convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Display the grayscale image
cv2.imshow('Grayscale Image', gray)
cv2.waitKey(0)

# Apply a blur to the image
blur = cv2.GaussianBlur(image, (5, 5), 0)

# Display the blurred image
cv2.imshow('Blurred Image', blur)
cv2.waitKey(0)

# Draw a rectangle on the image
start_point = (100, 100)
end_point = (400, 400)
color = (0, 255, 0)  # Green color in BGR
thickness = 3
rectangle = cv2.rectangle(image, start_point, end_point, color, thickness)

# Display the image with the rectangle
cv2.imshow('Image with Rectangle', rectangle)
cv2.waitKey(0)

# Resize the image
resized = cv2.resize(image, (500, 500))

# Display the resized image
cv2.imshow('Resized Image', resized)
cv2.waitKey(0)

# Rotate the image
rows, cols, _ = image.shape
M = cv2.getRotationMatrix2D((cols / 2, rows / 2), 45, 1)
rotated = cv2.warpAffine(image, M, (cols, rows))

# Display the rotated image
cv2.imshow('Rotated Image', rotated)
cv2.waitKey(0)

# Save the modified image
cv2.imwrite('modified_image.jpg', rotated)

# Close all windows
cv2.destroyAllWindows()

import cv2

# Load the Haar cascade XML file for face detection
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# Load the image
image = cv2.imread('image.jpg')

# Convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Perform face detection
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

# Draw rectangles around the detected faces
for (x, y, w, h) in faces:
    cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 3)

# Display the image with the detected faces
cv2.imshow('Object Detection', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import numpy as np

# Define the number of chessboard corners (inner corners)
num_corners_x = 9
num_corners_y = 6

# Create arrays to store object points and image points from all calibration images
obj_points = []  # 3D points in real world space
img_points = []  # 2D points in image plane

# Generate object points for the chessboard (0,0,0), (1,0,0), (2,0,0) ...., (8,5,0)
objp = np.zeros((num_corners_x * num_corners_y, 3), np.float32)
objp[:, :2] = np.mgrid[0:num_corners_x, 0:num_corners_y].T.reshape(-1, 2)

# Iterate through calibration images
for i in range(1, 11):  # Assuming you have 10 calibration images named as 'calibration1.jpg', 'calibration2.jpg', ...
    # Load the calibration image
    img = cv2.imread(f'calibration{i}.jpg')

    # Convert the image to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Find chessboard corners
    ret, corners = cv2.findChessboardCorners(gray, (num_corners_x, num_corners_y), None)

    # If corners are found, add object points and image points
    if ret:
        obj_points.append(objp)
        img_points.append(corners)

        # Draw and display the corners
        img = cv2.drawChessboardCorners(img, (num_corners_x, num_corners_y), corners, ret)
        cv2.imshow(f'Chessboard Corners - Image {i}', img)
        cv2.waitKey(500)  # Delay to display the image

# Perform camera calibration
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, gray.shape[::-1], None, None)

# Print the camera matrix and distortion coefficients
print("Camera Matrix:")
print(mtx)
print("\nDistortion Coefficients:")
print(dist)

# Select an image for 3D reconstruction
img_index = 1
img = cv2.imread(f'calibration{img_index}.jpg')
h, w = img.shape[:2]

# Perform undistortion
new_camera_mtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w, h), 1, (w, h))
undistorted_img = cv2.undistort(img, mtx, dist, None, new_camera_mtx)

# Crop the undistorted image
x, y, w, h = roi
undistorted_img = undistorted_img[y:y+h, x:x+w]

# Display the original and undistorted images
cv2.imshow('Original Image', img)
cv2.imshow('Undistorted Image', undistorted_img)
cv2.waitKey(0)

# Perform 3D reconstruction
# Define the 3D object points for the selected image
obj_points_reconstruct = objp

# Project the object points onto the image plane
_, rvecs, tvecs, inliers = cv2.solvePnPRansac(obj_points_reconstruct, img_points[img_index - 1], mtx, dist)
img_points_reconstruct, _ = cv2.projectPoints(obj_points_reconstruct, rvecs, tvecs, mtx, dist)

# Draw the 3D coordinate axes on the image
axis_length = 3  # Length of the coordinate axes in the visualization


# Define the 3D points for the coordinate axes
axis_points = np.float32([[0,0,0], [axis_length,0,0], [0,axis_length,0], [0,0,-axis_length]]).reshape(-1,3)

# Project the coordinate axis points onto the image plane
img_points_axis, _ = cv2.projectPoints(axis_points, rvecs, tvecs, mtx, dist)

# Extract the origin point and the projected points of the axes
origin = tuple(img_points_reconstruct[0].ravel())
x_axis = tuple(img_points_axis[1].ravel())
y_axis = tuple(img_points_axis[2].ravel())
z_axis = tuple(img_points_axis[3].ravel())

# Draw the coordinate axes on the image
img_with_axes = cv2.line(undistorted_img, origin, x_axis, (255, 0, 0), 2)  # X-axis (Blue)
img_with_axes = cv2.line(img_with_axes, origin, y_axis, (0, 255, 0), 2)  # Y-axis (Green)
img_with_axes = cv2.line(img_with_axes, origin, z_axis, (0, 0, 255), 2)  # Z-axis (Red)

# Display the image with the coordinate axes
cv2.imshow('3D Reconstruction with Coordinate Axes', img_with_axes)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# Load the video file
video = cv2.VideoCapture('video.mp4')

# Check if the video file was successfully opened
if not video.isOpened():
    print("Error opening video file.")

# Read the video frame by frame
while video.isOpened():
    # Read a frame from the video
    ret, frame = video.read()

    # If the frame was successfully read
    if ret:
        # Perform video analysis operations here
        # For example, you can apply image processing, object detection, or any other analysis techniques

        # Display the frame
        cv2.imshow('Video', frame)

        # Press 'q' to exit the video
        if cv2.waitKey(25) & 0xFF == ord('q'):
            break
    else:
        # Break the loop if the video has ended
        break

# Release the video capture object and close all windows
video.release()
cv2.destroyAllWindows()

import cv2

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

# Display the image
cv2.imshow('Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

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

# Resize the image to a specific width and height
resized_image = cv2.resize(image, (500, 300))

# Display the resized image
cv2.imshow('Resized Image', resized_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# Load the Haar cascade file for face detection
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

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

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

# Perform face detection
faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5)

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

# Display the image with detected faces
cv2.imshow('Detected Faces', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import numpy as np
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.applications.resnet50 import ResNet50

# Load a pretrained ResNet50 model
model = ResNet50(weights='imagenet')

# Load and preprocess an image
image = cv2.imread('image.jpg')
preprocessed_image = preprocess_input(image)

# Expand dimensions to match model input shape
input_image = np.expand_dims(preprocessed_image, axis=0)

# Perform image classification
predictions = model.predict(input_image)
decoded_predictions = decode_predictions(predictions, top=5)[0]

# Display the top 5 predicted labels and probabilities
for label, _, probability in decoded_predictions:
    print(f'{label}: {probability}')

import cv2

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

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

# Display the grayscale image
cv2.imshow('Grayscale Image', gray_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

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

# Apply Gaussian blur to the image
blurred_image = cv2.GaussianBlur(image, (5, 5), 0)

# Display the blurred image
cv2.imshow('Blurred Image', blurred_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# Load a grayscale image
image = cv2.imread('image.jpg', 0)

# Apply binary thresholding to the image
_, thresholded_image = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)

# Display the thresholded image
cv2.imshow('Thresholded Image', thresholded_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

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

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

# Apply Canny edge detection to the grayscale image
edges = cv2.Canny(gray_image, threshold1=100, threshold2=200)

# Display the edges
cv2.imshow('Edges', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import numpy as np

# Load a binary image
image = cv2.imread('binary_image.jpg', 0)

# Create a structuring element
kernel = np.ones((5, 5), np.uint8)

# Apply erosion and dilation operations
eroded_image = cv2.erode(image, kernel, iterations=1)
dilated_image = cv2.dilate(image, kernel, iterations=1)

# Display the eroded and dilated images
cv2.imshow('Eroded Image', eroded_image)
cv2.imshow('Dilated Image', dilated_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

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

# Display the image
cv2.imshow('Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# Resize an image
resized = cv2.resize(image, (new_width, new_height))

# Convert image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Apply Gaussian blur
blurred = cv2.GaussianBlur(image, (kernel_size, kernel_size), 0)

# Apply thresholding
ret, thresholded = cv2.threshold(gray, threshold_value, max_value, cv2.THRESH_BINARY)

import cv2

# Load a pre-trained Haar cascade
cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# Perform face detection
faces = cascade.detectMultiScale(image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

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

import cv2

# Open a video capture object
cap = cv2.VideoCapture(0)  # 0 for the default camera

while True:
    # Read a frame from the video
    ret, frame = cap.read()

    # Perform processing on the frame

    # Display the processed frame
    cv2.imshow('Frame', frame)

    # Break the loop if 'q' key is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the video capture object and close windows
cap.release()
cv2.destroyAllWindows()

import cv2

# Read the image
image = cv2.imread('image.jpg')

# Display the image
cv2.imshow('Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

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

# Display the image
cv2.imshow('Image', image)

# Wait for a keyboard event and close the window
cv2.waitKey(0)
cv2.destroyAllWindows()

cv2.imwrite('path/to/save/image.jpg', image)

import cv2

# Step 1: Load the image
image = cv2.imread('path/to/your/image.jpg')

# Step 2: Access image properties
height, width, channels = image.shape
image_type = image.dtype

# Step 3: Print image properties
print("Image dimensions (height x width):", height, "x", width)
print("Number of channels:", channels)
print("Image data type:", image_type)

import cv2

# Step 1: Load the image
image = cv2.imread('path/to/your/image.jpg')

# Step 2: Convert the color space
converted_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Step 3: Display the original and converted images
cv2.imshow('Original Image', image)
cv2.imshow('Converted Image', converted_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# Step 1: Load the image
image = cv2.imread('path/to/your/image.jpg')

# Step 2: Convert the color space
converted_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Step 3: Display the original and converted images
cv2.imshow('Original Image', image)
cv2.imshow('Converted Image', converted_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# Step 1: Load the image
image = cv2.imread('path/to/your/image.jpg')

# Step 2: Define the text parameters
text = "Hello, OpenCV!"
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 1.5
color = (0, 255, 0)  # BGR color tuple
thickness = 2

# Step 3: Add the text to the image
image_with_text = cv2.putText(image, text, (50, 50), font, font_scale, color, thickness, cv2.LINE_AA)

# Step 4: Display the image with text
cv2.imshow('Image with Text', image_with_text)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# Step 1: Load the image
image = cv2.imread('path/to/your/image.jpg')

# Step 2: Define the line parameters
start_point = (50, 50)
end_point = (200, 200)
color = (0, 0, 255)  # BGR color tuple
thickness = 2

# Step 3: Draw the line on the image
image_with_line = cv2.line(image, start_point, end_point, color, thickness)

# Step 4: Display the image with the line
cv2.imshow('Image with Line', image_with_line)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# Step 1: Load the image
image = cv2.imread('path/to/your/image.jpg')

# Step 2: Define the circle parameters
center_coordinates = (250, 250)
radius = 100
color = (0, 0, 255)  # BGR color tuple
thickness = 2

# Step 3: Draw the circle on the image
image_with_circle = cv2.circle(image, center_coordinates, radius, color, thickness)

# Step 4: Display the image with the circle
cv2.imshow('Image with Circle', image_with_circle)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# Step 1: Load the image
image = cv2.imread('path/to/your/image.jpg')

# Step 2: Resize the image
resized_image = cv2.resize(image, (500, 400))

# Step 3: Flip the image horizontally
flipped_image = cv2.flip(resized_image, 1)

# Step 4: Display the original, resized, and flipped images
cv2.imshow('Original Image', image)
cv2.imshow('Resized Image', resized_image)
cv2.imshow('Flipped Image', flipped_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# Load the pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# Load the pre-trained face recognizer
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trained_model.yml')

# Load the image
image = cv2.imread('image.jpg')

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

# Perform face detection
faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5)

# Iterate over detected faces
for (x, y, w, h) in faces:
    # Draw a rectangle around the face
    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
    
    # Crop the face region from the image
    face_roi = gray_image[y:y+h, x:x+w]
    
    # Perform face recognition
    label, confidence = recognizer.predict(face_roi)
    
    # Get the predicted label name
    if label == 0:
        predicted_name = 'John Doe'
    elif label == 1:
        predicted_name = 'Jane Smith'
    else:
        predicted_name = 'Unknown'
    
    # Display the predicted name and confidence
    cv2.putText(image, f'{predicted_name} ({confidence:.2f})', (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

# Display the image with detected faces and recognized names
cv2.imshow('Face Detection and Recognition', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# Load the pre-trained Haar cascade classifiers for face and eyes
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')

# Initialize the video capture
cap = cv2.VideoCapture(0)

# Initialize variables for motion analysis
motion_history = None
mot_threshold = 32
mot_duration = 1.0

while True:
    # Read the current frame from the video capture
    ret, frame = cap.read()

    # Convert the frame to grayscale
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Perform face detection
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)

    # Draw rectangles around detected faces and eyes
    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
        roi_gray = gray[y:y+h, x:x+w]
        roi_color = frame[y:y+h, x:x+w]

        eyes = eye_cascade.detectMultiScale(roi_gray)
        for (ex, ey, ew, eh) in eyes:
            cv2.rectangle(roi_color, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2)

    # Perform motion analysis
    if motion_history is None:
        motion_history = np.zeros_like(gray).astype(float)
        prev_gray = gray

    # Calculate the absolute difference between the current and previous frame
    frame_diff = cv2.absdiff(prev_gray, gray)

    # Apply a threshold to the frame difference
    ret, motion_mask = cv2.threshold(frame_diff, mot_threshold, 1, cv2.THRESH_BINARY)

    # Update the motion history
    timestamp = cv2.getTickCount() / cv2.getTickFrequency()
    cv2.motempl.updateMotionHistory(motion_mask, motion_history, timestamp, mot_duration)

    # Normalize the motion history
    motion_history_norm = np.uint8(np.clip((motion_history - (timestamp - mot_duration)) / mot_duration, 0, 1) * 255)

    # Display the frames
    cv2.imshow('Object Detection and Tracking', frame)
    cv2.imshow('Motion Analysis', motion_history_norm)

    # Check for the 'q' key to quit the program
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

    # Update the previous frame
    prev_gray = gray

# Release the video capture and destroy all windows
cap.release()
cv2.destroyAllWindows()

import cv2
import numpy as np
import tensorflow as tf

# Load the pre-trained CNN model
model = tf.keras.applications.VGG16(weights='imagenet')

# Load the pre-trained image classification labels
with open('imagenet_labels.txt', 'r') as f:
    labels = f.read().splitlines()

# Load and preprocess the input image
image = cv2.imread('image.jpg')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (224, 224))
image = np.expand_dims(image, axis=0)
image = tf.keras.applications.vgg16.preprocess_input(image)

# Perform image classification
predictions = model.predict(image)
top_predictions = tf.keras.applications.vgg16.decode_predictions(predictions, top=5)[0]

# Display the predicted labels
for _, label, confidence in top_predictions:
    print(f'{label}: {confidence * 100:.2f}%')

import cv2
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

# Load the dataset
image_paths = ['cat1.jpg', 'cat2.jpg', 'dog1.jpg', 'dog2.jpg']
labels = [0, 0, 1, 1]  # 0 for cats, 1 for dogs

# Initialize lists to store the image features and labels
features = []
target = []

# Extract features from the images
for image_path in image_paths:
    image = cv2.imread(image_path)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    image = cv2.resize(image, (32, 32))  # Resize the image to a fixed size

    # Flatten the image into a 1-dimensional array
    image = image.flatten()

    # Append the flattened image to the features list
    features.append(image)

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)

# Create and train the classifier
classifier = SVC()
classifier.fit(X_train, y_train)

# Predict the labels for the test set
y_pred = classifier.predict(X_test)

# Calculate the accuracy of the classifier
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy * 100:.2f}%")

import cv2
import pytesseract

# Set the path to the Tesseract executable
pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"

# Load the image
image = cv2.imread('text_image.jpg')

# Preprocess the image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]

# Apply OCR to the preprocessed image
text = pytesseract.image_to_string(gray, config='--psm 6')

# Print the extracted text
print(text)

import cv2
import numpy as np
import tensorflow as tf

# Load the pre-trained VGG19 model
model = tf.keras.applications.VGG19(weights='imagenet', include_top=False)

# Define the content and style layers
content_layers = ['block4_conv2']
style_layers = ['block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1', 'block5_conv1']

# Load the content and style images
content_image = cv2.imread('content_image.jpg')
style_image = cv2.imread('style_image.jpg')

# Preprocess the images
preprocess = tf.keras.applications.vgg19.preprocess_input

content_image = cv2.resize(content_image, (224, 224))
content_image = np.expand_dims(content_image, axis=0)
content_image = preprocess(content_image)

style_image = cv2.resize(style_image, (224, 224))
style_image = np.expand_dims(style_image, axis=0)
style_image = preprocess(style_image)

# Convert the images to tensors
content_tensor = tf.convert_to_tensor(content_image)
style_tensor = tf.convert_to_tensor(style_image)

# Extract the content and style features
def get_feature_representations(model, content_tensor, style_tensor):
    content_outputs = []
    style_outputs = []

    for layer in content_layers:
        content_outputs.append(model.get_layer(layer).output)

    for layer in style_layers:
        style_outputs.append(model.get_layer(layer).output)

    content_model = tf.keras.models.Model(inputs=model.input, outputs=content_outputs)
    style_model = tf.keras.models.Model(inputs=model.input, outputs=style_outputs)

    content_features = content_model(content_tensor)
    style_features = style_model(style_tensor)

    return content_features, style_features

content_features, style_features = get_feature_representations(model, content_tensor, style_tensor)

# Calculate the Gram matrix
def gram_matrix(input_tensor):
    result = tf.linalg.einsum('bijc,bijd->bcd', input_tensor, input_tensor)
    input_shape = tf.shape(input_tensor)
    num_locations = tf.cast(input_shape[1] * input_shape[2], tf.float32)
    return result / num_locations

# Calculate the style loss
def style_loss(style, generated):
    style_gram = gram_matrix(style)
    generated_gram = gram_matrix(generated)
    channels = 3
    size = 224 * 224
    return tf.reduce_sum(tf.square(style_gram - generated_gram)) / (4.0 * (channels ** 2) * (size ** 2))

# Create the generated image as a trainable variable
generated_image = tf.Variable(content_image, dtype=tf.float32)

# Define the optimizer
optimizer = tf.optimizers.Adam(learning_rate=0.02, beta_1=0.99, epsilon=1e-1)

# Define the total variation loss
def total_variation_loss(image):
    x_deltas, y_deltas = tf.image.image_gradients(image)
    return tf.reduce_mean(tf.abs(x_deltas)) + tf.reduce_mean(tf.abs(y_deltas))

# Perform Neural Style Transfer
@tf.function()
def train_step(image):
    with tf.GradientTape() as tape:
        content_features, style_features = get_feature_representations(model, content_tensor, image)
        loss = tf.zeros(shape=())

        for content, style in zip(content_features, style_features):
                           # Compute the content loss
            loss += tf.reduce_mean(tf.square(content - content_features))

            # Compute the style loss
            loss += style_loss(style, style_features)

            # Add total variation regularization
            loss += total_variation_loss(image)

        # Compute the gradients of the loss with respect to the generated image
        gradients = tape.gradient(loss, image)

        # Update the generated image
        optimizer.apply_gradients([(gradients, image)])

        # Clip the pixel values to the valid range
        image.assign(tf.clip_by_value(image, clip_value_min=-1.0, clip_value_max=1.0))

# Number of iterations for optimization
num_iterations = 100

# Perform the Neural Style Transfer
for i in range(num_iterations):
    train_step(generated_image)

# Convert the generated image tensor to a numpy array
generated_image = generated_image.numpy()

# Rescale the pixel values to the range [0, 255]
generated_image = generated_image.reshape((224, 224, 3))
generated_image = ((generated_image + 1) / 2) * 255
generated_image = np.clip(generated_image, 0, 255).astype(np.uint8)

# Save the generated image
cv2.imwrite('generated_image.jpg', generated_image)

import cv2

# Load the pre-trained model
net = cv2.dnn.readNetFromDarknet('yolov3.cfg', 'yolov3.weights')

# Load the classes
classes = []
with open('coco.names', 'r') as f:
    classes = [line.strip() for line in f.readlines()]

# Set the input image and output layer names
image = cv2.imread('image.jpg')
height, width = image.shape[:2]
blob = cv2.dnn.blobFromImage(image, 1/255, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
output_layers = net.getUnconnectedOutLayersNames()

# Forward pass through the network
outputs = net.forward(output_layers)

# Process the outputs
class_ids = []
confidences = []
boxes = []

for output in outputs:
    for detection in output:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]

        if confidence > 0.5:
            center_x = int(detection[0] * width)
            center_y = int(detection[1] * height)
            w = int(detection[2] * width)
            h = int(detection[3] * height)

            # Calculate the top-left corner of the bounding box
            x = int(center_x - w / 2)
            y = int(center_y - h / 2)

            class_ids.append(class_id)
            confidences.append(float(confidence))
            boxes.append([x, y, w, h])

# Apply non-maximum suppression
indices = cv2.dnn.NMSBoxes(boxes, confidences, score_threshold=0.5, nms_threshold=0.4)

# Draw the bounding boxes and labels
for i in indices:
    i = i[0]
    x, y, w, h = boxes[i]
    label = classes[class_ids[i]]
    confidence = confidences[i]

    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
    cv2.putText(image, f'{label} {confidence:.2f}', (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

# Display the output image
cv2.imshow('Object Detection', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import caffe
import numpy as np
import cv2

# Load the pre-trained model and configure Caffe
caffe.set_mode_gpu()
net = caffe.Net('colorization_deploy_v2.prototxt', 'colorization_release_v2.caffemodel', caffe.TEST)

# Load the black and white image
bw_image = cv2.imread('bw_image.jpg', 0)

# Resize the image to match the input size of the network
resized_image = cv2.resize(bw_image, (224, 224))

# Convert the image to Lab color space
lab_image = cv2.cvtColor(resized_image, cv2.COLOR_GRAY2LAB)

# Split the Lab image into L and ab channels
L_channel = lab_image[:, :, 0]

# Subtract the mean value of the L channel (50) as required by the model
L_channel -= 50

# Reshape the L channel to match the network input shape
reshaped_L = L_channel.reshape(1, 1, 224, 224)

# Set the input data for the network
net.blobs['data_l'].data[...] = reshaped_L

# Forward pass through the network
net.forward()

# Retrieve the generated ab channels from the network output
ab_channels = net.blobs['class8_ab'].data[0, :, :, :]

# Resize the ab channels to match the size of the input image
resized_ab = cv2.resize(ab_channels.transpose((1, 2, 0)), (resized_image.shape[1], resized_image.shape[0]))

# Combine the L channel and ab channels to form the Lab image
lab_result = np.concatenate((L_channel[:, :, np.newaxis], resized_ab), axis=2)

# Convert the Lab image to BGR color space
color_result = cv2.cvtColor(lab_result, cv2.COLOR_LAB2BGR)

# Convert the color image to the original image size
final_result = cv2.resize(color_result, (bw_image.shape[1], bw_image.shape[0]))

# Display the colorized image
cv2.imshow('Colorized Image', final_result)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import numpy as np

# Load the image
image = cv2.imread('image.jpg')

# Split the image into RGB channels
blue_channel, green_channel, red_channel = cv2.split(image)

# Show the individual channels
cv2.imshow('Blue Channel', blue_channel)
cv2.imshow('Green Channel', green_channel)
cv2.imshow('Red Channel', red_channel)

# Merge the channels back together
merged_image = cv2.merge([blue_channel, green_channel, red_channel])

# Show the merged image
cv2.imshow('Merged Image', merged_image)

# Manipulate the channels (e.g., increase the red channel)
red_channel = cv2.add(red_channel, 50)

# Merge the modified channels back together
modified_image = cv2.merge([blue_channel, green_channel, red_channel])

# Show the modified image
cv2.imshow('Modified Image', modified_image)

# Wait for a key press and then close all windows
cv2.waitKey(0)
cv2.destroyAllWindows()