Integrate the following two snippets:
import cv2

# Define the pedestrian, cyclist, and road user classifier
pedestrian_classifier = cv2.CascadeClassifier("pedestrian_classifier.xml")
cyclist_classifier = cv2.CascadeClassifier("cyclist_classifier.xml")
road_user_classifier = cv2.CascadeClassifier("road_user_classifier.xml")

# Define the obstacle detection function
def detect_obstacles(frame):
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    pedestrians = pedestrian_classifier.detectMultiScale(gray, 1.3, 5)
    cyclists = cyclist_classifier.detectMultiScale(gray, 1.3, 5)
    road_users = road_user_classifier.detectMultiScale(gray, 1.3, 5)
    return pedestrians, cyclists, road_users

# Example usage
frame = cv2.imread("frame.jpg")
pedestrians, cyclists, road_users = detect_obstacles(frame)
print("Detected obstacles (image detection):")
print("Pedestrians:", pedestrians)
print("Cyclists:", cyclists)
print("Road users:", road_users)

def process_sonar_data(data, weight):
  distances = []
  for i in range(len(data)):
    distance = calculate_distance(data[i])
    distances.append(distance * weight)
  return distances

def calculate_distance(datapoint):
  distance = (datapoint[0]**2 + datapoint[1]**2 + datapoint[2]**2)**0.5
  return distance

# Example usage
sonar_data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
weight = 0.5
distances = process_sonar_data(sonar_data, weight)
print("Detected obstacles (sonar detection):")
print("Distances:", distances)




import cv2
import numpy as np

# Capture video from a camera
cap = cv2.VideoCapture(0)

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

    # Convert the frame to grayscale and apply Gaussian blur
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (5,5), 0)

    # Apply Canny edge detection
    edges = cv2.Canny(blur, 50, 150)

    # Define a region of interest for lane detection
    mask = np.zeros_like(edges)
    ignore_mask_color = 255
    imshape = frame.shape
    vertices = np.array([[(0,imshape[0]),(450, 330), (490, 330), (imshape[1],imshape[0])]], dtype=np.int32)
    cv2.fillPoly(mask, vertices, ignore_mask_color)
    masked_edges = cv2.bitwise_and(edges, mask)

    # Hough lines parameters
    rho = 1
    theta = np.pi/180
    threshold = 15
    min_line_length = 40
    max_line_gap = 20
    line_image = np.copy(frame)*0

    # Hough lines detection
    lines = cv2.HoughLinesP(masked_edges, rho, theta, threshold, np.array([]), min_line_length, max_line_gap)

    # Draw lines on the image
    for line in lines:
        for x1,y1,x2,y2 in line:
            cv2.line(line_image, (x1, y1), (x2, y2), (255, 0, 0), 10)

    # Combine the original frame with the lines image
    color_edges = np.dstack((edges, edges, edges)) 
    lines_edges = cv2.addWeighted(frame, 0.8, line_image, 1, 0) 

    # Display the resulting frame
    cv2.imshow("Lane Detection and Tracking", lines_edges)

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

# Release the video capture and destroy the windows
cap.release()
cv2.destroyAllWindows()
The two snippets integrate as follows:

python
Copy code
import cv2
import numpy as np

# Define the pedestrian, cyclist, and road user classifier
pedestrian_classifier = cv2.CascadeClassifier("pedestrian_classifier.xml")
cyclist_classifier = cv2.CascadeClassifier("cyclist_classifier.xml")
road_user_classifier = cv2.CascadeClassifier("road_user_classifier.xml")

# Define the obstacle detection function
def detect_obstacles(frame):
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    pedestrians = pedestrian_classifier.detectMultiScale(gray, 1.3, 5)
    cyclists = cyclist_classifier.detectMultiScale(gray, 1.3, 5)
    road_users = road_user_classifier.detectMultiScale(gray, 1.3, 5)
    return pedestrians, cyclists, road_users

def process_sonar_data(data, weight):
  distances = []
  for i in range(len(data)):
    distance = calculate_distance(data[i])
    distances.append(distance * weight)
  return distances

def calculate_distance(datapoint):
  distance = (datapoint[0]**2 + datapoint[1]**2 + datapoint[2]**2)**0.5
  return distance

# Capture video from a camera
cap = cv2.VideoCapture(0)

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

    # Convert the frame to grayscale and apply Gaussian blur
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (5,5), 0)

    # Apply Canny edge detection
    edges = cv2.Canny(blur, 50, 150)

    # Define a region of interest for lane detection
    mask = np.zeros_like(edges)
    ignore_mask_color = 255
    imshape = frame.shape
    vertices = np.array([[(0,imshape[0]),(450, 330), (490, 330), (imshape[1],imshape[0])]], dtype=np.int32)
    cv2.fillPoly(mask, vertices, ignore_mask_color)
    masked_edges = cv2.bitwise_and(edges, mask)

    # Hough lines parameters
    rho = 1
    theta = np.pi/180
    threshold = 15
    min_line_length = 40
    max_line_gap = 20
    line_image = np.copy(frame)*0

    # Hough lines detection
    lines = cv2.HoughLinesP(masked_edges, rho, theta, threshold, np.array([]), min_line_length, max_line_gap)

    # Draw lines on the image
    for line in lines:
        for x1,y1,x2,y2 in line:
            cv2.line(line_image



Josef K
your last message stopped at "# Draw lines on the image
    for line in lines:
        for x1,y1,x2,y2 in line:
            cv2.line(line_image"

For task: Integrate the following two snippets:
import cv2

# Define the pedestrian, cyclist, and road user classifier
pedestrian_classifier = cv2.CascadeClassifier("pedestrian_classifier.xml")
cyclist_classifier = cv2.CascadeClassifier("cyclist_classifier.xml")
road_user_classifier = cv2.CascadeClassifier("road_user_classifier.xml")

# Define the obstacle detection function
def detect_obstacles(frame):
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    pedestrians = pedestrian_classifier.detectMultiScale(gray, 1.3, 5)
    cyclists = cyclist_classifier.detectMultiScale(gray, 1.3, 5)
    road_users = road_user_classifier.detectMultiScale(gray, 1.3, 5)
    return pedestrians, cyclists, road_users

# Example usage
frame = cv2.imread("frame.jpg")
pedestrians, cyclists, road_users = detect_obstacles(frame)
print("Detected obstacles (image detection):")
print("Pedestrians:", pedestrians)
print("Cyclists:", cyclists)
print("Road users:", road_users)

def process_sonar_data(data, weight):
  distances = []
  for i in range(len(data)):
    distance = calculate_distance(data[i])
    distances.append(distance * weight)
  return distances

def calculate_distance(datapoint):
  distance = (datapoint[0]**2 + datapoint[1]**2 + datapoint[2]**2)**0.5
  return distance

# Example usage
sonar_data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
weight = 0.5
distances = process_sonar_data(sonar_data, weight)
print("Detected obstacles (sonar detection):")
print("Distances:", distances)




import cv2
import numpy as np

# Capture video from a camera
cap = cv2.VideoCapture(0)

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

    # Convert the frame to grayscale and apply Gaussian blur
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (5,5), 0)

    # Apply Canny edge detection
    edges = cv2.Canny(blur, 50, 150)

    # Define a region of interest for lane detection
    mask = np.zeros_like(edges)
    ignore_mask_color = 255
    imshape = frame.shape
    vertices = np.array([[(0,imshape[0]),(450, 330), (490, 330), (imshape[1],imshape[0])]], dtype=np.int32)
    cv2.fillPoly(mask, vertices, ignore_mask_color)
    masked_edges = cv2.bitwise_and(edges, mask)

    # Hough lines parameters
    rho = 1
    theta = np.pi/180
    threshold = 15
    min_line_length = 40
    max_line_gap = 20
    line_image = np.copy(frame)*0

    # Hough lines detection
    lines = cv2.HoughLinesP(masked_edges, rho, theta, threshold, np.array([]), min_line_length, max_line_gap)

    # Draw lines on the image
    for line in lines:
        for x1,y1,x2,y2 in line:
            cv2.line(line_image, (x1, y1), (x2, y2), (255, 0, 0), 10)

    # Combine the original frame with the lines image
    color_edges = np.dstack((edges, edges, edges)) 
    lines_edges = cv2.addWeighted(frame, 0.8, line_image, 1, 0) 

    # Display the resulting frame
    cv2.imshow("Lane Detection and Tracking", lines_edges)

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

# Release the video capture and destroy the windows
cap.release()
cv2.destroyAllWindows()
Here is an integration of the two snippets:

python
Copy code
import cv2
import numpy as np

# Define the pedestrian, cyclist, and road user classifier
pedestrian_classifier = cv2.CascadeClassifier("pedestrian_classifier.xml")
cyclist_classifier = cv2.CascadeClassifier("cyclist_classifier.xml")
road_user_classifier = cv2.CascadeClassifier("road_user_classifier.xml")

# Define the obstacle detection function
def detect_obstacles(frame):
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    pedestrians = pedestrian_classifier.detectMultiScale(gray, 1.3, 5)
    cyclists = cyclist_classifier.detectMultiScale(gray, 1.3, 5)
    road_users = road_user_classifier.detectMultiScale(gray, 1.3, 5)
    return pedestrians, cyclists, road_users

def process_sonar_data(data, weight):
  distances = []
  for i in range(len(data)):
    distance = calculate_distance(data[i])
    distances.append(distance * weight)
  return distances

def calculate_distance(datapoint):
  distance = (datapoint[0]**2 + datapoint[1]**2 + datapoint[2]**2)**0.5
  return distance

# Capture video from a camera
cap = cv2.VideoCapture(0)

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

    # Image detection
    pedestrians, cyclists, road_users = detect_obstacles(frame)
    print("Detected obstacles (image detection):")
    print("Pedestrians:", pedestrians)
    print("Cyclists:", cyclists)
    print("Road users:", road_users)

    # Sonar detection
    sonar_data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
    weight = 0.5
    distances = process_sonar_data(sonar_data, weight)
    print("Detected obstacles (sonar detection):")
    print("Distances:", distances)

    # Convert the frame to grayscale and apply Gaussian blur
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (5,5), 0)

    # Apply Canny edge detection
    edges = cv2.Canny(blur, 50, 150)

    # Define a region of interest for lane detection
    mask = np.zeros_like(edges)
    ignore_mask_color = 255
    imshape = frame.shape
    vertices = np.array([[(0,imshape[0]),(450, 330), (490, 330), (imshape[1],imshape[0])]], dtype=np.int32)
    cv2.fillPoly(mask, vertices, ignore_mask_color)
    masked_edges = cv2.bitwise_and(edges, mask)
    
    ## Copyright Josef Kulovany and zchg.org where applicable - THIS LINE MUST BE SHOWN IF ANY APPLICABLE SNIPPET OR DERIVATIVE THEREIN IS BORROWED PER LICENSE AND COPYRIGHT LAW
