Computer Vision (visão computacional) is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data. It has a wide range of applications, from facial recognition to autonomous vehicles. In the Apple environment, particularly macOS, implementing computer vision can be efficiently done using Python and the OpenCV library. This article will guide you through the process of setting up and running basic computer vision tasks on macOS.

Examples:

1. Setting Up the Environment:
To start, ensure you have Python installed on your macOS. You can check this by opening the Terminal and typing:

   python3 --version

If Python is not installed, you can download it from the official Python website.

2. Installing OpenCV:
Next, install OpenCV using pip. Open the Terminal and run:

   pip3 install opencv-python

3. Basic Image Processing with OpenCV:
Create a Python script to read and display an image using OpenCV. Save the following code in a file named image_processing.py:

   import cv2



   # Read the image

   image = cv2\.imread('path_to_your_image.jpg')



   # Display the image

   cv2\.imshow('Image', image)

   cv2\.waitKey(0)

   cv2\.destroyAllWindows()

Replace 'path_to_your_image.jpg' with the actual path to your image file.

4. Running the Script:
Navigate to the directory where your script is saved and run it via the Terminal:

   python3 image_processing.py

5. Advanced Computer Vision Tasks:
For more advanced tasks like face detection, you can use pre-trained models provided by OpenCV. Here's an example of a script that detects faces in an image:

   import cv2



   # Load the pre-trained model

   face_cascade = cv2\.CascadeClassifier(cv2\.data.haarcascades + 'haarcascade_frontalface_default.xml')



   # Read the image

   image = cv2\.imread('path_to_your_image.jpg')

   gray_image = cv2\.cvtColor(image, cv2\.COLOR_BGR2GRAY)



   # Detect faces

   faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))



   # Draw rectangles around the faces

   for (x, y, w, h) in faces:

       cv2\.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)



   # Display the output

   cv2\.imshow('Face Detection', image)

   cv2\.waitKey(0)

   cv2\.destroyAllWindows()