How to implement drawing object borders around animals in images and videos and labelling these borders with particular animal names?

/ / Published in Artificial Intelligence, EITC/AI/GVAPI Google Vision API, Understanding shapes and objects, Drawing object borders using pillow python library

The task of detecting animals in images and videos, drawing borders around them, and labeling these borders with the names of the animals involves a combination of techniques from the fields of computer vision and machine learning. This process can be broken down into several key steps: utilizing the Google Vision API for object detection, processing images in Python using the Pillow library, and applying appropriate labeling techniques. Here, we will walk through each of these steps in detail, providing a comprehensive guide on how to implement this functionality effectively.

Step 1: Setting Up the Environment

Before starting, ensure that you have Python installed on your machine. Python libraries such as Pillow (PIL Fork) and Google Client Library will be required. You can install these using pip:

bash
pip install pillow google-cloud-vision

You will also need to set up a Google Cloud account and enable the Vision API, creating credentials (API key or service account) to authenticate your requests.

Step 2: Using Google Vision API for Animal Detection

Google Vision API provides powerful pre-trained models for detecting objects in images. For our purpose, we'll focus on detecting animals within images. Here's how you can set up and make a request to the Vision API to detect objects:

1. Import the libraries and initialize the client:

python
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()

2. Load your image into the Vision API:

You can load an image from a local file or from an URI:

python
    def load_image(image_path):
        with open(image_path, 'rb') as image_file:
            content = image_file.read()
        image = vision.Image(content=content)
        return image

3. Request the API to detect objects:

python
    def detect_animals(image):
        objects = client.object_localization(image=image).localized_object_annotations
        return objects

4. Filter for animal detections:

The Vision API recognizes various object types, so you'll need to filter out non-animal objects:

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Step 3: Drawing Borders and Labeling Using Pillow


Once you have the animal objects detected and filtered, the next step is to draw borders around these objects and label them. Pillow, which is an updated fork of PIL, offers comprehensive capabilities for image processing.

1. Drawing the object borders:

    
python
    from PIL import Image, ImageDraw, ImageFont

    def draw_borders(image_path, objects):
        image = Image.open(image_path)
        draw = ImageDraw.Draw(image)

        # Loop through each object and draw a rectangle
        for obj in objects:
            box = [(vertex.x, vertex.y) for vertex in obj.bounding_poly.normalized_vertices]
            draw.rectangle(box, outline='red', width=3)
        
        return image
    
2. Labeling the borders:
 To label the borders with the animal names, you can use Pillow to draw text:
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Step 4: Integrating the Components


Now, integrate all the components into a single function that processes an image, detects and filters animals, and then draws labeled borders around them:


python
def process_image(image_path):
    image = load_image(image_path)
    detected_objects = detect_animals(image)
    animal_objects = filter_animals(detected_objects)
    image_with_borders = draw_borders(image_path, animal_objects)
    final_image = label_objects(image_with_borders, animal_objects)
    final_image.show()

This function can be called with the path to an image, and it will display the image with detected animals highlighted and labeled.
Practical Considerations and Limitations
While the outlined approach is robust for basic use cases, several considerations should be kept in mind:
 - Accuracy of Detection: The accuracy of animal detection depends heavily on the quality of the image and the capabilities of the Google Vision API's pre-trained model.
 - Performance: Processing high-resolution images or a large volume of images can be computationally intensive and may require optimization or cloud-based processing.
 - Cost: Google Vision API charges based on the number of requests. Frequent or large-scale use may incur significant costs.
This guide provides a foundational approach to detecting animals in images and videos, drawing borders around them, and labeling these borders with the names of the animals using Python, Google Vision API, and Pillow. By following the steps outlined, one can build a functional prototype for animal detection and labeling in multimedia content.
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