📚 Learn OpenCV with Python by Examples (2023)

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📚 Hands-On ML Projects with OpenCV (2023)

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📚 Learn OpenCV with Python by Examples (2023)

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📚 Mastering OpenCV with Python (2023)

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📚 Neural Network Computer Vision with OpenCV 5 (2024)

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📚 OPENCV | Python for Computer Vision (2024)

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📚 Advanced Image Processing with Python and OpenCV (2024)

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📚 Practical Python and OpenCV (2018)

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✨ Improving Text Detection Speed with OpenCV and GPUs ✨

📖 In this tutorial, you will learn to improve text detection speed with OpenCV and GPUs. This tutorial is the last in a 4-part series on OCR with Python: Multi-Column Table OCR OpenCV Fast Fourier Transform (FFT) for Blur Detection in…...

🏷️ #GraphicsProcessingUnitsGPU #OpenCV #OpticalCharacterRecognitionOCR #Python #Tutorials

# Real-World Case Study: E-commerce Product Pipeline
import boto3
from PIL import Image
import io

def process_product_image(s3_bucket, s3_key):
    # 1. Download from S3
    s3 = boto3.client('s3')
    response = s3.get_object(Bucket=s3_bucket, Key=s3_key)
    img = Image.open(io.BytesIO(response['Body'].read()))
    
    # 2. Standardize dimensions
    img = img.convert("RGB")
    img = img.resize((1200, 1200), Image.LANCZOS)
    
    # 3. Remove background (simplified)
    # In practice: use rembg or AWS Rekognition
    img = remove_background(img)
    
    # 4. Generate variants
    variants = {
        "web": img.resize((800, 800)),
        "mobile": img.resize((400, 400)),
        "thumbnail": img.resize((100, 100))
    }
    
    # 5. Upload to CDN
    for name, variant in variants.items():
        buffer = io.BytesIO()
        variant.save(buffer, "JPEG", quality=95)
        s3.upload_fileobj(
            buffer, 
            "cdn-bucket", 
            f"products/{s3_key.split('/')[-1].split('.')[0]}_{name}.jpg",
            ExtraArgs={'ContentType': 'image/jpeg', 'CacheControl': 'max-age=31536000'}
        )
    
    # 6. Generate WebP version
    webp_buffer = io.BytesIO()
    img.save(webp_buffer, "WEBP", quality=85)
    s3.upload_fileobj(webp_buffer, "cdn-bucket", f"products/{s3_key.split('/')[-1].split('.')[0]}.webp")

process_product_image("user-uploads", "products/summer_dress.jpg")

By: @DataScienceM 👁

#Python #ImageProcessing #ComputerVision #Pillow #OpenCV #MachineLearning #CodingInterview #DataScience #Programming #TechJobs #DeveloperTips #AI #DeepLearning #CloudComputing #Docker #BackendDevelopment #SoftwareEngineering #CareerGrowth #TechTips #Python3

#YOLOv8 #ComputerVision #ObjectDetection #IndustrialAI #Python

Applying YOLOv8 for Industrial Automation: Counting Plastic Bottles

This lesson will guide you through a complete computer vision project using YOLOv8. The goal is to detect and count plastic bottles in an image from an industrial setting, such as a conveyor belt or a storage area.

---

Step 1: Setup and Installation

First, we need to install the necessary libraries. The ultralytics library provides the YOLOv8 model, and opencv-python is essential for image processing tasks.

#Setup #Installation

# Open your terminal or command prompt and run this command:
pip install ultralytics opencv-python

---

Step 2: Loading the Model and the Target Image

We will load a pre-trained YOLOv8 model. These models are trained on the large COCO dataset, which already knows how to identify common objects like 'bottle'. Then, we'll load our industrial image. Ensure you have an image named factory_bottles.jpg in your project folder.

#ModelLoading #DataHandling

import cv2
from ultralytics import YOLO

# Load a pre-trained YOLOv8 model (yolov8n.pt is the smallest and fastest)
model = YOLO('yolov8n.pt')

# Load the image from the industrial setting
image_path = 'factory_bottles.jpg' # Make sure this image is in your directory
img = cv2.imread(image_path)

# A quick check to ensure the image was loaded correctly
if img is None:
    print(f"Error: Could not load image at {image_path}")
else:
    print("YOLOv8 model and image loaded successfully.")

---

Step 3: Performing Detection on the Image

With the model and image loaded, we can now run the detection. The ultralytics library makes this process incredibly simple. The model will analyze the image and identify all the objects it recognizes.

#Inference #ObjectDetection

# Run the model on the image to get detection results
results = model(img)

print("Detection complete. Processing results...")

---

Step 4: Filtering and Counting the Bottles

The model detects many types of objects. Our task is to go through the results, filter for only the 'bottle' class, and count how many there are. We'll also store the locations (bounding boxes) of each detected bottle for visualization.

#DataProcessing #Filtering

# Initialize a counter for the bottles
bottle_count = 0
bottle_boxes = []

# The model's results is a list, so we loop through it
for result in results:
    # Each result has a 'boxes' attribute with the detections
    boxes = result.boxes
    for box in boxes:
        # Get the class ID of the detected object
        class_id = int(box.cls)
        # Check if the class name is 'bottle'
        if model.names[class_id] == 'bottle':
            bottle_count += 1
            # Store the bounding box coordinates (x1, y1, x2, y2)
            bottle_boxes.append(box.xyxy[0])

print(f"Total plastic bottles detected: {bottle_count}")

---

Step 5: Visualizing the Results

A number is good, but seeing what the model detected is better. We will draw the bounding boxes and the final count directly onto the image to create a clear visual output.

#Visualization #OpenCV