Computer Vision

In Shopzzy AI, we utilize Computer Vision technology to analyze visual data and recognize images of products. This allows our AI to provide more accurate and effective product recommendations based on a user's preferences. By using Computer Vision, we can identify visual patterns and features in images, such as color, shape, and texture, that are important to a user's preferences and use this information to recommend similar products.

Furthermore, Computer Vision can help improve the overall user experience by allowing users to search for products using images rather than text. For example, a user can take a photo of a product they like and our AI can use Computer Vision to identify the product and provide recommendations for similar items.

At Shopzzy AI, we are constantly exploring and developing new ways to incorporate Computer Vision into our platform to create a more efficient and personalized shopping experience for our users.

This script loads a pre-trained ResNet50 model, which is a deep convolutional neural network used for image recognition. The predict_image() function takes an image path as input, loads the image, preprocesses it, and then uses the ResNet50 model to predict the most likely category for the product in the image. The script then prints out the predicted category for the product. This computer vision technology used in Shopzzy AI to analyze product images and provide more accurate and personalized recommendations to users.

Simple Shopzzy AI script utilizing computer vision technology

// Simple Shopzzy AI script utilizing computer vision technology
import tensorflow as tf
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np

# Load pre-trained ResNet50 model
model = ResNet50(weights='imagenet')

# Define a function for image recognition
def predict_image(image_path):
    # Load image
    img = image.load_img(image_path, target_size=(224, 224))
    # Convert image to array and preprocess
    img_array = image.img_to_array(img)
    img_array = np.expand_dims(img_array, axis=0)
    img_array = preprocess_input(img_array)
    # Predict class probabilities
    predictions = model.predict(img_array)
    # Decode predictions
    decoded_predictions = decode_predictions(predictions, top=3)[0]
    # Return top prediction
    return decoded_predictions[0][1]

# Test image recognition function
product_image = 'product_image.jpg'
predicted_category = predict_image(product_image)
print(f"Product category: {predicted_category}")

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Last updated 2 years ago

// Simple Shopzzy AI script utilizing computer vision technology import tensorflow as tf from tensorflow.keras.applications.resnet50 import ResNet50 from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions import numpy as np # Load pre-trained ResNet50 model model = ResNet50(weights='imagenet') # Define a function for image recognition def predict_image(image_path): # Load image img = image.load_img(image_path, target_size=(224, 224)) # Convert image to array and preprocess img_array = image.img_to_array(img) img_array = np.expand_dims(img_array, axis=0) img_array = preprocess_input(img_array) # Predict class probabilities predictions = model.predict(img_array) # Decode predictions decoded_predictions = decode_predictions(predictions, top=3)[0] # Return top prediction return decoded_predictions[0][1] # Test image recognition function product_image = 'product_image.jpg' predicted_category = predict_image(product_image) print(f"Product category: {predicted_category}")