Un khey devs pour me dire si le code que gpt m'a pondu est réalisable?
les deux question :
-How Can i create a code based ai that will show all related grafiti with a photo sample
-Can you show me the code in python to do that?
Le code en python
- Import required libraries
import tensorflow as tf
import numpy as np
import cv2
import os
- Define the paths to the graffiti image dataset
DATASET_PATH = '/path/to/dataset'
IMAGES_PATH = os.path.join(DATASET_PATH, 'images')
LABELS_PATH = os.path.join(DATASET_PATH, 'labels.txt')
- Load the graffiti image dataset and labels
with open(LABELS_PATH, 'r') as f:
labels =
images = []
for filename in os.listdir(IMAGES_PATH):
img = cv2.imread(os.path.join(IMAGES_PATH, filename))
img = cv2.resize(img, (224, 224)) # Resize image to fit model input
images.append(img)
images = np.array(images)
- Load a pre-trained image recognition model
model = tf.keras.applications.MobileNetV2(weights='imagenet')
- Define a function to extract features from an image
def extract_features(image):
image = np.expand_dims(image, axis=0)
image = tf.keras.applications.mobilenet_v2.preprocess_input(image)
features = model.predict(image)
return features
- Extract features from all images in the dataset
features = []
for image in images:
feature = extract_features(image)
features.append(feature)
features = np.array(features)
- Define a function to find similar images
def find_similar_images(query_image, num_results=5):
query_feature = extract_features(query_image)
distances = np.linalg.norm(features - query_feature, axis=1)
indices = np.argsort(distances)[:num_results]
return images[indices]
- Test the function with a sample image
query_image = cv2.imread('/path/to/sample_image.jpg')
query_image = cv2.resize(query_image, (224, 224))
similar_images = find_similar_images(query_image)
for similar_image in similar_images:
cv2.imshow('Similar Image', similar_image)
cv2.waitKey(0)
