├── Object Recognition for Pre-trained model.py
└── README.md


/Object Recognition for Pre-trained model.py:
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 1 | from PIL import Image
 2 | import numpy as np
 3 | 
 4 | def load_image(image_path):
 5 |     return Image.open(image_path).convert('L')  # Convert to grayscale
 6 | 
 7 | def image_to_array(image):
 8 |     return np.array(image)
 9 | 
10 | def normalize_image(image_array):
11 |     mean = np.mean(image_array)
12 |     std = np.std(image_array)
13 |     return (image_array - mean) / std
14 | 
15 | def save_sub_image(image_array, top_left, size, file_name):
16 |     y, x = top_left
17 |     h, w = size
18 |     sub_image = image_array[y:y+h, x:x+w]
19 |     Image.fromarray(sub_image).save(file_name)
20 | 
21 | def match_template(image, template):
22 |     image_array = normalize_image(image_to_array(image))
23 |     template_array = normalize_image(image_to_array(template))
24 |     
25 |     img_h, img_w = image_array.shape
26 |     tmpl_h, tmpl_w = template_array.shape
27 |     
28 |     # Use FFT to compute the correlation
29 |     correlation = np.fft.fft2(image_array) * np.conj(np.fft.fft2(template_array, s=image_array.shape))
30 |     correlation = np.fft.ifft2(correlation).real  # Inverse FFT to get the correlation result
31 |     
32 |     # Normalize the correlation map
33 |     correlation = correlation / (tmpl_h * tmpl_w)  # Adjust for the template size
34 |     
35 |     # Find the best match
36 |     best_match = np.unravel_index(np.argmax(correlation), correlation.shape)
37 |     best_score = correlation[best_match]
38 | 
39 |     # Save the best matching region for visualization
40 |     save_sub_image(image_array, best_match, (tmpl_h, tmpl_w), 'best_match_region.jpg')
41 |     
42 |     return best_match, best_score
43 | 
44 | # Provide the paths to your image and template
45 | image_path = "C:\\Users\\Lenovo\\Downloads\\download.jpeg"
46 | template_path = "C:\\Users\\Lenovo\\Downloads\\download (1).jpeg"
47 | 
48 | # Load the image and template
49 | image = load_image(image_path)
50 | template = load_image(template_path)
51 | 
52 | # Perform template matching
53 | best_match, best_score = match_template(image, template)
54 | 
55 | # Display the result
56 | if best_match:
57 |     print(f"Best match found at: {best_match} with score: {best_score}")
58 | else:
59 |     print("No match found")
60 | print(f"Image size: {image.size}")
61 | print(f"Template size: {template.size}")
62 | 


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/README.md:
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1 | # Object-recognition-for-pre-trained-model


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