1+ import numpy as np
2+ from flask import Flask , request , jsonify , render_template ,json
3+ import cv2
4+ from skimage .metrics import structural_similarity as ssim
5+ 6+ 7+ 8+ app = Flask (__name__ )
9+ 10+ @app .route ('/' )
11+ def home ():
12+ return jsonify ({'message' :'Welcome to Flask Apis' })
13+ 14+ @app .route ('/img' ,methods = ['POST' ])
15+ def predict ():
16+ file1 = request .files ['file1' ]
17+ file2 = request .files ['file2' ]
18+ 19+ # Read the images using OpenCV
20+ img1 = cv2 .imdecode (np .frombuffer (file1 .read (), np .uint8 ), cv2 .IMREAD_COLOR )
21+ img2 = cv2 .imdecode (np .frombuffer (file2 .read (), np .uint8 ), cv2 .IMREAD_COLOR )
22+ 23+ # Resize the images to 256x256 pixels
24+ img1 = cv2 .resize (img1 , (256 , 256 ))
25+ img2 = cv2 .resize (img2 , (256 , 256 ))
26+ 27+ # Convert the images to grayscale
28+ gray_img1 = cv2 .cvtColor (img1 , cv2 .COLOR_BGR2GRAY )
29+ gray_img2 = cv2 .cvtColor (img2 , cv2 .COLOR_BGR2GRAY )
30+ 31+ # Calculate the structural similarity index between the images
32+ score = ssim (gray_img1 , gray_img2 , full = True )[0 ]
33+ 34+ # Convert the similarity score to a percentage
35+ similarity_percentage = score * 100
36+ 37+ # Return the similarity percentage in a JSON response
38+ return jsonify ({'similarity_percentage' : similarity_percentage })
39+ 40+ 41+ if __name__ == '__main__' :
42+ app .run (debug = True )
0 commit comments