11import numpy as np
2- from flask import Flask , request , jsonify , render_template ,json
2+ from flask import Flask , request , jsonify , render_template ,json
33import cv2
44from skimage .metrics import structural_similarity as ssim
55
66
7- 87app = Flask (__name__ )
98
10- face_cascade = cv2 .CascadeClassifier (cv2 .data .haarcascades + "haarcascade_frontalface_default.xml" )
9+ face_cascade = cv2 .CascadeClassifier (
10+ cv2 .data .haarcascades + "haarcascade_frontalface_default.xml" )
11+ 1112
1213@app .route ('/' )
1314def home ():
14- return jsonify ({'message' :'Welcome to Flask Apis' })
15+ return jsonify ({'message' : 'Welcome to Flask Apis' })
16+ 1517
16- @app .route ('/image_Compare' ,methods = ['POST' ])
18+ @app .route ('/image_Compare' ,methods = ['POST' ])
1719def predict ():
1820 file1 = request .files ['file1' ]
1921 file2 = request .files ['file2' ]
2022
2123 # Read the images using OpenCV
22- img1 = cv2 .imdecode (np .frombuffer (file1 .read (), np .uint8 ), cv2 .IMREAD_COLOR )
23- img2 = cv2 .imdecode (np .frombuffer (file2 .read (), np .uint8 ), cv2 .IMREAD_COLOR )
24+ img1 = cv2 .imdecode (np .frombuffer (
25+ file1 .read (), np .uint8 ), cv2 .IMREAD_COLOR )
26+ img2 = cv2 .imdecode (np .frombuffer (
27+ file2 .read (), np .uint8 ), cv2 .IMREAD_COLOR )
2428
2529 # Resize the images to 256x256 pixels
2630 img1 = cv2 .resize (img1 , (256 , 256 ))
@@ -37,25 +41,30 @@ def predict():
3741 similarity_percentage = score * 100
3842
3943 # Return the similarity percentage in a JSON response
40- return jsonify ({'similarity_percentage' : similarity_percentage })
44+ return jsonify ({'similarity_percentage' : similarity_percentage })
4145
42- @app .route ('/face_recognize' ,methods = ['POST' ])
46+ 47+ @app .route ('/face_recognize' , methods = ['POST' ])
4348def predictface ():
44- # Get the uploaded files from the request
49+ # Get the uploaded files from the request
4550 file1 = request .files ['file1' ]
4651 file2 = request .files ['file2' ]
4752
4853 # Read the images using OpenCV directly from the request files
49- img1 = cv2 .imdecode (np .frombuffer (file1 .read (), np .uint8 ), cv2 .IMREAD_COLOR )
50- img2 = cv2 .imdecode (np .frombuffer (file2 .read (), np .uint8 ), cv2 .IMREAD_COLOR )
54+ img1 = cv2 .imdecode (np .frombuffer (
55+ file1 .read (), np .uint8 ), cv2 .IMREAD_COLOR )
56+ img2 = cv2 .imdecode (np .frombuffer (
57+ file2 .read (), np .uint8 ), cv2 .IMREAD_COLOR )
5158
5259 # Convert the images to grayscale
5360 gray_img1 = cv2 .cvtColor (img1 , cv2 .COLOR_BGR2GRAY )
5461 gray_img2 = cv2 .cvtColor (img2 , cv2 .COLOR_BGR2GRAY )
5562
5663 # Detect faces in the images
57- faces1 = face_cascade .detectMultiScale (gray_img1 , scaleFactor = 1.1 , minNeighbors = 5 )
58- faces2 = face_cascade .detectMultiScale (gray_img2 , scaleFactor = 1.1 , minNeighbors = 5 )
64+ faces1 = face_cascade .detectMultiScale (
65+ gray_img1 , scaleFactor = 1.1 , minNeighbors = 5 )
66+ faces2 = face_cascade .detectMultiScale (
67+ gray_img2 , scaleFactor = 1.1 , minNeighbors = 5 )
5968
6069 # Compare only the first detected face in each image
6170 if len (faces1 ) > 0 and len (faces2 ) > 0 :
@@ -81,5 +90,6 @@ def predictface():
8190 else :
8291 return jsonify ({'similarity_percentage' : 'Could not detect faces in both images.' })
8392
93+ 8494if __name__ == '__main__' :
85- app .run (debug = True )
95+ app .run (debug = True )
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