77
88app = Flask (__name__ )
99
10+ face_cascade = cv2 .CascadeClassifier (cv2 .data .haarcascades + "haarcascade_frontalface_default.xml" )
11+ 1012@app .route ('/' )
1113def home ():
1214 return jsonify ({'message' :'Welcome to Flask Apis' })
1315
14- @app .route ('/img ' ,methods = ['POST' ])
16+ @app .route ('/image_Compare ' ,methods = ['POST' ])
1517def predict ():
1618 file1 = request .files ['file1' ]
1719 file2 = request .files ['file2' ]
@@ -37,6 +39,47 @@ def predict():
3739 # Return the similarity percentage in a JSON response
3840 return jsonify ({'similarity_percentage' : similarity_percentage })
3941
42+ @app .route ('/face_recognize' ,methods = ['POST' ])
43+ def predictface ():
44+ # Get the uploaded files from the request
45+ file1 = request .files ['file1' ]
46+ file2 = request .files ['file2' ]
47+ 48+ # 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 )
51+ 52+ # Convert the images to grayscale
53+ gray_img1 = cv2 .cvtColor (img1 , cv2 .COLOR_BGR2GRAY )
54+ gray_img2 = cv2 .cvtColor (img2 , cv2 .COLOR_BGR2GRAY )
55+ 56+ # 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 )
59+ 60+ # Compare only the first detected face in each image
61+ if len (faces1 ) > 0 and len (faces2 ) > 0 :
62+ x1 , y1 , w1 , h1 = faces1 [0 ]
63+ x2 , y2 , w2 , h2 = faces2 [0 ]
64+ 65+ # Extract the face regions from the images
66+ face1 = gray_img1 [y1 :y1 + h1 , x1 :x1 + w1 ]
67+ face2 = gray_img2 [y2 :y2 + h2 , x2 :x2 + w2 ]
68+ 69+ # Resize the face regions to the same dimensions
70+ resized_face1 = cv2 .resize (face1 , (face2 .shape [1 ], face2 .shape [0 ]))
71+ 72+ # Calculate the structural similarity index between the face regions
73+ score = ssim (resized_face1 , face2 , full = True )[0 ]
74+ 75+ # Convert the similarity score to a percentage
76+ similarity_percentage = score * 100
77+ 78+ # Return the similarity percentage in a JSON response
79+ return jsonify ({'similarity_percentage' : similarity_percentage })
80+ 81+ else :
82+ return jsonify ({'similarity_percentage' : 'Could not detect faces in both images.' })
4083
4184if __name__ == '__main__' :
4285 app .run (debug = True )
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