蒾i%hdZddlZddlZddlZddlmZmZeje Z GddZ y)zk Face preprocessing pipeline using OpenCV. Handles face detection, alignment, cropping, and normalization. N)ImageExifTagsc peZdZdZdZdej fdZdejdejfdZ dej de fdZ de fd Z ddej d e d edej fd Zd ej dej fdZdej dej fdZdej fdZy)FacePreprocessoraM Preprocesses images for face recognition: 1. Load and convert to RGB 2. Apply EXIF rotation correction 3. Detect face using Haarcascade 4. Crop the largest face with padding 5. Align using eye landmarks 6. Resize to 160x160 7. Apply histogram equalization 8. Normalize pixel values to [0, 1] ctjtjjdz|_tjtjjdz|_y)Nz#haarcascade_frontalface_default.xmlzhaarcascade_eye.xml)cv2CascadeClassifierdata haarcascades face_cascade eye_cascade)selfs 9/var/www/face-recognition/faces/services/preprocessing.py__init__zFacePreprocessor.__init__sP11 HH ! !$I I 00 HH ! !$9 9 returnct|trKtj|}|t d|tj |tj }|St|tjr7|j|}tj|jd}|St|tjrt|jdk(r&tj |tj}|S|jddk(r&tj |tj }|S|jddk(r|j#}|St d|jt%|dr]|j'dtj(|}|j|}tj|jd}|St d t+|j,d ) z Load image from various input types and return as RGB numpy array. Supports: file path (str), PIL Image, numpy array, Django UploadedFile. z Could not load image from path: RGBzUnsupported image shape: readrzUnsupported image input type: zC. Expected str (path), PIL Image, numpy array, or file-like object.) isinstancestrrimread ValueErrorcvtColor COLOR_BGR2RGBr_apply_exif_rotationnparrayconvertndarraylenshapeCOLOR_GRAY2RGBCOLOR_BGRA2RGBcopyhasattrseekopentype__name__)r image_inputimgpil_imgs r _load_imagezFacePreprocessor._load_image%s k3 '**[)C{ #CK=!QRR,,sC$5$56CJ  U[[ 1++K8C((3;;u-.CJ  RZZ 0;$$%*ll;0B0BCJ ""1%*ll;0B0BC J ""1%*!&&(J!#<[=N=NHHI^I^_  !%,,R,=GGH]H]^ *5   s:D0+D0 D0%D06$D0$D05D095D00EEr/ctj|tj}|jj |dddtj }t |dk(r td|S)z9Detect faces using Haarcascade and return bounding boxes.皙?r:)rN) scaleFactor minNeighborsminSizeflagsrzfNo face detected in image. Please ensure the face is clearly visible, well-lit, and facing the camera.)rrCOLOR_RGB2GRAYr detectMultiScaleCASCADE_SCALE_IMAGEr$r)rr/grayfacess r _detect_faceszFacePreprocessor._detect_faceszsn||C!3!34!!22 )) 3  u:?<   rc t|dk(rt|dS|Dcgc] \}}}}||z}}}}}tj|}t||Scc}}}}w)z)Select the largest detected face by area.r)r$tupler argmax)rrWxywhareas largest_idxs r_get_largest_facez"FacePreprocessor._get_largest_faces` u:?q? "+011z.FacePreprocessor._align_face..s QqTr)rHrrZrNg?)scale)rR borderModez >:K"1~H#AI hqkQ.. hqkQ..K ! y|q00! y|q00L a;q>1Ba;q>1BJJrzz"b12E??2A&DAq1fa1f%F"55fe3OOnnAoo// GN  STrcHtj|tj}tj|\}}}tjdd}|j |}tj |||g}tj|tj} | S)z{ Apply histogram equalization on the L channel (LAB color space) to normalize lighting conditions. g@)r8r8) clipLimit tileGridSize)rr COLOR_RGB2LABsplit createCLAHEapplymerge COLOR_LAB2RGB) rr/lab l_channel a_channel b_channelclahe l_equalized lab_equalizedresults r_apply_histogram_equalizationz.FacePreprocessor._apply_histogram_equalizations| ll3 1 12*-))C.' 9i#FCkk),  ; 9"EF mS->->? rc |j|}t|jdk(r$tj|tj }|j |}|j|}|j||d}|j|}tj|dtj}|j|}|jtjdz } t j#d| S)a( Full preprocessing pipeline: 1. Load image (handles str path, PIL Image, numpy array, file-like) 2. Detect face using Haarcascade 3. Crop the largest detected face with 20px padding 4. Align using eye landmarks 5. Resize to 160x160 (ArcFace input size) 6. Apply histogram equalization on L channel (LAB) 7. Normalize pixel values to [0, 1] 8. Return as float32 numpy array shape (160, 160, 3) Raises: ValueError: If no face is detected in the image. r)re)r) interpolationgo@zcPreprocessing complete: face detected, cropped, aligned, resized to 160x160, equalized, normalized.)r1r$r%rrr&rXrcrorresizerrastyper float32rinfo) rr.r/rW largest_facerp aligned_faceresized equalized normalizeds r preprocesszFacePreprocessor.preprocesss {+ syy>Q ,,sC$6$67C""3'--e4 OOCrOB '' 2 **\:S__U66w? %%bjj1E9   9 rN)r)r- __module__ __qualname____doc__rr r#r1rrlistrXr[rcintrorrrrurrrrs  +"**+Z&ekk&ekk&P&)%) !bjj !E !C !QSQ[Q[ !4RZZ4BJJ4l   00rr) rloggingrnumpyr PILrr getLoggerr-rrrurrrs6     8 $HHr