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        "author": "Luis Rojas Aguilera",
        "author_email": "rojas@icomp.ufam.edu.br",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
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        "description": "## Face Detector\n\nPython package and Command Line Tool for state-of-the-art face detection and face\nlandmark points localization. It gathers the techniques implemented in dlib and\nmtcnn, which can be easily switched between by setting a parameter in the\nFaceDetector class instantiation (dlib\\_5 is default if no technique is\nspecified, use dlib\\_5 for dlib with 5 landmarks and dlib\\_68 for dlib with 68\nlandmarks).\n\n## How to Install:\n\n First install C compiler:\n    sudo apt-get install cmake, g++\n\n Then this package:\n    pip install face-detector\n\n## How to Use python package:\n\n    from face_detector import FaceDetector\n\n    img_addr = \"path/to/image.[jpg/png/jpeg ...]\"\n\n    # First parameter in FaceDetector constructor specifies face detection method (dlib: fl_5 or fl_68, mtcnn is default: mtcnn)\n    face_detector = FaceDetector()\n    faces = face_detector.get_faces(img_addr)\n\n    # Or to get the most prominent face in photo\n    main_face = face_detector.get_main_face(img_addr)\n\n    # Show image with bounding boxes and landmarks\n    import cv2\n    img = cv2.imread(img_addr)\n\n    for face in faces:\n       bb = face.bounding_box\n       landmarks = face.landmarks\n       cv2.rectangle(img, (int(bb.x), int(bb.y)), (int(bb.x + bb.w), int(bb.y+bb.h)), (0, 255, 0), 1)\n       for l in landmarks:\n            cv2.circle(img, (l.x, l.y), 2, (0,0,255))\n\n        cv2.imshow('img', img)\n        cv2.waitKey(0)\n        cv2.destroyAllWindows()\n\n## How to use Command Line Tool\n\n```console\n    foo@bar:~$ facedetector /home/foo/images/Yasser_Arafat.jpg\n```\nThe previous command will display the image passed in arguments with a bounding box wrapping every face in the image. Fig. 1 shows the image displayed.\n\n<div align='left' style=\"display:inline-block; text-align:center; word-wrap: break-word;\">\n<img src='https://raw.githubusercontent.com/roj4s/face_detector/master/samples/Yasser_Arafat_2_faces.jpg' /> <p>Fig. 1 Face detections as outputted by facedetector command line tool</p>\n</div>\n\n<!--\n<div align='left' style=\"margin-left:10px; display:inline-block; text-align:center; word-wrap: break-word;\">\n<img  src='samples/Yasser_Arafat_main_face.jpg'/> <p>Fig. 3 Main face in photo, outputted by facedetector using -j option</p>\n</div>\n-->\n<div align='left' style=\"margin-left:10px; display:inline-block; text-align:center; word-wrap: break-word;\">\n<img  src='https://raw.githubusercontent.com/roj4s/face_detector/master/samples/Yasser_Arafat_landmarks.jpg'/> <p>Fig. 2 Face detections and landmarks as outputted by facedetector with -l (--landmarks) and -j (--only-main-face) options</p>\n</div>\n\n\n```console\n    foo@bar:~$ facedetector /home/foo/images/Yasser_Arafat.jpg -j -o /tmp/output.jpg -l\n```\nThe previous command adds -j, -l and -o options, which capture the main\nface in the photo, adds landmark points and output the image with bounding boxes to the\nspecified path, respectivelly. It also display the image in Fig. 2.\n\n\n<!--\n[//]: <> - From Github:\n[//]: <>    - Clone this repository\n[//]: <>    - Install dependencies in requirements.txt:\n[//]: <>        - pip install -r requirements.txt\n[//]: <>    - You might need to install zlib and link it to /usr/lib/x86_64-linux-gnu/libz.so:\n[//]: <>        ```console\n[//]: <>         foo@bar:~/face_detector$ tar xzvf data/zlib-1.2.9.tar.gz\n[//]: <>         foo@bar:~/face_detector$ cd data/zlib\n[//]: <>         foo@bar:~/face_detector/data/zlib$ sudo ./configure && make && make install\n[//]: <>         foo@bar:~/face_detector/data/zlib$ ln -s /lib/x86_64-linux-gnu/libz.so.1.2.8 /usr/lib/x86_64-linux-gnu/libz.so\n[//]: <>        ```\n-->\n\n\n",
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