Untitled.ipynb

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     "text": [
      "12 81 155 155\n"
     ]
    }
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   "source": [
    "from imutils import face_utils\n",
    "import numpy as np\n",
    "import argparse\n",
    "import imutils\n",
    "import dlib\n",
    "import cv2\n",
    "\n",
    "# code from https://www.pyimagesearch.com/2017/04/03/facial-landmarks-dlib-opencv-python/\n",
    "\n",
    "def rect_to_bb(rect):\n",
    "    # take a bounding predicted by dlib and convert it\n",
    "    # to the format (x, y, w, h) as we would normally do\n",
    "    # with OpenCV\n",
    "    x = rect.left()\n",
    "    y = rect.top()\n",
    "    w = rect.right() - x\n",
    "    h = rect.bottom() - y\n",
    " \n",
    "    # return a tuple of (x, y, w, h)\n",
    "    return (x, y, w, h)\n",
    "\n",
    "def shape_to_np(shape, dtype=\"int\"):\n",
    "    # initialize the list of (x, y)-coordinates\n",
    "    coords = np.zeros((68, 2), dtype=dtype)\n",
    " \n",
    "    # loop over the 68 facial landmarks and convert them\n",
    "    # to a 2-tuple of (x, y)-coordinates\n",
    "    for i in range(0, 68):\n",
    "        coords[i] = (shape.part(i).x, shape.part(i).y)\n",
    " \n",
    "    # return the list of (x, y)-coordinates\n",
    "    return coords\n",
    " \n",
    "# initialize dlib's face detector (HOG-based) and then create\n",
    "# the facial landmark predictor\n",
    "detector = dlib.get_frontal_face_detector()\n",
    "predictor = dlib.shape_predictor(\"shape_predictor_68_face_landmarks.dat\")\n",
    " \n",
    "# load the input image, resize it, and convert it to grayscale\n",
    "image = cv2.imread(\"image.png\")\n",
    "image = imutils.resize(image, width=500)\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    " \n",
    "# detect faces in the grayscale image\n",
    "rects = detector(gray, 1)\n",
    "\n",
    "for (i, rect) in enumerate(rects):\n",
    "    # determine the facial landmarks for the face region, then\n",
    "    # convert the facial landmark (x, y)-coordinates to a NumPy\n",
    "    # array\n",
    "    shape = predictor(gray, rect)\n",
    "    shape = face_utils.shape_to_np(shape)\n",
    " \n",
    "    # convert dlib's rectangle to a OpenCV-style bounding box\n",
    "    # [i.e., (x, y, w, h)], then draw the face bounding box\n",
    "    (x, y, w, h) = face_utils.rect_to_bb(rect)\n",
    "    print(x, y, w, h)\n",
    "    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)\n",
    " \n",
    "    # loop over the (x, y)-coordinates for the facial landmarks\n",
    "    # and draw them on the image\n",
    "    for index, (x, y) in enumerate(shape):\n",
    "        cv2.circle(image, (x, y), 1, ((255/80)*index, (255/80)*index, (255/80)*index), -1)\n",
    " \n",
    "# show the output image with the face detections + facial landmarks\n",
    "cv2.imshow(\"Output\", image)\n",
    "cv2.waitKey(0)"
   ]
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     "text": [
      "rectangles[[(12, 81) (167, 236)]]\n"
     ]
    }
   ],
   "source": [
    "print(rects)"
   ]
  },
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   "execution_count": null,
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