mirror of https://github.com/davisking/dlib.git
99 lines
3.4 KiB
Python
Executable File
99 lines
3.4 KiB
Python
Executable File
#!/usr/bin/python
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# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
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#
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# This example shows how to use dlib's face recognition tool for image alignment.
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#
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# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
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# You can install dlib using the command:
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# pip install dlib
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#
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# Alternatively, if you want to compile dlib yourself then go into the dlib
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# root folder and run:
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# python setup.py install
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# or
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# python setup.py install --yes USE_AVX_INSTRUCTIONS
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# if you have a CPU that supports AVX instructions, since this makes some
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# things run faster. This code will also use CUDA if you have CUDA and cuDNN
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# installed.
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#
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# Compiling dlib should work on any operating system so long as you have
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# CMake and boost-python installed. On Ubuntu, this can be done easily by
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# running the command:
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# sudo apt-get install libboost-python-dev cmake
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#
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# Also note that this example requires OpenCV and Numpy which can be installed
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# via the command:
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# pip install opencv-python numpy
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# Or downloaded from http://opencv.org/releases.html
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import sys
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import os
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import dlib
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import glob
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import cv2
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import numpy as np
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if len(sys.argv) != 4:
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print(
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"Call this program like this:\n"
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" ./face_alignment.py shape_predictor_5_face_landmarks.dat dlib_face_recognition_resnet_model_v1.dat ../examples/faces/bald_guys.jpg\n"
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"You can download a trained facial shape predictor and recognition model from:\n"
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" http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2\n"
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" http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2")
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exit()
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predictor_path = sys.argv[1]
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face_rec_model_path = sys.argv[2]
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face_file_path = sys.argv[3]
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# Load all the models we need: a detector to find the faces, a shape predictor
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# to find face landmarks so we can precisely localize the face, and finally the
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# face recognition model.
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detector = dlib.get_frontal_face_detector()
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sp = dlib.shape_predictor(predictor_path)
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facerec = dlib.face_recognition_model_v1(face_rec_model_path)
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# Load the image using OpenCV
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bgr_img = cv2.imread(face_file_path)
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if bgr_img is None:
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print("Sorry, we could not load '{}' as an image".format(face_file_path))
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exit()
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# Convert to RGB since dlib uses RGB images
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img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB)
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# Ask the detector to find the bounding boxes of each face. The 1 in the
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# second argument indicates that we should upsample the image 1 time. This
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# will make everything bigger and allow us to detect more faces.
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dets = detector(img, 1)
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num_faces = len(dets)
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if num_faces == 0:
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print("Sorry, there were no faces found in '{}'".format(face_file_path))
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exit()
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# The full object detection object
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faces = dlib.full_object_detections()
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for detection in dets:
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faces.append(sp(img, detection))
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# Get the aligned face images
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# Optionally:
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# images = dlib.get_face_chips(img, faces, size=160, padding=0.25)
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images = dlib.get_face_chips(img, faces, size=320)
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for image in images:
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cv_rgb_image = np.array(image).astype(np.uint8)
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cv_bgr_img = cv2.cvtColor(cv_rgb_image, cv2.COLOR_RGB2BGR)
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cv2.imshow('image',cv_bgr_img)
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cv2.waitKey(0)
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# It is also possible to get a single chip
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image = dlib.get_face_chip(img, faces[0])
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cv_rgb_image = np.array(image).astype(np.uint8)
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cv_bgr_img = cv2.cvtColor(cv_rgb_image, cv2.COLOR_RGB2BGR)
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cv2.imshow('image',cv_bgr_img)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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