mirror of https://github.com/davisking/dlib.git
Add basic image io and remove python C-API refs from numpy_returns.cpp (#1258)
* Fixed reference count issue * Fixed refcount issue in Python dlib.jitter_image and dlib.get_face_chips * Consolidation of https://github.com/davisking/dlib/pull/1249 * Fixed build issue * Fixed: Paths in a pytest file should be relative to dlib root * Skip numpy return tests for Python 2.7 or if Numpy is not installed * Enabled numpy returns tests on Python 2.7 using cPickle.dumpspull/1265/head
visionworkz
7 years ago
committed by
Davis E. King
parent
d7dfd8ad26
commit
e8faced822
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#!/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 program shows how to find frontal human faces in a webcam stream using OpenCV.
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# It is also meant to demonstrate that rgb images from Dlib can be used with opencv by just
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# swapping the Red and Blue channels.
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#
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# You can run this program and see the detections from your webcam by executing the
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# following command:
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# ./opencv_face_detection.py
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#
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# This face detector is made using the now classic Histogram of Oriented
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# Gradients (HOG) feature combined with a linear classifier, an image
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# pyramid, and sliding window detection scheme. This type of object detector
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# is fairly general and capable of detecting many types of semi-rigid objects
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# in addition to human faces. Therefore, if you are interested in making
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# your own object detectors then read the train_object_detector.py example
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# program.
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#
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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.
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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 installed. On Ubuntu, this can be done easily by running the
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# command:
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# sudo apt-get install cmake
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#
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# Also note that this example requires Numpy which can be installed
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# via the command:
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# pip install numpy
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import sys
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import dlib
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import cv2
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detector = dlib.get_frontal_face_detector()
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cam = cv2.VideoCapture(0)
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color_green = (0,255,0)
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line_width = 3
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while True:
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ret_val, img = cam.read()
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rgb_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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dets = detector(rgb_image)
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for det in dets:
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cv2.rectangle(img,(det.left(), det.top()), (det.right(), det.bottom()), color_green, line_width)
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cv2.imshow('my webcam', img)
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if cv2.waitKey(1) == 27:
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break # esc to quit
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cv2.destroyAllWindows()
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@ -1,3 +1,2 @@
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scikit-image>=0.9.3
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opencv-python
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numpy
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@ -1,59 +0,0 @@
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#include "opaque_types.h"
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#include <dlib/python.h>
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#include "dlib/pixel.h"
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#include <dlib/image_transforms.h>
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using namespace dlib;
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using namespace std;
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namespace py = pybind11;
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// ----------------------------------------------------------------------------------------
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py::list get_jitter_images(py::object img, size_t num_jitters = 1, bool disturb_colors = false)
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{
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throw dlib::error("jitter_image is only supported if you compiled dlib with numpy installed!");
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}
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// ----------------------------------------------------------------------------------------
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py::list get_face_chips (
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py::object img,
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const std::vector<full_object_detection>& faces,
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size_t size = 150,
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float padding = 0.25
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)
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{
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throw dlib::error("get_face_chips is only supported if you compiled dlib with numpy installed!");
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}
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py::object get_face_chip (
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py::object img,
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const full_object_detection& face,
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size_t size = 150,
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float padding = 0.25
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)
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{
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throw dlib::error("get_face_chip is only supported if you compiled dlib with numpy installed!");
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}
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// ----------------------------------------------------------------------------------------
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void bind_numpy_returns(py::module &m)
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{
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m.def("jitter_image", &get_jitter_images,
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"Takes an image and returns a list of jittered images."
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"The returned list contains num_jitters images (default is 1)."
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"If disturb_colors is set to True, the colors of the image are disturbed (default is False)",
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py::arg("img"), py::arg("num_jitters")=1, py::arg("disturb_colors")=false
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);
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m.def("get_face_chip", &get_face_chip,
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"Takes an image and a full_object_detection that references a face in that image and returns the face as a Numpy array representing the image. The face will be rotated upright and scaled to 150x150 pixels or with the optional specified size and padding.",
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py::arg("img"), py::arg("face"), py::arg("size")=150, py::arg("padding")=0.25
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);
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m.def("get_face_chips", &get_face_chips,
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"Takes an image and a full_object_detections object that reference faces in that image and returns the faces as a list of Numpy arrays representing the image. The faces will be rotated upright and scaled to 150x150 pixels or with the optional specified size and padding.",
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py::arg("img"), py::arg("faces"), py::arg("size")=150, py::arg("padding")=0.25
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);
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}
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#!/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 utility generates the test data required for the tests contained in test_numpy_returns.py
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#
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# Also note that this utility requires Numpy which can be installed
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# via the command:
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# pip install numpy
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import sys
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import dlib
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import numpy as np
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import utils
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if len(sys.argv) != 2:
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print(
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"Call this program like this:\n"
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" ./generate_numpy_returns_test_data.py shape_predictor_5_face_landmarks.dat\n"
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"You can download a trained facial shape predictor from:\n"
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" http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2\n")
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exit()
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detector = dlib.get_frontal_face_detector()
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predictor = dlib.shape_predictor(sys.argv[1])
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img = dlib.load_rgb_image("../../../examples/faces/Tom_Cruise_avp_2014_4.jpg")
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dets = detector(img)
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shape = predictor(img, dets[0])
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utils.save_pickled_compatible(shape, "shape.pkl")
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face_chip = dlib.get_face_chip(img, shape)
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np.save("test_face_chip", face_chip)
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Binary file not shown.
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import sys
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import pickle
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import dlib
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import pytest
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import utils
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# Paths are relative to dlib root
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image_path = "examples/faces/Tom_Cruise_avp_2014_4.jpg"
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shape_path = "tools/python/test/shape.pkl"
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face_chip_path = "tools/python/test/test_face_chip.npy"
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def get_test_image_and_shape():
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img = dlib.load_rgb_image(image_path)
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shape = utils.load_pickled_compatible(shape_path)
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return img, shape
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def get_test_face_chips():
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rgb_img, shape = get_test_image_and_shape()
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shapes = dlib.full_object_detections()
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shapes.append(shape)
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return dlib.get_face_chips(rgb_img, shapes)
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def get_test_face_chip():
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rgb_img, shape = get_test_image_and_shape()
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return dlib.get_face_chip(rgb_img, shape)
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# The tests below will be skipped if Numpy is not installed
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@pytest.mark.skipif(not utils.is_numpy_installed(), reason="requires numpy")
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def test_get_face_chip():
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import numpy
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face_chip = get_test_face_chip()
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expected = numpy.load(face_chip_path)
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assert numpy.array_equal(face_chip, expected)
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@pytest.mark.skipif(not utils.is_numpy_installed(), reason="requires numpy")
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def test_get_face_chips():
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import numpy
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face_chips = get_test_face_chips()
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expected = numpy.load(face_chip_path)
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assert numpy.array_equal(face_chips[0], expected)
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@pytest.mark.skipif(not utils.is_numpy_installed(), reason="requires numpy")
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def test_regression_issue_1220_get_face_chip():
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"""
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Memory leak in Python get_face_chip
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https://github.com/davisking/dlib/issues/1220
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"""
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face_chip = get_test_face_chip()
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# we expect two references:
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# 1.) the local variable
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# 2.) the temporary passed to getrefcount
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assert sys.getrefcount(face_chip) == 2
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@pytest.mark.skipif(not utils.is_numpy_installed(), reason="requires numpy")
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def test_regression_issue_1220_get_face_chips():
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"""
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Memory leak in Python get_face_chip
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https://github.com/davisking/dlib/issues/1220
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"""
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face_chips = get_test_face_chips()
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count = sys.getrefcount(face_chips)
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assert count == 2
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count = sys.getrefcount(face_chips[0])
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assert count == 2
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import pkgutil
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import sys
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def save_pickled_compatible(obj_to_pickle, file_name):
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'''
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Save an object to the specified file in a backward compatible
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way for Pybind objects. See:
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http://pybind11.readthedocs.io/en/stable/advanced/classes.html#pickling-support
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and https://github.com/pybind/pybind11/issues/271
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'''
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try:
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import cPickle as pickle # Use cPickle on Python 2.7
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except ImportError:
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import pickle
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data = pickle.dumps(obj_to_pickle, 2)
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with open(file_name, "wb") as handle:
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handle.write(data)
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def load_pickled_compatible(file_name):
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'''
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Loads a pickled object from the specified file
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'''
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try:
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import cPickle as pickle # Use cPickle on Python 2.7
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except ImportError:
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import pickle
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with open(file_name, "rb") as handle:
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data = handle.read()
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if not is_python3():
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return pickle.loads(data)
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else:
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return pickle.loads(data, encoding="bytes")
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def is_numpy_installed():
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'''
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Returns True if Numpy is installed otherwise False
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'''
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if pkgutil.find_loader("numpy"):
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return True
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else:
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return False
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def is_python3():
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'''
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Returns True if using Python 3 or above, otherwise False
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'''
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return sys.version_info >= (3, 0)
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