Install opencv3 with CUDA support on a mac

Install from source (recommended):

sudo xcode-select --install # if not done already
brew tap homebrew/science
brew install cmake pkg-config jpeg libpng libtiff openexr eigen tbb
cd ~/CppProjects/
git clone --depth 1 https://github.com/opencv/opencv
git clone --depth 1 https://github.com/opencv/opencv_contrib

cd /Users/kaiyin/anaconda3/envs/tensorflow/lib/
ln -s libpython3.5m.dylib libpython3.5.dylib 

# install for python 3.5
# tensorflow is an anaconda python3.5 environment on my machine created for tensorflow
export ENV_TENSORFLOW=/Users/kaiyin/anaconda3/envs/tensorflow
export PREFIX=/opt/local
export PY_DYLIB="$ENV_TENSORFLOW/lib/libpython3.5.dylib"
export OPENCV_CONTRIB=~/CppProjects/opencv_contrib/modules
export PY_INCLUDE="$ENV_TENSORFLOW/include/python3.5m"
export PY_BINARY="$ENV_TENSORFLOW/bin/python3.5"
cmake -D CMAKE_BUILD_TYPE=RELEASE \
    -D CMAKE_INSTALL_PREFIX=$PREFIX \
    -D OPENCV_EXTRA_MODULES_PATH="$OPENCV_CONTRIB" \
    -D PYTHON3_LIBRARY="$PY_DYLIB" \
    -D PYTHON3_INCLUDE_DIR="$PY_INCLUDE" \
    -D PYTHON3_EXECUTABLE="$PY_BINARY" \
    -D BUILD_opencv_python2=OFF \
    -D BUILD_opencv_python3=ON \
    -D INSTALL_PYTHON_EXAMPLES=ON \
    -D INSTALL_C_EXAMPLES=OFF \
    -D BUILD_EXAMPLES=ON ..

make -j8 # use 8 jobs for compiling
sudo make install
cp $PREFIX/lib/python3.5/site-packages/cv2.cpython-35m-darwin.so  $ENV_TENSORFLOW/lib/python3.5/site-packages


# install for python 2.7
# tf27 is an anaconda python2.7 environment on my machine created for tensorflow
export ENV_TENSORFLOW=/Users/kaiyin/anaconda3/envs/tf27
export PREFIX=/opt/local
export PY_DYLIB="$ENV_TENSORFLOW/lib/libpython2.7.dylib"
export OPENCV_CONTRIB=~/CppProjects/opencv_contrib/modules
export PY_INCLUDE="$ENV_TENSORFLOW/include/python2.7"
export PY_BINARY="$ENV_TENSORFLOW/bin/python2.7"
cmake -D CMAKE_BUILD_TYPE=RELEASE \
    -D CMAKE_INSTALL_PREFIX=$PREFIX \
    -D OPENCV_EXTRA_MODULES_PATH="$OPENCV_CONTRIB" \
    -D PYTHON2_LIBRARY="$PY_DYLIB" \
    -D PYTHON2_INCLUDE_DIR="$PY_INCLUDE" \
    -D PYTHON2_EXECUTABLE="$PY_BINARY" \
    -D BUILD_opencv_python2=ON \
    -D BUILD_opencv_python3=OFF \
    -D INSTALL_PYTHON_EXAMPLES=ON \
    -D INSTALL_C_EXAMPLES=OFF \
    -D BUILD_EXAMPLES=ON ..


make -j8 # use 8 jobs for compiling
sudo make install
cp $PREFIX/lib/python2.7/site-packages/cv2.so $ENV_TENSORFLOW/lib/python2.7/site-packages/

Verify your installation in python 2.7:

# source activate tf27
(tf27) kaiyin@kaiyins-mbp 21:11:12 | /opt/local/lib/python3.5/site-packages =>
ipython
Python 2.7.13 |Continuum Analytics, Inc.| (default, Dec 20 2016, 23:05:08)
Type "copyright", "credits" or "license" for more information.

IPython 5.1.0 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object', use 'object??' for extra details.

IPython profile: kaiyin

In [1]: import cv2
c
In [2]: cv2.__version__
Out[2]: '3.2.0-dev'

Verify your installation in python 3.5:

# source activate tensorflow
(tensorflow) kaiyin@kaiyins-mbp 21:13:13 | /opt/local/lib/python3.5/site-packages =>
ipython
Python 3.5.2 |Continuum Analytics, Inc.| (default, Jul  2 2016, 17:52:12)
Type "copyright", "credits" or "license" for more information.

IPython 5.1.0 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object', use 'object??' for extra details.

IPython profile: kaiyin

In [1]: import cv2; cv2.__version__
Out[1]: '3.2.0-dev'

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Examples of linear convolutional filters

The sharpening filter perhaps needs a bit explanation. Suppose the pixel under the center of the filter has value $v = x+d$, while all the pixels around have value $v_a = x$, then after filtering:

$$\begin{align*} v' &= \frac{17}{9}(x + d) - \frac{8}{9}x \\ &= x + \frac{17}{9}d \end{align*}$$

Obviously, when the difference between the pixel and its environment is zero, the filter will not have any effect, but when there is a difference, it’s amplified by a factor of $17/9$. Therefore this is called a sharpening filter.

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Dodge and burn

Dodging has the over-exposure effect, where light pixels tend to be pushed to white.
Burning is the opposite, where dark pixels tend to be pushed to black.

import numpy as np
import cv2


def dodge(image, mask):
    return cv2.divide(image, 255 - mask, scale=256)


def burn(image, mask):
    return 255 - cv2.divide(255 - image, 255 - mask, scale=256)


class PencilSketch:
    def __init__(self, width, height, bg_gray="pencilsketch_bg.jpg"):
        self.width = width
        self.height = height
        self.canvas = cv2.imread(bg_gray, cv2.CV_8UC1)
        if self.canvas is not None:
            self.canvas = cv2.resize(self.canvas, (width, height))

    def render(self, img_rgb):
        img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2GRAY)
        img_gray_inv = 255 - img_gray
        img_blur = cv2.GaussianBlur(img_gray_inv, (21, 21), 0, 0)
        img_blend = dodge(img_gray, img_blur)
        return cv2.cvtColor(img_blend, cv2.COLOR_GRAY2RGB)


import matplotlib.pyplot as plt
# img = plt.imread("/Users/kaiyin/PycharmProjects/opencv3blueprints/chapter01/tree.jpg")
img_rgb = cv2.imread("/Users/kaiyin/PycharmProjects/opencv3blueprints/chapter01/tree.jpg", -1)
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2GRAY)
# print(img_rgb.shape)
plt.imshow(img_rgb)
plt.imshow(img_gray, cmap="gray")
img_dodge = dodge(img_gray, img_gray)
plt.imshow(img_dodge, cmap="gray")
img_burn = burn(img_gray, img_gray)
img_blur = cv2.GaussianBlur(img_gray, (21, 21), 3)
plt.clf(); plt.imshow(img_blur, cmap="gray")
# effect of dodging: pixels that are brighter than a certain threshold are pushed to 255 (except that 255 is degenerated into 0)
plt.clf(); plt.scatter(img_gray.flatten(), img_dodge.flatten())
plt.clf(); plt.scatter(img_gray.flatten(), img_burn.flatten())
img_dodge1 = dodge(img_gray, img_blur)
img_burn1 = burn(img_gray, img_blur)
plt.clf(); plt.scatter(img_gray.flatten(), img_dodge1.flatten())
plt.clf(); plt.scatter(img_gray.flatten(), img_burn1.flatten())
plt.clf(); plt.imshow(img_dodge1, cmap="gray")
plt.clf(); plt.imshow(img_burn1, cmap="gray")

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