warmspringwinds

# Assuming the origin is in the top left corner
# x axis facing right
# y axis facing downwards

# r.x, r.y -- are top left corners of the rectangle
# with corresponding r.height and r.width

def intersection_area(r_1, r_2):
  intersection = 0.0

warmspringwinds

%matplotlib inline

import tensorflow as tf
import numpy as np
import skimage.io as io
import os, sys
from PIL import Image
from matplotlib import pyplot as plt

sys.path.append("tf-image-segmentation/")

warmspringwinds

%matplotlib inline

from __future__ import division

import os
import sys
import tensorflow as tf
import skimage.io as io
import numpy as np

warmspringwinds

%matplotlib inline
from __future__ import division

import numpy as np
import tensorflow as tf
import sys
import os
from matplotlib import pyplot as plt
import urllib2

warmspringwinds

%%cython -a
# cython: cdivision=True
# cython: boundscheck=False
# cython: nonecheck=False
# cython: wraparound=False
# distutils: language = c++

from libcpp.vector cimport vector
from libcpp.pair cimport pair
import skimage.io as io

warmspringwinds

def intersection_ratio(rect_a, rect_b):
    
    r_a_1 = rect_a["r"]
    r_a_2 = rect_a["r"] + rect_a["height"]
    c_a_1 = rect_a["c"]
    c_a_2 = rect_a["c"] + rect_a["width"]
    
    r_b_1 = rect_b["r"]
    r_b_2 = rect_b["r"] + rect_b["height"]
    c_b_1 = rect_b["c"]

warmspringwinds

%%cython -a
# cython: cdivision=True
# cython: boundscheck=False
# cython: nonecheck=False
# cython: wraparound=False

import numpy as np
cimport numpy as cnp
from skimage._shared.transform cimport integrate

warmspringwinds

import skimage.io as io

from skimage.transform import pyramid_gaussian, rescale
from skimage.util import view_as_windows
from opencv_trainfile_test import detect_face
from matplotlib import pyplot as plt
import matplotlib.patches as patches

current_img = io.imread('roi.jpg')

warmspringwinds

# Use local scikit-image
import sys
sys.path.insert(0, "/home/dan/University/projects/gsoc_face_detection/scikit-image/")

from skimage.feature import multiblock_local_binary_pattern
from skimage.transform import integral_image
import numpy as np
import skimage.io as io
import xml.etree.ElementTree as ET

warmspringwinds

import numpy as np
cimport numpy as cnp
 
 
def get_scale_local_maximas_cython(cnp.ndarray[cnp.int_t, ndim=2] cube_coordinates, cnp.ndarray[cnp.double_t, ndim=3] laplacian_cube):
    """
    Check provided cube coordinate for scale space local maximas.
    Returns only the points that satisfy the criteria.

    A point is considered to be a local maxima if its value is greater