def my_canny(img, fn = None, sigma=6, with_mask=False, save=False, show=False):
height = img.shape[0]
width = img.shape[1]
if with_mask:
import numpy as np
mymask = np.zeros((height, width),'uint8')
y1, x1 = 200, 150
y2, x2 = 500, 350
mymask[y1: y2, x1: x2] = 1
ret = canny(img, sigma=sigma, mask=mymask)
else:
ret = canny(img, sigma)
if show:
from src.utils.io import showimage_pil
showimage_pil(255*ret.astype('uint8'))
if save:
from src.utils.io import saveimage_pil
if with_mask:
feature = '_sigma' + str(sigma) + '_mask'
else:
feature = '_sigma' + str(sigma)
saveimage_pil(255*ret.astype('uint8'), fn+feature+'.jpg',show=False)
return ret
def save_all_sliding_windows(foldername, arr, step_size_W=30, step_size_H=30, winW=400, winH=400):
j = 1
from src.utils.io import saveimage_pil
for y in xrange(0, arr.shape[0], step_size_H):
for x in xrange(0, arr.shape[1], step_size_W):
if arr.dtype != 'uint8':
a = (255*arr[y:y+winH, x:x+winW]).astype(np.uint8)
else:
a = arr[y:y+winH, x:x+winW]
if a.shape[0] != winH or a.shape[1] != winW:
continue
saveimage_pil(a, foldername+str(j)+'.jpg')
j += 1
return j
def generate_blur_canny_subfolders(folder, outputFolder):
from src.utils.canny import my_canny
from src.utils.canny import best_edges
from src.utils.io import filename2arr
from src.utils.io import saveimage_pil
import os
for dirName, subdirList, fileList in os.walk(folder):
for subdir in subdirList:
for dirName2, subdirList2, fileList in os.walk(dirName + '/' +subdir):
for filename in fileList:
parts = filename.split('.')
if parts[0] == '':
continue
arr = filename2arr(dirName2 + '/' + filename)
ret = best_edges(arr)
if not os.path.exists(outputFolder + '/' + subdir):
os.makedirs(outputFolder + '/' + subdir)
outfn = outputFolder + '/' + subdir + '/'+ parts[0] + '.jpg'
print "saving canny file " + outfn
saveimage_pil(255 * ret.astype('uint8'), outfn, show=False)
def Harris_Corner(arr, show=False, save=False, fn=None):
gray = np.float32(arr)
dst = cv2.cornerHarris(gray, 2, 3, 0.04)
dst = cv2.dilate(dst, None)
ret, dst = cv2.threshold(dst, 0.01 * dst.max(), 255, 0)
dst = np.uint8(dst)
ret, labels, stats, centroids = cv2.connectedComponentsWithStats(dst)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.001)
corners = cv2.cornerSubPix(gray, np.float32(centroids), (5, 5), (-1, -1), criteria)
res = np.hstack((centroids, corners))
res = np.int0(res)
arr[res[:, 1], res[:, 0]] = 255
if show is True:
showimage_pil(arr)
if save is True:
if fn is None:
return
else:
saveimage_pil(arr, fn)
def HOG(arr, show=False, save=False, fn=None):
from src.utils.util import normalize_array
from skimage.feature import hog
fd, hog_image = hog(
arr, orientations=8, pixels_per_cell=(16, 16), cells_per_block=(1, 1), visualise=True, normalise=True
)
if show is True:
# from skimage import exposure
# hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 1))
# showimage_pil(hog_image_rescaled)
hog_image = normalize_array(hog_image, low=0.2, high=1)
showimage_pil(hog_image)
if save is True:
if fn is None:
print "filename in src.utils.features.HOG is None"
return hog_image
# from skimage import exposure
# hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 1))
# saveimage_pil(hog_image_rescaled)
saveimage_pil(hog_image)
return fd
from src.utils.util import scale
arr = scale(arr, sh, sw)
showimage_pil(arr)
#
from src.utils.gabor import gabor_feature_real
tao = 4.
elongation = 8.
sigmax = tao/2.35
sigmay = sigmax * elongation
max_orientation, max_magnitude, max_frequency = gabor_feature_real(arr, sigmax=sigmax, sigmay=sigmay)
from src.utils.gabor import normalize
normal_gabor = normalize(max_magnitude)
fn = '/Users/ruhansa/Desktop/4.jpg'
saveimage_pil(normal_gabor, fn)
arr = filename2arr(fn)
from src.utils.canny import decide_sigma
sigma = 0.0
for i in range(5):
print "sigma: " + str(sigma)
ret = my_canny(arr, sigma=sigma, save=False, show=True)
s = decide_sigma(ret, ret.size)
if s is False:
sigma += 0.5
else:
break
