def Merge(self, im):
h, w = np.shape(im)
merged = np.zeros((h, w, 3))
lbp = self.LBP.describe(im)
grad = self.Grad.getGradientSum(im)
if self.equalize:
im = cv2.equalizeHist(im)
lbp = cv2.equalizeHist(lbp)
grad = cv2.equalizeHist(grad)
if self.testMode:
moil.show(im, other_im=[lbp, grad])
merged[:, :, 0] = im
merged[:, :, 1] = lbp
merged[:, :, 2] = grad
return merged.astype('uint8')
def square_circle_on_1_2_in_path(path):
for i in range(6):
im, im_cp = moil.read_and_size_with_copy(str(i), path=path, scale=0.2)
im = equalize_border_with_mean_or_val(im)
h, w = np.shape(im)
r = (int)(h / 8)
img_sqd = square_circle_difference_filter_2d(im, r)
# img_sqd = square_circle_minus_filter_2d(im, r)
# img_sqd = circle_filter_2d(im)
img_sqd = cv2.equalizeHist(img_sqd)
ret, imt = cv2.threshold(img_sqd, 250, 255, cv2.THRESH_BINARY)
cv2.imshow('win2', im_cp)
cv2.imshow('win32', img_sqd)
moil.show(imt)
'''imt2,cnt, hier = cv2.findContours(imt, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
def MergeOnPath(self, path):
for i in range(len(os.listdir(path)) - 1):
img = moil.read_and_size(str(i), path=path, scale=0.3)
merged = self.Merge(img)
moil.show(merged, other_im=[img])
def circle_mask_on_random_image_in_path(self,
path,
target_size=None,
r=None,
extension=".jpg",
check_csv=True,
list=None):
numb = len([
i for i in os.listdir(path)
if os.path.isfile(os.path.join(path, i))
])
temp = ([
a for a in os.listdir(path)
if os.path.isfile(os.path.join(path, a))
])
try:
j = np.random.randint(numb)
except:
print(path + ", numb: " + str(numb))
return
ImName = random.choice(temp)
if not os.path.exists(path + '/mask'):
os.makedirs(path + '/mask')
tempName = path + '/mask/' + ImName
if os.path.exists(tempName):
print("Path exists (" + tempName + ")")
return
if check_csv:
paths = morn.getRepoPathAndImagePath(path)
row = paths[1].split("/")[:-1]
row.append(ImName)
if mocl.checkIfExistsInCSV(row, paths[0], list, False):
print("In CSV exists (" + tempName + ")")
return
if r is None and target_size is not None:
self.rr = int(target_size[0] / 10)
else:
self.rr = r
img = moil.read_and_size(ImName,
path=path,
target_size=target_size,
extension='')
w, h, c = moil.getWidthHeightChannels(img)
if r is None and target_size is None:
self.rr = int(w / 10)
target_size = (w, h)
moil.show(img)
accepted = False
while not accepted:
accepted = True
im2, contours, hierarchy = cv2.findContours(
self.mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
im2 = copy.deepcopy(img)
cv2.drawContours(im2, contours, 0, (0, 255, 255), 2)
moil.show(im2)
split_path = path.split("/")[:-1]
repo_path = reduce((lambda x, y: x + '/' + y),
split_path[:len(split_path) - 3])
if not os.path.isfile(repo_path + "/maskData.csv"):
csvFile = open(repo_path + '/maskData.csv', 'w', newline="")
writer = csv.writer(csvFile)
writer.writerow([
'patient', 'date', 'eye', 'name', 'width', 'height', 'x', 'y',
'r'
])
csvFile.close()
csvFile = open(repo_path + '/maskData.csv', 'a', newline="")
writer = csv.writer(csvFile)
ls = split_path[-3:]
ls.extend([
ImName, target_size[0], target_size[1], self.xx, self.yy, self.rr
])
writer.writerow(ls)
csvFile.close()
cv2.imwrite(path + '/mask/' + ImName, self.mask)
self.masks_done += 1
print("masks: " + str(self.masks_done))
cv2.destroyWindow('mask')
import cv2
import Code.Libraries.MyOculusImageLib as moil
import Code.Libraries.MyOculusRepoNav as morn
import os
path = '../../../Images/ZanikGray50/'
dict = {}
while True:
im_path = morn.next_path(path, dict)
dict[im_path] = True
if not os.path.exists(im_path):
continue
ims = os.listdir(im_path)
for im in ims:
if not os.path.isfile(im_path + im):
continue
img = moil.read_and_size(name=im, path=im_path, scale=1, extension='')
img2 = cv2.equalizeHist(img)
moil.show(img, other_im=[img2])
def model_show_function(x):
y = []
for i in range(len(x) - 1):
y.append(x[i + 1])
moil.show(x[0], other_im=y)
def LbpOnPath(self, path):
for i in range(len(os.listdir(path)) - 1):
img = moil.read_and_size(str(i), path=path, scale=self.scale)
lbp = self.describe(img)
moil.show(lbp, other_im=[img])
def GradientSumOnPath(self, path):
for i in range(len(os.listdir(path)) - 1):
img = moil.read_and_size(str(i), path=path, scale= self.scale)
grad = self.getGradientSum(img)
moil.show(grad, other_im=[img])
