def is_rhombohedral(self) -> bool:
"""Returns true if all lengths are equal and all angles are equal"""
return (
self.abc_equal
and close(np.array(self.angles) - self.angles[0], zeros(3))
and (not close(self.alpha, np.pi / 2))
)
def angles_different(self) -> bool:
"are all of the angles alpha, beta, gamma different?"
return not (
close(self.alpha, self.beta)
or close(self.alpha, self.gamma)
or close(self.beta, self.gamma)
)
def is_hexagonal(self) -> bool:
"""Returns true if lengths a == b, a != c, alpha and beta == 90 and gamma == 120"""
return (
close(self.a, self.b)
and (not close(self.a, self.c))
and close(self.angles[:2], np.pi / 2)
and close(self.gamma, 2 * np.pi / 3)
)
def Frontier_init():
Frontier = rospy.Publisher('/Frontier', GridCells)
#find maximum point in empty space in the x and y drection
Array_EmptyCells = PtArr_Empty
OCCUPIED = False
MAX_BOOL = False
MIN_BOOL = False
while Array_EmptyCells:
MAX_Point = max(Array_EmptyCells)
MIN_Point = min(Array_EmptyCells)
Frontier_Array = []
while True:
#check If the point after the max_point.x is occupied
for elem in PtArr_Occupied:
if (numpy.close(MAX_Point.x + RESOLUTION, elem.x) and numpy.close(MAX_Point.y, elem.y)):
OCCUPIED = True
MAX_BOOL = True
break
for elem in PtArr_Occupied:
if (numpy.close(MIN_Point.x - RESOLUTION, elem.x) and numpy.close(MIN_Point.y, elem.y)):
MIN_BOOL = True
OCCUPIED = True
break
if not OCCUPIED:
if MAX_BOOL:
Frontier_Array.append(MAX_Point) #add it to frontier array
if MIN_BOOL:
Frontier_Array.append(MIN_Point) #add it to frontier array
Frontier_MaxPoint = Max_Point
Frontier_MinPoint = Min_Point
while True:
Frontier_MaxPoint.y = Frontier_MaxPoint.y + RESOLUTION #Next frontier point
for elem in PtArr_Occupied:
if numpy.close(Frontier_MaxPoint.y, elem.y ) and numpy.close(MAX_Point.x, elem.x) :
Frontier_Array.append(Frontier_MaxPoint)
break
for elem in PtArr_Occupied:
if numpy.close(Frontier_MinPoint.y, elem.y ) and numpy.close(Frontier_MinPoint.x, elem.x) :
Frontier_Array.append(Frontier_MinPoint)
break
break
#Remove row from frontier array
for elem in Array_EmptyCells:
if numpy.close(MAX_Point.y, elem.y):
Array_EmptyCells.remove(elem)
gridcells_frontier = GridCells()
gridcells_frontier.header = HEADER
gridcells_frontier.cell_width = RESOLUTION
gridcells_frontier.cell_height = RESOLUTION
gridcells_frontier.cells = Frontier_Array
Frontier.publish(gridcells_frontier)
return
def is_deterministic(matrix):
rows, cols = matrix.shape
answer = True
for r in range(rows):
if not np.close(max(matrix[r, :]), sum(matrix[r, :])):
answer = False
break
return answer
def first_four(thisfolder = ".", newfolder = "first_four"):
files = os.listdir(thisfolder)
if not newfolder in files:
os.mkdir(newfolder)
for f in files:
if f[-4:] == ".txt" and f[0] == "r":
oldpath = os.path.join(thisfolder,f)
op = open(oldpath,"r")
newfile = f[:-4] + "FIRSTFOUR.txt"
newpath = os.path.join(newfolder,newfile)
np = open(newpath,"w")
print(f)
for line in op:
if re.match(new_referents_line,line):
np.close()
break
else:
np.write(line)
def isInside(self, other: Union['Line', 'Rectangle', 'Circle']) -> bool:
if isinstance(other, Line):
AM = Line(other.p1, self)
MB = Line(self, other.p2)
return np.close(np.abs(AM.dot(BM)), AM.length * MB.length)
elif isinstance(other, Rectangle):
# Based on https://stackoverflow.com/a/2763387
AB = Line(other.c1, other.c2)
AM = Line(other.c1, self)
BC = Line(other.c2, other.c3)
BM = Line(other.c2, self)
return 0 <= AB.dot(AM) <= AB.dot(AB) and 0 <= BC.dot(BM) <= BC.dot(
BC)
elif isinstance(other, Circle):
return (self - other.m).norm(p=2) <= other.r
raise NotImplementedError
def serchBtn_event(self):
hashTag = self.serchWindow.text()
self.show_heart(self.heart_label, 0) # 새로 검색을 누르면 하트 초기화
self.imageList = ['이미지로딩.png'] # 이미지 리스트 초기화
if os.path.isfile('./contents/' + hashTag + '.tsv') != True:
# 해시태그 URL
tagUrl = 'https://www.instagram.com/explore/tags/' + hashTag + '/'
driver.implicitly_wait(5)
# 게시글 내용 저장
cf = open('./contents/' + hashTag + '.txt', 'w', encoding='utf-8')
df = open('./datetimes/' + hashTag + '.txt', 'w', encoding='utf-8')
np = open('./postnums/' + hashTag + '.txt', 'w', encoding='utf-8')
# 웹 사이트 접속
driver.get(tagUrl)
post_num = driver.find_element_by_xpath(
'//*[@id="react-root"]/section/main/header/div[2]/div/div[2]/span/span'
).text
np.write(post_num)
np.close()
if not os.path.exists('./photos/' + hashTag):
os.mkdir('./photos/' + hashTag)
for j in range(1, 4): # 이미지 3개 저장
img_elem = driver.find_element_by_xpath(
'//*[@id="react-root"]/section/main/article/div[1]/div/div/div[1]/div['
+ str(j) + ']/a/div/div[1]/img')
img_src = img_elem.get_attribute('src')
img_file_path = './photos/' + hashTag + '/' + hashTag + str(
j) + '.jpg'
urllib.request.urlretrieve(img_src, img_file_path)
self.imageList.append(img_file_path)
# 게시글 클릭
driver.find_element_by_css_selector(
'div.v1Nh3.kIKUG._bz0w').click()
for i in range(20):
time.sleep(2)
# 게시글에 텍스트가 있으면 읽어들임
if hasxpath(
'/html/body/div[5]/div[2]/div/article/div[3]/div[1]/ul/div/li/div/div/div[2]/span'
):
post = driver.find_element_by_xpath(
'/html/body/div[5]/div[2]/div/article/div[3]/div[1]/ul/div/li/div/div/div[2]/span'
).text
post = re.findall('[가-힣]+', post) # 한국어로 된 게시글만 찾기
post = ' '.join(post)
check_result = spell_checker.check(post)
cor_post = check_result.checked # 맞춤법 체크
if len(cor_post) > 1:
cf.write(cor_post + '\n')
# 포스팅 시간대 크롤링
datetime = driver.find_element_by_tag_name('time')
datetime = datetime.get_attribute('datetime')[11:13]
df.write(datetime + '\n')
# 다음 게시글로 넘어가는 화살표 클릭
driver.find_element_by_css_selector(
'a._65Bje.coreSpriteRightPaginationArrow').click()
cf.close()
df.close()
else:
post_num_path = './postnums/' + hashTag + '.txt'
f = open(post_num_path, 'r', encoding='utf-8')
post_num = f.readline()
f.close()
for p in range(1, 4):
img_file_path = 'photos/' + hashTag + '/' + hashTag + str(
p) + '.jpg'
self.imageList.append(img_file_path)
tsv_path = './contents/' + hashTag + '.tsv'
txt_path = './contents/' + hashTag + '.txt'
time_path = './datetimes/' + hashTag + '.txt'
change_tsv(txt_path, hashTag, 'text')
test_data = pd.read_csv(tsv_path, encoding='utf-8', sep='\t')
vocab_path = 'dataset/vocab.txt'
x_test = read_data(test_data, vocab_path, 50)
predict = test(model, x_test, 1)
avg_score = round(avg(predict)) # 평균 점수에서 소수점 버림
self.show_heart(self.heart_label, avg_score) # 평점에 따라 이미지 변경
self.post_num_label.setText(str(post_num)) # 총 게시글 수를 gui 라벨에 나타내기
self.show_image(self.img1_label, 1)
self.show_image(self.img2_label, 2)
self.show_image(self.img3_label, 3)
make_wordcloud(txt_path, hashTag)
self.show_wc_or_plt(self.wc_label, hashTag)
make_timeplt(time_path, hashTag)
self.show_wc_or_plt(self.plt_label, hashTag)
self.show()
def open_nparser():
np = NOMADFieldsParser()
yield np
np.close()
def is_tetragonal(self) -> bool:
"""Returns true if a, b are equal and all angles are 90 degrees"""
return close(self.a, self.b) and (not close(self.a, self.c)) and self.orthogonal
def is_monoclinic(self) -> bool:
"""Returns true if angles alpha and gamma are equal"""
return close(self.alpha, self.gamma) and self.abc_different
def orthogonal(self) -> bool:
"returns true if the lattice vectors are orthogonal"
return close(np.abs(self.angles) - np.pi / 2, zeros(3))
def abc_different(self) -> bool:
"are all of the lengths a, b, c different?"
return not (
close(self.a, self.b) or close(self.a, self.c) or close(self.b, self.c)
)
def abc_equal(self) -> bool:
"are the lengths a, b, c all equal?"
return close(np.array(self.lengths) - self.lengths[0], zeros(3))
import cv2
import numpy as np
import imutils
low_red = np.array([50, 150, 30]) #üst ve alt kırmızı değerleri
high_red = np.array([180, 255, 255])
cap= cv2.VideoCapture(0)
kernelOpen=np.ones((5,5)) #morphology alan ayarları
kernelClose=np.close((20,20))
rect= False
try:
while True:
grabbed, frame= cap.read()
status= "HEDEF YOK"
if not grabbed:
continue
frame=cv2.resize(frame, (340,220))
hsv=cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
mask=cv2.inRange(hsv, low_red, high_red)
res= cv2.bitwise_and(frame, frame, mask=mask)
maskOpen=cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernelOpen)
maskClose=cv2.morphologyEx(maskOpen, cv2.MORPH_CLOSE, kernelClose) #pürüzleri yok et.
maskSon=maskClose
