def CalculateCOS():
print("请输入第一个向量坐标，x，y坐标用空格隔开,按Enter键完成:")
p1 = input()
tmp1 = p1.split()
if isdigit(tmp1[0]) == False or isdigit(tmp1[1]) == False:
print("输入非法,请重新输入!")
return
if isinstance(int(tmp1[0]), int) and isinstance(int(tmp1[1]), int):
print("您输入的第一个向量为(" + p1.split()[0] + "," + p1.split()[1] + ")" +
"\n" + "请按照同样的方式输入第二个向量坐标，按Enter键完成:")
p2 = input()
tmp2 = p2.split()
if isdigit(tmp2[0]) == False or isdigit(tmp2[1]) == False:
print("输入非法,请重新输入!")
return
if isinstance(int(tmp2[0]), int) and isinstance(int(tmp2[1]), int):
print("您输入的第二个向量为(" + p2.split()[0] + "," + p2.split()[1] + ")")
p1x = int(p1.split()[0])
p1y = int(p1.split()[1])
p2x = int(p2.split()[0])
p2y = int(p2.split()[1])
a = p1x * p2x + p1y * p2y
b = sqrt((p1x**2) + (p1y**2))
c = sqrt((p2x**2) + (p2y**2))
cos = a / (b * c)
print("您输入的两个向量的余玄值为%f：" % (cos))
return
pass
return
def is_decomp(r1, r2, operator):
# if the two responses aren't in the same item, can't be decomposed
#if item_check(r1, r2) == "N":
#return False
# must have operator in the potentially "decomposed" response
if operator not in r2:
return False
decomp = "Maybe"
decomp_len = 0
# search for decomposition from r1 -> r2 (as in: R1 = 2 + 2, R2 = 1 + 1 + 2)
# therefore, r2 cannot be shorter than r1
if len(r2) < len(r1):
return False
for i in range(len(r1)):
sub_exp = ""
# start at i's position in j
# if decomp found this will change
for j in range(i, len(r2)):
# potential for decomposition
if (r2[j] != r1[i] and j == i) and ((j + 1 < len(r2) and r2[j + 1] == operator) or (j - 1 < len(r2) and r2[j - 1] == operator and r2[j + 1] != operator)):
k = j
while k < len(r2):
sub_exp += r2[k]
print(sub_exp)
# do the stuffs
if isdigit(sub_exp[-1]):
if pd.eval(sub_exp) == int(r1[i]):
decomp_len += len(sub_exp)
decomp = "True"
break
elif (k == len(r2) - 1 or (isdigit(r2[k]) and r2[k + 1] != "+")) and decomp == "Maybe":
decomp = "False"
j += k - j
break
else:
decomp = "Maybe"
k += 1
if decomp == "True":
break
# These expressions don't match up, can't be decomposed
if decomp == "False" and ((j == i and r2[j] != r1[i]) or (j == i + len(sub_exp))):
return False
# These expressions have at least one case of decomposition
# but we need to make sure there are no other differences
elif decomp == True and j == i + len(sub_exp):
if r2[j] != r1[i]:
# the expressions have another difference
return False
# The expressions only differ by one or more cases of decomposition, all good
return True
def get_operands(r, operator):
# op_list: list of operands for either "*"" or "+""
op_list = []
for i in operator:
if isdigit(i):
op_list.append(i)
return op_list
def creatWordDict(self):
"""
creat self.word_dict && self.word_list according to self.infolist, select top 10% of the keywords
:return: Null
"""
temp_dict = {}
word_num = 0
for info_item in self.infolist:
# print(info_item.title)
# print(info_item.text)
# info_weight 待调
info_weight = info_item.attitudecount + 2 * info_item.commentcount
# print(info_weight)
keynum = math.ceil(len(info_item.title) / 5)
keywords = analyse.extract_tags(info_item.title, topK=keynum)
for word_item in keywords:
if word_item in temp_dict:
temp_dict[word_item] += 5
else:
temp_dict[word_item] = info_weight + 5
text_list = re.split('，|。|？|！', info_item.text)
for sentence in text_list:
# print(sentence)
keynum = math.ceil(len(sentence) / 10)
keywords = analyse.extract_tags(sentence, topK=keynum)
# print(keywords)
for word_item in keywords:
if word_item in temp_dict:
temp_dict[word_item] += 1
else:
temp_dict[word_item] = 1
word_temp_list = sorted(temp_dict.items(), key=lambda item: item[1], reverse=True) # 出来后是列表形式
topnum = math.ceil(len(word_temp_list) / 10)
# creat self.word_dict
i = 0
while word_num < topnum:
w = word_temp_list[i][0]
if not isdigit(w) and w not in stopwords:
self.word_dict[w] = word_num
self.word_list.append(w)
word_num += 1
# print(word_temp_list[i])
i += 1
def predict(self, license_plate):
plate = ""
if len(license_plate.subrects) == 7:
for index in range(3):
character = license_plate.subrects[index]
character_image = license_plate.image.crop(
Point(character.x, character.y),
Point(character.x + character.w - 1,
character.y + character.h - 1))
if character_image.data.size > 0 and character_image.data is not None:
letter = self.letter_recognizer.predict(
character_image, True)
if isalpha(letter):
plate += letter
else:
plate += "a"
else:
plate += "a"
for index in range(3, 7):
character = license_plate.subrects[index]
character_image = license_plate.image.crop(
Point(character.x, character.y),
Point(character.x + character.w - 1,
character.y + character.h - 1))
if character_image.data.size > 0 and character_image.data is not None:
digit = self.number_recognizer.predict(character_image)
if isdigit(digit):
plate += digit
else:
plate += str(1)
else:
plate += str(1)
else:
plate = "AAA1111"
return plate
# /** # * @author [Limm-jk] # * @email [[email protected]] # * @create date 2020-04-13 15:46:31 # * @modify date 2020-04-13 15:46:31 # * @desc [description] # */ from numpy.core.defchararray import isdigit import sys poke_arr = [] x, y = map(int, sys.stdin.readline().split()) for _ in range(x): poke_arr.append(sys.stdin.readline().rstrip()) for _ in range(y): input1 = sys.stdin.readline().rstrip() if (isdigit(input1)): print(poke_arr[int(input1) - 1]) else: print(poke_arr.index(input1) + 1)
license_plate = character_validator.adjust_characters(license_plate, image_original, image_width)
correct_plate = config_data.license_plates_text
index = 0
for character in license_plate.subrects:
index += 1
character_image = license_plate.image.crop(Point(character.x, character.y), Point(character.x + character.w - 1, character.y + character.h - 1))
if character_image.data.size > 0 and character_image.data is not None:
image = Image(image=character_image.data)
image = image.resize(200, 200)
character = chr(image.plot())
image = character_image.resize(character_original_width, character_original_height)
if isalpha(character) or isdigit(character):
if isalpha(character) or character == "1" or character == "0":
if character != "1" and character != "0":
letter_labels.append(ord(character))
letter_images.append(image.data.flatten())
print "Character: " + character
else:
if character == "1":
character = "i"
if character == "0":
character = "o"
letter_labels.append(ord(character))
letter_images.append(image.data.flatten())
print "Character: " + character
def writeIntoFile(self, outFilePath, similarContentDic):
# 关联的数组的个数
LinkedArraySize = len(similarContentDic)
AllArrayDic = {}
if self.kind == 0:
kind_str = "one day"
elif self.kind == 1:
kind_str = "one hour"
else:
kind_str = "ten minutes"
# 文件中的节点相连数组总个数
AllArrayDic['LinkedArraySize'] = LinkedArraySize
AllArrayDic['Date'] = self.window_date.strftime("%Y-%m-%d %H:%M:%S")
AllArrayDic['TimeWindowLen'] = kind_str
count_array = 1
for i in similarContentDic:
nodeSize = len(similarContentDic[i])
AllNodeDic = {}
keywords_list = []
keywords_dict = {}
# 一个数组的节点个数
AllNodeDic['nodeSize'] = nodeSize
count_node = 1
# 得到所有节点名字的数组
AllNodeNameArray = [str(data.username) for data in similarContentDic[i]]
AllNodeDic['AllNodeNameArray'] = AllNodeNameArray
for data in similarContentDic[i]:
nodeAttr = 'node_' + str(count_node)
ID = int(data.id)
nodeName = str(data.username)
# print(nodeName)
# 去掉自身名字
# linkedNodeNameArray = AllNodeNameArray.copy()
# linkedNodeNameArray.remove(nodeName)
nodeInfo = {}
nodeDic = {}
nodeDic['ID'] = ID
nodeDic['nodeName'] = nodeName
# nodeDic['linkedNodeNameArray'] = linkedNodeNameArray
nodeDic['nodeInfo'] = nodeInfo
# 将节点信息添加到字典中
AllNodeDic[nodeAttr] = nodeDic
count_node += 1
# 提取题目关键词并将其加入keywords_dict字典中
keynum = math.ceil(len(data.title) / 5)
keywords = analyse.extract_tags(data.title, topK=keynum)
# print(keynum)
# print(keywords)
for word_item in keywords:
if word_item in keywords_dict:
keywords_dict[word_item] += 2
else:
keywords_dict[word_item] = 2
# 提取text关键词并将其加入keywords_dict字典中
keynum = math.ceil(len(data.text) / 30)
keywords = analyse.extract_tags(data.text, topK=keynum)
# print(keynum)
# print(keywords)
for word_item in keywords:
if word_item in keywords_dict:
keywords_dict[word_item] += 1
else:
keywords_dict[word_item] = 1
LinkedarrayAttr = 'LinkedArray_' + str(count_array)
# 一个相连数组信息添加到字典
AllArrayDic[LinkedarrayAttr] = AllNodeDic
count_array += 1
# 处理关键词字典
keywords_temp_list = sorted(keywords_dict.items(), key=lambda item: item[1], reverse=True) # 出来后是列表形式
if len(keywords_temp_list) > 20:
topnum = 10
else:
topnum = math.ceil(len(keywords_temp_list) / 2)
# creat self.word_dict
i = 0
word_num = 0
while word_num < topnum and i < len(keywords_temp_list):
w = keywords_temp_list[i][0]
if not isdigit(w) and w not in stopwords:
keywords_list.append(w)
word_num += 1
# print(keywords_temp_list[i])
i += 1
AllNodeDic['keywords'] = keywords_list
# 写入文件
with open(outFilePath, 'w', encoding='utf-8') as file_object:
json.dump(AllArrayDic, file_object, ensure_ascii=False, indent=4)
file_object.close()
logger.log(Logger.INFO, "Loading images to train classifiers.")
number_images = np.loadtxt(path_number_images, np.uint8)
number_labels = np.loadtxt(path_number_labels, np.float32)
number_labels = number_labels.reshape((number_labels.size, 1))
converted_images = []
labels = []
for index in range(len(number_images)):
image = number_images[index]
reshaped = Image(image=image.reshape((character_original_height, character_original_width)))
reshaped.binarize(adaptative=True)
mean_value = np.mean(reshaped.data)
if mean_value < 220:
if isdigit(chr(int(number_labels[index]))):
converted_images.append(reshaped)
labels.append(number_labels[index])
number_images = converted_images
number_labels = labels
letter_images = np.loadtxt(path_letter_images, np.uint8)
letter_labels = np.loadtxt(path_letter_labels, np.float32)
letter_labels = letter_labels.reshape((letter_labels.size, 1))
converted_images_l = []
labels_l = []
for index in range(len(letter_images)):
image = letter_images[index]
reshaped = Image(image=image.reshape((character_original_height, character_original_width)))
def ans(img):
pin = []
# image = cv2.imread(img, cv2.IMREAD_COLOR)
image = img
gray = cv2.cvtColor(image.copy(), cv2.COLOR_BGR2GRAY)
gray = cv2.fastNlMeansDenoising(gray, 100, 10, 7, 21)
ret, th = cv2.threshold(
gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
contours = cv2.findContours(
th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
contours = sorted(contours, key=lambda ctr: cv2.boundingRect(ctr)[0] + cv2.boundingRect(
ctr)[2] + (cv2.boundingRect(ctr)[1] + cv2.boundingRect(ctr)[3]))
for cnt in contours:
x, y, w, h = cv2.boundingRect(cnt)
if h < 40:
continue
digit = th[y:y + h, x:x + w]
resized_digit = cv2.resize(digit, (18, 18))
padded_digit = np.pad(resized_digit, ((5, 5), (5, 5)),
"constant", constant_values=0)
digit = padded_digit.reshape(1, 28, 28, 1)
digit = digit / 255.0
pred1 = model1.predict([digit])[0]
pred2 = model2.predict([digit])[0]
final_pred1 = np.argmax(pred1)
final_pred2 = np.argmax(pred2)
final_pred = final_pred2 if max(
pred1)*1.5 < max(pred2) else final_pred1
pred = int(max(max(pred1), max(pred2)) * 100)
if isdigit(chr(ascii_map[int(final_pred)])) or chr(ascii_map[int(final_pred)]) in ['O', 'o', 'I', '|', "T"]:
num = chr(ascii_map[int(final_pred)])
num = '0' if num in ['O', 'o'] else num
num = '1' if num in ['|', 'I'] else num
num = '7' if num in ["T"] else num
data = num + " " + str(int(pred)) + '%'
cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 1)
font = cv2.FONT_HERSHEY_SIMPLEX
fontScale = 0.5
color = (255, 0, 0)
thickness = 1
cv2.putText(image, data, (x, y - 5), font,
fontScale, color, thickness)
pin.append(num)
# print(data)
# cv2.imshow('Predictions', image)
# cv2.waitKey(0)
return pin[-6::]