def upload():
# check if user logged_in
if not session['logged_in']:
return render_template('home.html')
# once logged_in, user is able to store images to his own folder
# under image_repo directory
tmp = 'image_repo/' + session['username'] + '/'
savePath = os.path.join(SER_ROOT, tmp)
if not os.path.isdir(savePath):
os.mkdir(savePath)
else:
print("Failed to create directory\n")
item = request.files.getlist('file')
if len(item) == 0:
return render_template('uploadImage.html')
# set upload time
for image in request.files.getlist('file'):
filename = image.filename
print(filename.split('.')[1])
if filename.split('.')[1] in ['jpg', 'png', 'jpeg']:
image.save(savePath + filename)
os.utime(savePath + filename, None)
files = os.listdir(savePath)
s = []
for file in files:
s.append(str(file) + '\n')
photoName = savePath + '/' + file
cut.cut(photoName)
return render_template("complete.html")
def main(origin_dir, target_dir):
dir_name = basename(origin_dir)
# handle_video(origin_dir, target_dir)
target_dir = os.path.join(target_dir, dir_name)
output_dir = os.path.join(target_dir, 'cluster', 'output')
if not os.path.exists(output_dir):
makedirs(output_dir)
cut([join(target_dir, d) for d in listdir(target_dir) if d != 'cluster'])
do_cluster(os.path.join(target_dir, 'cluster'))
def balance(graph):
'''parame: graph, a DAG its.__class__ == nx.DiGraph
return: r, removed edges set so makr the input graph a b-structure
'''
# 只处理整数形式的图，每一个整数对应的节点可以在后面查到
# 输入进来的图应该是连通的，如果存在非连通图，minimum_edge_cut就会产生问题
assert nx.is_directed_acyclic_graph(graph),\
"The target graph you want to banlance is not a DAG"
r = [] # removed set
if check(graph):
return r
#非B-Stucture时，一直循环下去
# BUGY: 如果cs为空呢，那么不可能有两个图返回来，这时候怎么办
print "\nCutting Graph"
cs, g1, g2 = cut(graph)
r = balance(g1) + balance(g2) + cs
csl = []
for eachEdge in cs:
under_check_graph = graph.copy()
under_check_graph.remove_edges_from(r)
under_check_graph.add_edges_from(csl)
under_check_graph.add_edge(eachEdge[0], eachEdge[1])
if check(under_check_graph):
print "Edge: %s added back" % str(eachEdge)
csl.append(eachEdge)
graph.add_edge(eachEdge[0], eachEdge[1])
for eachEdge in csl:
r.remove(eachEdge)
print "Removed Edge Set: %s" % str(r)
return r
def balance(graph):
'''parame: graph, a DAG its.__class__ == nx.DiGraph
return: r, removed edges set so makr the input graph a b-structure
'''
# 只处理整数形式的图，每一个整数对应的节点可以在后面查到
# 输入进来的图应该是连通的，如果存在非连通图，minimum_edge_cut就会产生问题
assert nx.is_directed_acyclic_graph(graph),\
"The target graph you want to banlance is not a DAG"
r = [] # removed set
if check(graph):
return r
#非B-Stucture时，一直循环下去
# BUGY: 如果cs为空呢，那么不可能有两个图返回来，这时候怎么办
print "\nCutting Graph"
cs, g1, g2 = cut(graph)
r = balance(g1) + balance(g2) + cs
csl = []
for eachEdge in cs:
under_check_graph = graph.copy()
under_check_graph.remove_edges_from(r)
under_check_graph.add_edges_from(csl)
under_check_graph.add_edge(eachEdge[0],eachEdge[1])
if check(under_check_graph):
print "Edge: %s added back" % str(eachEdge)
csl.append(eachEdge)
graph.add_edge(eachEdge[0],eachEdge[1])
for eachEdge in csl:
r.remove(eachEdge)
print "Removed Edge Set: %s" % str(r)
return r
def start(input, output):
valArr = np.zeros([ROW, COL])
with open(input, 'rb') as f:
for i in range(ROW):
for j in range(COL):
val = list(struct.unpack(">1f", f.read(4)))[0]
valArr[i][j] = float(val)
interpVal = U.interp(valArr, tX, tY)
valTable = interpVal.flatten()
img = Image.new("RGBA", resolution + 1, (0, 0, 0, 0))
length = len(latTable)
for i in range(length):
val = valTable[i]
img.putpixel(mercatorMap[i], COLOR.color1(val))
img = CUT.cut(img)
img = LEGEND.draw(img, 'l1')
img.save(output + ".png", 'PNG')
def WaitUntil(waitImage, beforeDelay, afterDelay):
Delay(beforeDelay)
while True:
srcImage = cut(savePicture=False)
pos = ac.find_template(srcImage, waitImage)
if pos is not None and pos['confidence'] > 0.9:
break
Delay(1.0)
Delay(afterDelay)
def main(target_dir):
handle_video(target_dir)
# target_dir = os.path.join(target_dir, os.path.basename(origin_dir))
for f in os.listdir(target_dir):
print('Task: {} start'.format(f))
try:
scenario_dir = os.path.join(target_dir, f)
output_dir = os.path.join(scenario_dir, 'cluster', 'output')
cut(scenario_dir, output_dir)
cluster_dir = os.path.join(scenario_dir, 'cluster', 'cluster')
makedirs(cluster_dir)
do_cluster(os.path.join(scenario_dir, 'cluster'))
zip_file = os.path.join(
scenario_dir, '{}.zip'.format(os.path.basename(scenario_dir)))
do_zip(zip_file, cluster_dir)
except Exception as e:
print('Task: {} error'.format(f))
print(e)
def CLCS(A, B):
bestLCS = 0
for i in range(len(A)):
currLCS = LCS(cut(A, i), B)
if currLCS > bestLCS:
bestLCS = currLCS
if bestLCS == len(A): # catches a perfect match
return bestLCS
return bestLCS
def wait_until_next_phase():
while True:
src_image = cut(save_picture=False)
pos = ac.find_template(
src_image, master_image if remain_phase_number > 0 else ban_image)
if pos is not None and pos['confidence'] > 0.95:
break
等待(1.0)
等待(0.2 if remain_phase_number > 0 else 1.0)
def texture_transfer(texture, target, patchsize, overlap, tol):
outsize = size(target)
outsize = [outsize[1], outsize[2]]
xtrim = patchsize - (outsize[1] - ((patchsize - overlap) * math.floor(
(outsize[1] - patchsize) /
(patchsize - overlap)) + 1 + patchsize - overlap))
ytrim = patchsize - (outsize[0] - ((patchsize - overlap) * math.floor(
(outsize[0] - patchsize) /
(patchsize - overlap)) + 1 + patchsize - overlap))
outsize_ = outsize
outsize_[0] = outsize_[0] + ytrim
outsize_[1] = outsize_[1] + xtrim
if (size(target)[3] == 1):
imout = np.zeros((outsize_))
else:
imout = np.zeros(
(int(outsize_[0]), int(outsize_[1]), int(size(target)[3])))
for i in xrange(0, outsize_[0] - patchsize + 1, patchsize - overlap):
print i
for j in xrange(0, outsize_[1] - patchsize + 1, patchsize - overlap):
if (i == 0 and j == 0):
pos = find_mindelta(texture, target, [0, 0], patchsize, tol)
imout[i:i + patchsize - 1, j:j + patchsize -
1, :] = texture[int(pos[1]):int(pos[1]) + patchsize - 1,
int(pos[2]):int(pos[2]) + patchsize -
1, :]
else:
if (i > outsize[0] - patchsize + 1):
i_new = outsize[0] - patchsize + 1
else:
i_new = copy(i)
if (j > outsize[1] - patchsize + 1):
j_new = outsize[1] - patchsize + 1
else:
j_new = copy(j)
currentpos = [i_new, j_new]
patchpos = find_mindelta(texture, target, currentpos,
patchsize, tol)
imout = cut(texture, patchsize, overlap, imout, patchpos,
currentpos)
# imout=imout[1:outsize[1],1:outsize[2],:]
return imout
def bot():
token = read_token()
bible = cut()
client = discord.Client()
@client.event
async def on_ready():
channel = client.get_channel(ID)
# await channel.send(bible)
if (len(bible) > 2000):
def build(do_publish=0):
print("Build")
print("-----")
outfile = 'hardware.json'
oldfile = 'backup.json'
print("Backup current json...")
oldjso = None
if os.path.isfile(outfile) and not os.path.isfile(oldfile):
os.rename(outfile, oldfile)
errorlevel = 0
errorlevel += parse_machines()
if errorlevel == 0:
errorlevel += vitamins()
if errorlevel == 0:
errorlevel += cut()
if errorlevel == 0:
errorlevel += printed()
if errorlevel == 0:
errorlevel += assemblies()
if errorlevel == 0:
errorlevel += machines()
if errorlevel == 0:
errorlevel += guides()
catalogue()
if errorlevel == 0 and do_publish > 0:
publish()
# if everything is ok then delete backup - no longer required
if errorlevel == 0:
os.remove(oldfile)
try:
if sys.platform == "darwin":
check_output([
'osascript', '-e',
'display notification "Build Complete" with title "Build Process"'
])
except:
print("Exception running osascript")
print("")
print("==============")
print("Build Complete")
return errorlevel
def build(do_publish=0):
print("Build")
print("-----")
outfile = 'hardware.json'
oldfile = 'backup.json'
print("Backup current json...")
oldjso = None
if os.path.isfile(outfile) and not os.path.isfile(oldfile):
os.rename(outfile, oldfile)
errorlevel = 0
errorlevel += parse_machines()
if errorlevel == 0:
errorlevel += vitamins()
if errorlevel == 0:
errorlevel += cut()
if errorlevel == 0:
errorlevel += printed()
if errorlevel == 0:
errorlevel += assemblies()
if errorlevel == 0:
errorlevel += machines()
if errorlevel == 0:
errorlevel += guides()
catalogue();
if errorlevel == 0 and do_publish > 0:
publish()
# if everything is ok then delete backup - no longer required
if errorlevel == 0:
os.remove(oldfile)
try:
if sys.platform == "darwin":
check_output(['osascript','-e','display notification "Build Complete" with title "Build Process"'])
except:
print("Exception running osascript")
print("")
print("==============")
print("Build Complete")
return errorlevel
def train(n):
k = 16
import wave
code = []
for i in range(n):
wav_file=wave.open('{}'.format(i+1) + '.wav')
raw_frames = wav_file.readframes(-1)
num_frames = wav_file.getnframes()
num_channels = wav_file.getnchannels()
sample_rate = wav_file.getframerate()
sample_width = wav_file.getsampwidth()
temp_buffer = np.empty((num_frames, num_channels, 4), dtype=np.uint8)
raw_bytes = np.frombuffer(raw_frames, dtype=np.uint8)
temp_buffer[:, :, :sample_width] = raw_bytes.reshape(-1, num_channels,
sample_width)
temp_buffer[:, :, sample_width:] = \
(temp_buffer[:, :, sample_width-1:sample_width] >> 7) * 255
frames = temp_buffer.view('<i4').reshape(temp_buffer.shape[:-1])
#sample_rate = wav_file.getframerate()
sample_rate = 16000;
signal=frames[::3,0]
sample_rate, signal = cut.cut(sample_rate, signal)
# sample_rate, signal = scipy.io.wavfile.read('{}'.format(i+1) + '.wav')
v = mfcc(signal, sample_rate)
code.append(vqlbg(v, k))
print(i)
if n == 3:
d1 = disteu(code[0], code[1])
d2 = disteu(code[1], code[2])
d3 = disteu(code[2], code[0])
dk = []
dk.append(sum(d1.min(1)) / d1.shape[0])
dk.append(sum(d2.min(1)) / d2.shape[0])
dk.append(sum(d3.min(1)) / d3.shape[0])
dmax = max(dk)
dmin = min(dk)
else:
dmax = 0
dmin = 0
return code, dmax, dmin
def scan(root, debug):
# 文件名正则
filePattern = re.compile("^[a-zA-Z0-9]+$")
# 上衣文件名正则
clothingPattern = re.compile(".*上衣")
clothingCount = 0
pantsCount = 0
# 先删除文件
for dp, dn, fs in os.walk(root):
for f in fs:
# 分割图像并进行存储
filename = f.split('.')[0]
srcPath = os.path.join(dp, f)
if not filePattern.match(filename):
os.remove(srcPath)
for dp, dn, fs in os.walk(root):
for f in fs:
# 分割图像并进行存储
filename = f.split('.')[0]
srcPath = os.path.join(dp, f)
if filePattern.match(filename):
cutPath = os.path.join(dp, "%s_cut.jpg" % filename)
maskPath = os.path.join(dp, "%s_mask.jpg" % filename)
if clothingPattern.match(srcPath):
isClothing = True
clothingCount = clothingCount + 1
else:
isClothing = False
pantsCount = pantsCount + 1
print '---->'
print '%s is cutting' % srcPath
image = cv2.imread(srcPath)
mask, cutImage = cut(image,
isClothing,
dp,
filename,
isDebug=debug)
cv2.imwrite(cutPath, cutImage)
cv2.imwrite(maskPath, mask)
print '<----'
else:
os.remove(srcPath)
print '----------completed! ----------'
print '----------clothings: %d ----------' % clothingCount
print '----------pants: %d ----------' % pantsCount
def build(do_publish=0):
print("Build")
print("-----")
outfile = 'hardware.json'
oldfile = 'backup.json'
print("Backup current json...")
oldjso = None
if os.path.isfile(outfile) and not os.path.isfile(oldfile):
os.rename(outfile, oldfile)
errorlevel = 0
errorlevel += parse_machines()
if errorlevel == 0:
errorlevel += vitamins()
if errorlevel == 0:
errorlevel += cut()
if errorlevel == 0:
errorlevel += printed()
if errorlevel == 0:
errorlevel += assemblies()
if errorlevel == 0:
errorlevel += machines()
if errorlevel == 0:
errorlevel += guides()
catalogue();
if errorlevel == 0 and do_publish > 0:
publish()
# if everything is ok then delete backup - no longer required
if errorlevel == 0:
os.remove(oldfile)
return errorlevel
def build(do_publish=0):
print("Build")
print("-----")
outfile = 'hardware.json'
oldfile = 'backup.json'
print("Backup current json...")
oldjso = None
if os.path.isfile(outfile) and not os.path.isfile(oldfile):
os.rename(outfile, oldfile)
errorlevel = 0
errorlevel += parse_machines()
if errorlevel == 0:
errorlevel += vitamins()
if errorlevel == 0:
errorlevel += cut()
if errorlevel == 0:
errorlevel += printed()
if errorlevel == 0:
errorlevel += assemblies()
if errorlevel == 0:
errorlevel += machines()
if errorlevel == 0:
errorlevel += guides()
catalogue()
if errorlevel == 0 and do_publish > 0:
publish()
# if everything is ok then delete backup - no longer required
if errorlevel == 0:
os.remove(oldfile)
return errorlevel
def test(name, code):
import wave
wav_file = wave.open(name + '.wav')
raw_frames = wav_file.readframes(-1)
num_frames = wav_file.getnframes()
num_channels = wav_file.getnchannels()
sample_rate = wav_file.getframerate()
sample_width = wav_file.getsampwidth()
temp_buffer = np.empty((num_frames, num_channels, 4), dtype=np.uint8)
raw_bytes = np.frombuffer(raw_frames, dtype=np.uint8)
temp_buffer[:, :, :sample_width] = raw_bytes.reshape(
-1, num_channels, sample_width)
temp_buffer[:, :, sample_width:] = \
(temp_buffer[:, :, sample_width-1:sample_width] >> 7) * 255
frames = temp_buffer.view('<i4').reshape(temp_buffer.shape[:-1])
#sample_rate = wav_file.getframerate()/3
sample_rate = 16000
signal = frames[::3, 0]
sample_rate, signal = cut.cut(sample_rate, signal)
v = mfcc(signal, sample_rate)
distmin = float('inf')
dist = 0
for i in range(len(code)):
d = disteu(v, code[i])
dist += sum(d.min(1)) / d.shape[0]
dist = dist / len(code)
# dist = sum(d.min(1)) / d.shape[0]
#
# if dist < distmin:
# distmin = dist
print(dist)
if dist < 4.0:
return True
else:
return False
def detect(self,
img,
min_size=40,
factor=0.709,
score_threshold=[0.5, 0.5, 0.5],
iou_threshold=[0.5, 0.7, 0.7, 0.7]):
'''
:param img: numpy array (shape [height, width, 3] )
:return: boxes: numpy array (shape [N, 5])
[h0, w0, h1, w1, score]
landmarks: numpy array (shape [N, 10]
[h_leye, h_reye, h_nose, h_lmouth, h_rmouth,
w_leye, w_reye, w_nose, w_lmouth, w_rmouth]
'''
# Stage 1
h, w, _ = img.shape
min_l = min(h, w)
m = 12. / min_size
min_l = min_l * m
scales = []
while min_l >= 12:
scales.append(m)
min_l *= factor
m *= factor
proposals_total = np.zeros((0, 9), dtype=np.float32)
for scale in scales:
hs = int(np.round(h * scale))
ws = int(np.round(w * scale))
img_resize = cv2.resize(img, (ws, hs),
interpolation=cv2.INTER_LINEAR)
img_resize = (img_resize - 127.5) * 0.0078125
img_resize = img_resize[np.newaxis, :, :, :]
try:
cls_prob, bbox_reg = self.pnet.predict(img_resize)
except:
continue
prob = cls_prob[..., 1]
idx_pos = np.where(prob >= score_threshold[0])
prob_pos = prob[idx_pos].reshape([-1, 1])
h0 = idx_pos[1].reshape([-1, 1])
w0 = idx_pos[2].reshape([-1, 1])
bbox_pos = np.concatenate((h0, w0, h0 + 5.5, w0 + 5.5), axis=1)
bbox_pos = np.round(bbox_pos * 2 / scale)
bbox_reg_pos = bbox_reg[idx_pos]
proposals = np.concatenate((bbox_pos, prob_pos, bbox_reg_pos),
axis=1)
proposals = proposals[nms(proposals, iou_threshold[0])]
proposals_total = np.concatenate((proposals_total, proposals),
axis=0)
if len(proposals_total) == 0:
return (np.zeros(
(0, 5), dtype=np.float32), np.zeros((0, 10), dtype=np.float32))
proposals_total = proposals_total[nms(proposals_total,
threshold=iou_threshold[1])]
proposals_total = bbreg(proposals_total)
# Stage 2
proposals_total = rerec(proposals_total)
boxes = proposals_total[:, 0:4].astype(np.int32)
input = cut(img, boxes, h, w, 24)
input = (input - 127.5) * 0.0078125
try:
cls_prob, bbox_reg = self.rnet.predict(input)
except:
return (np.zeros(
(0, 5), dtype=np.float32), np.zeros((0, 10), dtype=np.float32))
prob = cls_prob[..., 1]
idx_pos = np.where(prob >= score_threshold[1])
prob_pos = prob[idx_pos].reshape([-1, 1])
bbox_pos = boxes[idx_pos]
bbox_reg_pos = bbox_reg[idx_pos]
proposals_total = np.concatenate((bbox_pos, prob_pos, bbox_reg_pos),
axis=1)
if len(proposals_total) == 0:
return (np.zeros(
(0, 5), dtype=np.float32), np.zeros((0, 10), dtype=np.float32))
proposals_total = proposals_total[nms(proposals_total,
threshold=iou_threshold[2])]
proposals_total = bbreg(proposals_total)
# Stage 3
proposals_total = rerec(proposals_total)
boxes = proposals_total[:, 0:4].astype(np.int32)
input = cut(img, boxes, h, w, 48)
input = (input - 127.5) * 0.0078125
try:
cls_prob, bbox_reg, landmark_reg = self.onet.predict(input)
except:
return (np.zeros(
(0, 5), dtype=np.float32), np.zeros((0, 10), dtype=np.float32))
prob = cls_prob[..., 1]
idx_pos = np.where(prob >= score_threshold[2])
prob_pos = prob[idx_pos].reshape([-1, 1])
bbox_pos = boxes[idx_pos]
# refine landmark
height = bbox_pos[:, 2:3] - bbox_pos[:, 0:1] + 1
width = bbox_pos[:, 3:4] - bbox_pos[:, 1:2] + 1
landmark = landmark_reg[idx_pos]
landmark[:,
0:5] = np.round(bbox_pos[:, 0:1] + landmark[:, 0:5] * height)
landmark[:,
5:10] = np.round(bbox_pos[:, 1:2] + landmark[:, 5:10] * width)
bbox_reg_pos = bbox_reg[idx_pos]
proposals_total = np.concatenate((bbox_pos, prob_pos, bbox_reg_pos),
axis=1)
if len(proposals_total) == 0:
return (np.zeros(
(0, 5), dtype=np.float32), np.zeros((0, 10), dtype=np.float32))
proposals_total = bbreg(proposals_total)
idx_keep = nms(proposals_total, threshold=iou_threshold[2], type='Min')
return proposals_total[idx_keep][:, 0:5], landmark[idx_keep]
def DOS():
W, H, N, RECTANGLES = di.Read()
RECTANGLES = u.merge_sort(RECTANGLES)
ORDER = 1
CANVAS = c.Canvas(0, 0, W, H, ORDER)
cu.cut(W, H, N, RECTANGLES, CANVAS, ORDER)
import numpy
import random
import matplotlib
matplotlib.use('pdf')
import matplotlib.pyplot as plt
import randvec as rd
import cellmain as cell
import rulesmain as rul
import plotmain as plm
import cut
import big
#M=512#liczba wierszy
#N=256 #liczba kolumn
cutout = cut.cut(112, 450)
#rd.setgen(A, N, vec)
BG = cut.findbg(cut.reduce(cutout, 100, 240))
#cutout = cut.cut(112, 0)
#vec=big.put(BG, cut.reduce(cutout, 75, 240))
#N=len(vec)
M = 100
A = numpy.zeros((M, N), dtype='i') #macierz MxN inicjowana zerami
#rd.setgen(A, N, vec)
rd.randgen(A, N)
#for rlnbr in range(1,128):
rule = rul.rules(111)
cell.evolve(A, N, M, rule)
plm.plotspec(A, "show")
def 宝具(user, 手动选卡=False, 敌对目标=0, 选卡策略=[]):
global remain_phase_number
if 敌对目标 > 0:
tap_key('234'[敌对目标 - 1], 普通操作时间)
tap_key('\n', 进入选卡界面时间)
index = 查找从者(user)
# 识别5张牌都是啥
# 5张牌在标准化图片（992*611）上的位置：
# （10+200X,300）-（190+200X,5320），X=0,1,2,3,4
if 自动选卡策略:
src_image = cv2.resize(cut(save_picture=False), (992, 611))
found_card_list = []
for i in range(5):
card_area_image = src_image[300:520, 10 + 200 * i:190 + 200 * i]
found = '未定义-未定义'
for servant_name in 阵容[:3]:
if servant_name != '':
for color in ['红', '蓝', '绿']:
for test_image in 阵容卡组[servant_name][color]:
pos = ac.find_template(card_area_image, test_image)
# 这里有一个助战图标的问题
if pos is not None and pos['confidence'] > 0.8:
found = '%s-%s' % (servant_name, color)
break
break
break
found_card_list.append(found)
print('检测结果:')
print(found_card_list)
if not 手动选卡:
result = []
if 自动选卡策略:
for 策略 in 选卡策略:
# 试图匹配策略
result = 选卡匹配(策略, found_card_list)
if len(result) > 0:
break
if len(result) == 0:
result = ['WER'[index], 'S', 'D']
tap_key(result[0], 普通操作时间)
tap_key(result[1], 普通操作时间)
if not 自动选卡策略:
tap_key(result[2], treasure_time(user) + 换面时间)
else:
tap_key(result[2], 普通操作时间)
remain_phase_number -= 1
print('剩余面数：%d' % remain_phase_number)
wait_until_next_phase()
if remain_phase_number <= 0:
remain_phase_number = 3
else:
tap_key('WER'[index], 普通操作时间)
等待操作()
等待(treasure_time(user) + 换面时间)
if user == '大英雄':
阵容[index] = 阵容[3]
阵容[3] = 阵容[4]
阵容[4] = 阵容[5]
阵容[5] = ''
def build(argv):
doCatalogue = True
doQuick = False
doPublish = False
try:
opts, args = getopt.getopt(argv, "hcqp", [])
except getopt.GetoptError:
print 'build.py -h -c -q -p'
print ''
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'Usage: -h -c -q -p'
print ''
print ' -c Skip catalogue'
print ' -p Publish: auto commit and push to git'
print ' -q Quick build - skip assemblies, guide and catalogue'
sys.exit()
if opt in ("-c"):
doCatalogue = False
if opt in ("-q"):
doQuick = True
doCatalogue = False
if opt in ("-p"):
doPublish = True
print("Build")
print("-----")
outfile = 'hardware.json'
oldfile = 'backup.json'
print("Backup current json...")
oldjso = None
if os.path.isfile(outfile) and not os.path.isfile(oldfile):
os.rename(outfile, oldfile)
errorlevel = 0
errorlevel += parse_machines()
if errorlevel == 0:
errorlevel += vitamins()
if errorlevel == 0:
errorlevel += cut()
if errorlevel == 0:
errorlevel += printed()
if errorlevel == 0 and not doQuick:
errorlevel += assemblies()
if errorlevel == 0:
errorlevel += machines()
if errorlevel == 0 and not doQuick:
errorlevel += guides()
if doCatalogue and not doQuick:
catalogue()
if errorlevel == 0 and doPublish > 0:
publish()
# if everything is ok then delete backup - no longer required
if errorlevel == 0:
os.remove(oldfile)
try:
if sys.platform == "darwin":
check_output([
'osascript', '-e',
'display notification "Build Complete" with title "Build Process"'
])
except:
print("Exception running osascript")
print("")
print("==============")
print("Build Complete")
return errorlevel
def main():
best_score = 0
# Load all stations from the csv file
skip = False
stations_data = load_data("data/StationsNationaal.csv", skip)
# Prompt user for necessary input
user_choices = user_interface(stations_data)
# Load the connections data from the csv file (Heel Nederland or Noord- en
# Zuid Holland), skipping a station if necessary
data_list = load_data(user_choices["data"], user_choices["skip"])
for _ in range(user_choices["attempts"]):
# Load all stations as objects into a dictionary
stations_objects = load_stations(data_list, stations_data)
# Load all connections into a dictionary
connection_objects = load_connections(
data_list, stations_objects, user_choices["change_connections"])
# Generate a random solution with chosen heuristics
solution = random_solution(stations_objects, connection_objects,
user_choices)
# Run the heuristic of "cutting" trains, if the user chose this option
if user_choices["cut_connections"]:
solution = cut(solution)
# Run the heuristic of "pasting" trains, if the user chose this option
if user_choices["paste_connections"]:
solution = paste(solution, user_choices["max_minutes"])
# Delete empty trains from solution
solution = delete_trains(solution)
# Calculate the K of a solution with the given function
score = calculate(solution)
# Set score to new values
if score > best_score:
best_solution = solution
best_score = score
# Open outputfile
f = open("output.csv", "w")
f.write("random:\ntrein, lijnvoering\n")
counter = 0
# Write random solution to outputfile
for train in best_solution["trains"]:
counter += 1
f.write(f'trein_{counter}, "{train}"\n')
f.write(f"SCORE:{best_score}\n\n")
f.close()
# If simulated annealing is chosen
if user_choices["sim_annealing"] == True:
best_solution = simulated_annealing(solution, stations_objects,
user_choices)
# If heuristic cut is chosen
if user_choices["SA_cut_connections"]:
best_solution = cut(best_solution)
best_solution = delete_trains(best_solution)
better_score = calculate(best_solution)
# Open outputfile
f = open("output.csv", "a+")
f.write("simulated annealing:\ntrein, lijnvoering\n")
counter = 0
# Write simulated annealing solution to outputfile
for train in best_solution["trains"]:
counter += 1
f.write(f'trein_{counter}, "{train}"\n')
f.write(f"SCORE:{better_score}\n\n")
f.close()
# Draw the map
if user_choices["data"] == "data/ConnectiesHolland.csv":
draw_train_holland(best_solution, stations_objects)
else:
draw_train(best_solution, stations_objects)
# -*- coding: utf-8 -*-
# 每次将模拟器滑到适当的位置，然后cmd里敲回车
# 完成截图之后继续下一次操作
from cut import cut
from processor import process_cut
from pywinauto.win32functions import SetForegroundWindow, GetForegroundWindow
print('截图助手已启动')
print('输入已进行的次数：')
op = input()
try:
op = int(op)
except ValueError:
op = 0
while True:
print('已完成%d次截图，回车以开始下一次……' % op)
input()
original_window = GetForegroundWindow()
cut()
# process_cut(start_index=op*5)
SetForegroundWindow(original_window)
op += 1
# coding: utf8
import cv2
import numpy as np
import cut
image = cv2.imread('./images/13883/上衣/57f7cdb0f90a8310f062b13d.jpg')
rows, cols, _ = image.shape
resized = cv2.pyrDown(image, (rows / 2, cols / 2))
blured = cv2.medianBlur(resized, 3)
cutMask, cutImage = cut.cut(image, True, './test', 'pure', isDebug=True)
cv2.imshow('mask', cutMask)
cv2.waitKey(0)
cv2.imshow('cutImage', cutImage)
cv2.waitKey(0)
def appliquer_changements(id):
form = Validate_form(request.form)
cur = mysql.connection.cursor()
result = cur.execute("SELECT id, nom FROM sample ")
objets=cur.fetchall()
cur.close()
# Dictionnaire pour envoyer les infos a la page html
dict={}
dict['items']=objets
dict['form']=form
dict['tempo']=form
# On récupere les infos depuis notre base de donnée pour les afficher
cur = mysql.connection.cursor()
result=cur.execute("SELECT titre,artiste,doc FROM musique WHERE id=%s ",[id])
item=cur.fetchone()
cur.close()
dict['doc']= item['doc']
dict['titre']=item['titre']
dict['id']=id
dict['tempo'] = 0
if request.method == 'POST':
original_filename = item['doc']
filename = form.filename.data
audio_data, sampling_rate = librosa.load('uploads/'+original_filename)
dict['tempo']=int(librosa.beat.tempo(audio_data,sampling_rate)[0])
# Evite les erreurs lors de la 1ere connexion
if filename == '' :
return render_template('mix.html',**dict)
# On recupere les donnée du formulaire
genre = form.genre.data
speed = form.speed.data
bass = form.bass.data
tone = form.tone.data
sampling_rate = form.sampling_rate.data
drum_funk = form.drum_funk.data
drum_blues = form.drum_blues.data
inst_debut=form.inst_debut.data
inst_fin=form.inst_fin.data
insert_time=form.insert_time.data
sample_id=form.sample_id.data
# Si les champs du formulaire sont vides, il faut quand même donner des valeurs acceptables
if inst_debut == '':inst_debut='0'
if inst_fin == '' : inst_fin = str(-1/sampling_rate)
print(drum_blues)
print(drum_funk)
cur = mysql.connection.cursor()
result=cur.execute("SELECT doc FROM sample WHERE id=%s ",[id])
nom_file=cur.fetchone()
cur.close()
# Changements sur le fichier audio
# Partie obsolète à cause du module librosa qui ne fonctionne plus
data_final=cut(audio_data,int(float(inst_debut)*sampling_rate), int(float(inst_fin)*sampling_rate))
if drum_funk == "activate" :
data_final = percu(data_final,son_percu="./uploads/Batterie_funk_1.wav")
if drum_blues == "activate":
data_final = percu(data_final,son_percu="./uploads/Batterie_blues_1.wav")
if insert_time != "":
data_final = sample_unique(data_final,"./uploads"+nom_file['doc'],insert_time)
if speed != 1:
data_final = librosa.effects.time_stretch(data_final, speed)
if tone != 0:
data_final = librosa.effects.pitch_shift(data_final, sampling_rate, tone)
if bass != 1:
data_final= change_bass(data_final,bass)
sf.write('downloads/'+filename, data_final, sampling_rate)
cur = mysql.connection.cursor()
cur.execute("INSERT INTO Musique(titre, artiste, doc) VALUES(%s, %s, %s)", (item['titre'], item['artiste'], filename))
mysql.connection.commit()
cur.close()
# Rajouter les validators dans les formulaires
flash('Changements appliqués à la musique', 'success')
return redirect(url_for('appliquer_changements', id=id))
return render_template('mix.html', **dict)
print('Вводите числа через "Enter" без пробелов, чтобы закончить введите "!"')
while x != '!':
x = input()
if x != '!':
a.append(int(x))
print('Ответ программы с визуализацией:')
while len(a) > 0:
for i in range(len(a)):
if len(a) > 1 and int(a[0]) < int(a[1]):
ft.sa(a)
print('exec sa')
print(" a -", *a)
print(" b -", *b)
fout.write('sa ')
ft2.ra(a)
a = ft5.cut(a)
print('exec ra')
print(" a -", *a)
print(" b -", *b)
fout.write('ra ')
b = ft1.pb(a, b)
a = ft5.cut(a)
print('exec pb')
print(" a -", *a)
print(" b -", *b)
fout.write('pb ')
for i in range(len(b)):
a = ft1.pa(b, a)
b = ft5.cut(b)
print('exec pa')
print(" a -", *a)
original = Image.open('cropped/{:s}.png'.format(dataset))
enhanced = Image.open('enhanced/{:s}.png'.format(dataset))
if 'test' in argv:
with open('offsets.txt') as od:
for line in od:
fields = line.split()
f = fields[2]
if f == dataset:
x0 = int(fields[3])
y0 = int(fields[4])
x = int(input('X: ')) - x0
y = int(input('Y: ')) - y0
zone = (x - r, y - r, x + r, y + r)
print(f'Extracting a {2 * r} square at {x}, {y}')
square = original.crop(zone)
cut(square, d, circle(d, margin, opacity=200), 'test.png')
quit()
if postprocess:
print('Post-processing for manuscript figures', dataset)
thresholded = Image.open('thresholded/{:s}.png'.format(dataset))
automaton = Image.open('automaton/{:s}.png'.format(dataset))
w, h = thresholded.size
factor = ow / w
sr = radius(dataset, factor) // 2
sd = 2 * sr
smask = circle(sd)
for treeID in trees:
pos, label = trees[treeID]
x, y = pos
zone = (x - r, y - r, x + r, y + r)
square = original.crop(zone)
def build(argv):
doCatalogue = True
doQuick = False
doPublish = False
try:
opts, args = getopt.getopt(argv,"hcqp",[])
except getopt.GetoptError:
print 'build.py -h -c -q -p'
print ''
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'Usage: -h -c -q -p'
print ''
print ' -c Skip catalogue'
print ' -p Publish: auto commit and push to git'
print ' -q Quick build - skip assemblies, guide and catalogue'
sys.exit()
if opt in ("-c"):
doCatalogue = False
if opt in ("-q"):
doQuick = True
doCatalogue = False
if opt in ("-p"):
doPublish = True
print("Build")
print("-----")
outfile = 'hardware.json'
oldfile = 'backup.json'
print("Backup current json...")
oldjso = None
if os.path.isfile(outfile) and not os.path.isfile(oldfile):
os.rename(outfile, oldfile)
errorlevel = 0
errorlevel += parse_machines()
if errorlevel == 0:
errorlevel += vitamins()
if errorlevel == 0:
errorlevel += cut()
if errorlevel == 0:
errorlevel += printed()
if errorlevel == 0 and not doQuick:
errorlevel += assemblies()
if errorlevel == 0:
errorlevel += machines()
if errorlevel == 0 and not doQuick:
errorlevel += guides()
if doCatalogue and not doQuick:
catalogue()
if errorlevel == 0 and doPublish > 0:
publish()
# if everything is ok then delete backup - no longer required
if errorlevel == 0:
os.remove(oldfile)
try:
if sys.platform == "darwin":
check_output(['osascript','-e','display notification "Build Complete" with title "Build Process"'])
except:
print("Exception running osascript")
print("")
print("==============")
print("Build Complete")
return errorlevel
def build(argv):
doCatalogue = True
doQuick = False
try:
opts, args = getopt.getopt(argv, "hcq", [])
except getopt.GetoptError:
print('build.py -h -c -q')
print('')
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print('Usage: -h -c -q')
print('')
print(' -c Skip catalogue')
print(' -q Quick build - skip assemblies, guide and catalogue')
sys.exit()
if opt in ("-c"):
doCatalogue = False
if opt in ("-q"):
doQuick = True
doCatalogue = False
print("Build")
print("-----")
print("Backup current json...")
oldjso = None
if os.path.isfile(config.paths['json']) and not os.path.isfile(
config.paths['jsonbackup']):
os.rename(config.paths['json'], config.paths['jsonbackup'])
errorlevel = 0
errorlevel += parse_machines()
if errorlevel == 0:
errorlevel += vitamins()
if errorlevel == 0:
errorlevel += cut()
if errorlevel == 0:
errorlevel += printed()
if errorlevel == 0 and not doQuick:
errorlevel += assemblies()
if errorlevel == 0:
errorlevel += machines()
if errorlevel == 0 and not doQuick:
errorlevel += guides()
if doCatalogue and not doQuick:
catalogue()
# if everything is ok then delete backup - no longer required
if errorlevel == 0 and os.path.isfile(config.paths['jsonbackup']):
os.remove(config.paths['jsonbackup'])
try:
if sys.platform == "darwin":
check_output([
'osascript', '-e',
'display notification "Build Complete" with title "Build Process"'
])
except:
print("Exception running osascript")
print("")
print("==============")
print("Build Complete")
if errorlevel > 0:
print("Error: " + str(errorlevel))
return errorlevel
def cutBtnHandler(self):
fig = cut.cut(self.lines, self.cutter)
for i, v in enumerate(fig):
self.drawLine(fig[i], fig[(i + 1) % len(fig)],
self.CUTTED_LINE_COLOR)
'4) Change the audio into mono and apply a different audio codec'
]),
]
exercice = inquirer.prompt(menu)['menu']
if '1)' in exercice:
print(
'Define the time the trimmed video will start as mm:ss knowing that the maximun is 10:25'
)
while 1:
time = str(input())
a = time.split(':')
if int(a[0]) < 11 and int(a[1]) < 61:
print('\nTime: ', time)
cut(time)
break
else:
print('Invalid option, choose another time')
elif '2)' in exercice:
histogram()
elif '3)' in exercice:
print(
'Define the width and height as (360x240) or the resolution as (720p)')
while 1:
size = str(input())
if 'p' in size or 'x' in size:
print('\nSize: ', size)
resize(size)
from cut import cut png = cut()
