def sim_red_data(reds, gains=None, shape=(10, 10), gain_scatter=.1):
""" Simulate noise-free random but redundant (up to differing gains) visibilities.
Args:
reds: list of lists of baseline-pol tuples where each sublist has only
redundant pairs
gains: pre-specify base gains to then scatter on top of in the
{(index,antpol): np.array} format. Default gives all ones.
shape: tuple of (Ntimes, Nfreqs). Default is (10,10).
gain_scatter: Relative amplitude of per-antenna complex gain scatter. Default is 0.1.
Returns:
gains: true gains used in the simulation in the {(index,antpol): np.array} format
true_vis: true underlying visibilities in the {(ind1,ind2,pol): np.array} format
data: simulated visibilities in the {(ind1,ind2,pol): np.array} format
"""
data, true_vis = {}, {}
ants = list(
set([
ant for bls in reds for bl in bls
for ant in [(bl[0], bl[2][0]), (bl[1], bl[2][1])]
]))
if gains is None: gains = {}
else: gains = deepcopy(gains)
for ant in ants:
gains[ant] = gains.get(ant, 1 + gain_scatter * noise(
(1, ))) * np.ones(shape, dtype=np.complex)
for bls in reds:
true_vis[bls[0]] = noise(shape)
for (i, j, pol) in bls:
data[(i, j, pol)] = true_vis[bls[0]] * gains[(i, pol[0])] * gains[
(j, pol[1])].conj()
return gains, true_vis, data
def TaylorSeries(H,t1,t2,order,noise,args=()):
'''Return an estimate of H using the Taylor expansion method to the
specified order.
H : exact Hamiltonian underlying the unitary evolution
t1 : first measurement of U
t2 : second measurement of U
order : order of Taylor series
noise : noise function to apply to unitaries
args : arguments to pass to noise function
return : (numpy array) estimate of Hamiltonian'''
dim=H.shape[0]
dt=t2-t1
# t1 : initial time
U_init=noise(expi(H,t1),*args)
# t2 : final time
U_final=noise(expi(H,t2),*args)
# propagator between t1 and t2
U=numpy.dot(U_final,dagger(U_init))
identity=Identity(dim)
A=identity-U
An=A
# zeroth order term
logU=numpy.zeros((dim,dim),dtype=complex)
for n in range(1,order+1):
logU=logU-An/float(n)
An=numpy.dot(An,A)
G=1j*logU/dt
return G
def make_hera_obs(aa,
lsts=DEFAULT_LSTS,
fqs=DEFAULT_FQS,
pols=['xx', 'yy'],
T_rx=150.,
inttime=10.7,
rfi_impulse=.02,
rfi_scatter=.001,
nsrcs=200,
gain_spread=.1,
dly_rng=(-20, 20),
xtalk=3.):
info = hera_cal.omni.aa_to_info(aa)
reds = info.get_reds()
ants = list(
set([
ant for bls in reds for bl in bls
for ant in [(bl[0], bl[2][0]), (bl[1], bl[2][1])]
]))
data, true_vis = {}, {}
if gains is None: gains = {}
else: gains = deepcopy(gains)
for ant in ants:
gains[ant] = gains.get(ant, 1 + gain_scatter * noise(
(1, ))) * np.ones(shape, dtype=np.complex)
for bls in reds:
true_vis[bls[0]] = noise(shape)
for (i, j, pol) in bls:
data[(i, j, pol)] = true_vis[bls[0]] * gains[(i, pol[0])] * gains[
(j, pol[1])].conj()
return gains, true_vis, data
def document_features(self, document):
print "document_features entered"
document_words = set(document)
features = {}
#for word in document:
#features['contains(%s)' % word] = (word in document_words)
#print document
f=open("xyz.txt","wb")
for word in document:
f.writelines(word+" ")
f.close()
de=open("xyz.txt","rb")
doc=de.read()
de.close()
print "ie_process called"
ie_preprocess(doc,self.t,self.chunker)
noise()
g=open("efgh.txt","rb")
st=g.readline()
while st!="":
features['contains(%s)' % st] = True
st=g.readline()
g.close()
return features
def Derivative(H,t,dt,noise,args=()): '''Return an estimate of H using the (3 point) time derivative method H : exact Hamiltonian underlying the unitary t : central time at which measurements are taken dt : timestep noise : noise function to apply to unitaries args : args to pass to noise function return : numpy array containing estimate of Hamiltonian''' # t : Central timestep U=noise(expi(H,t),*args) # t-h Uminus=noise(expi(H,t-dt),*args) # t+h Uplus=noise(expi(H,t+dt),*args) return (0.5j/dt)*numpy.dot((Uplus-Uminus),dagger(U))
def createStrata(heightMap,
height=64,
noise=lambda x, y: pnoise2(x, y, octaves=8)):
width, depth = heightMap.shape
block = Chunk(np.full((width, depth, height), AIR),
waterLevel=heightMap.metadata["waterLevel"])
for i, x in enumerate(tqdm(np.linspace(0, 1, num=width), "Soiling...")):
for j, z in enumerate(np.linspace(0, 1, num=depth)):
dirtThickness = noise(x, z) / 24 - 4
dirtTransition = heightMap[i, j]
stoneTransition = dirtTransition + dirtThickness
for y in range(height):
if y == 0:
blockType = LAVA
elif y <= stoneTransition:
blockType = STONE
elif y <= dirtTransition:
blockType = DIRT
else:
blockType = AIR
block[i, j, y] = blockType
return block
def createSurfaceLayer(block,
heightMap,
noise=lambda x, y: pnoise2(x, y, octaves=8)):
width, depth, height = block.shape
waterLevel = block.metadata["waterLevel"]
for i, x in enumerate(tqdm(np.linspace(0, 1, num=width), "Growing...")):
for j, z in enumerate(np.linspace(0, 1, num=depth)):
sandChance = noise(x, z) > 8
gravelChance = noise(x, z) > 12
y = heightMap[i, j]
blockAbove = block[i, j, y + 1]
if blockAbove == WATER and gravelChance:
block[i, j, y] = GRAVEL
elif blockAbove == AIR:
if y <= waterLevel and sandChance:
block[i, j, y] = SAND
else:
block[i, j, y] = GRASS
return block
binary = bin(num)[2:].zfill(numBit)
print(binary + " convert To: " + channelCoding(binary))
print("\n")
if __name__ == '__main__':
Nodes = generateNodes()
HuffmanCode = generateHuffman(Nodes)
decoder = HuffmanDecoder(HuffmanCode)
plainText = "alirezazarenejad"
cipherText = sourceCoding(HuffmanCode, plainText)
print("encode huffman code is: " + cipherText)
codeWord = channelCoding(cipherText)
print("channelCoding: " + codeWord)
decodedWord = channelDecoding(codeWord)
print("decoded word:" + decodedWord)
decodedData = destinationDecoding(decoder, decodedWord)
print("decodedData : ", decodedData)
print("\n\nwith noise:")
noisyCodeWord = noise(codeWord)
print("noisy codeword: " + noisyCodeWord)
decodedWordNoisy = channelDecoding(noisyCodeWord)
print("decoded word:" + decodedWordNoisy)
decodedDataNoisy = destinationDecoding(decoder, decodedWordNoisy)
print("decodedData : ", decodedDataNoisy)
def detect(filename, folder, file_no):
# print 'in'
img = cv2.imread(filename)
img = cv2.medianBlur(img, 1) #smothing image
# extracting white characters
image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imgray = crop(image)
# imgray = image
imgray = cv2.resize(imgray, (880, 100))
(h, w) = imgray.shape
(img_h, img_w) = (h, w)
drawing = noise(imgray, imgray.shape, 1000, 25000)
cv2.imshow('win1', drawing)
cv2.waitKey()
cv2.imwrite('E:\Results\\res.jpg', drawing)
# character recorgnition
image = cv.LoadImage("E:\Results\\res.jpg", cv.CV_LOAD_IMAGE_GRAYSCALE)
lang = ['eng']
result = 0
for i in lang:
data = pytesser.iplimage_to_string(image,
i,
pytesser.PSM_SINGLE_LINE,
makebox=True)
# print data
word = ''
section = [] #all the characters and coordinates in a list
line = []
for i in data:
if i == ' ' or i == '\n':
line.append(word)
word = ''
elif (i != '\n'):
word += i
if i == '\n':
section.append(line)
line = []
coord = []
chars = [] #coordinates of the characters
count = 0
for i in section:
for j in i:
# print count
if j.isdigit() and count < 5 and count > 0:
coord.append(int(j))
count += 1
count = 0
chars.append(coord)
coord = []
# removing all symbols
dele = 0
delete = []
for i in section:
if not i[0].isdigit() and not i[0].isalpha():
delete.append(dele)
dele += 1
delete.reverse()
for i in delete:
section.pop(i)
chars.pop(i)
c = 0
point = []
for i in chars:
cv2.rectangle(drawing, (i[0], i[1]), (i[2], i[3]), (255, 255, 255), 2)
cv2.imshow('rec', drawing)
cv2.waitKey(1)
cv2.imwrite('E:\\report\\res.jpg', drawing)
counter = 0
line_coordinates = [0]
if len(chars) == 1:
for i in chars:
if i[0] > 400:
line_coordinates.append(i[0] / 2)
line_coordinates.append(i[0])
# for debugging, draws box using coordinates received from make box function
for i in chars:
counter += 1
if c == 0:
point.append(i[2])
c = 1
elif c == 1:
point.append(i[0])
line = ((point[0] + point[1]) / 2)
line_coordinates.append(line)
cv2.line(drawing, (line, img_h), (line, 0), (255, 255, 255), 2)
c = 0
point = []
if counter == len(chars):
if img_w - i[2] < 150:
line_coordinates.append(img_w)
else:
line_coordinates.append(i[2])
line_coordinates.append(img_w)
cv2.line(drawing, (i[2], img_h), (i[2], 0), (255, 255, 255), 2)
# spliting image and processing
iterator = 1
id = ''
while iterator < len(line_coordinates):
roi = imgray[0:img_h,
line_coordinates[iterator - 1]:line_coordinates[iterator]]
iterator += 1
corrected = noise(roi, roi.shape, 300, 20000)
cv2.imwrite('E:\Results\\res.jpg', corrected)
cv2.imshow('win1', corrected)
cv2.waitKey(1)
image = cv.LoadImage("E:\Results\\res.jpg", cv.CV_LOAD_IMAGE_GRAYSCALE)
data = pytesser.iplimage_to_string(image, 'enm', 7)
# print data
for i in data:
if i == '!':
i = '1'
if i.isalpha() or i.isdigit():
if i == 'O' or i == 'o':
i = '0'
if i == 'L' or i == 'l':
i = '1'
if i == 'i' or i == 'I':
i = '1'
id = id + i
# print id
if id != '':
print id
sol = check(id)
final = sol[0]
alternative = sol[1]
possible = sol[2]
else:
return None
# print sol
if not final:
os.chdir(folder)
filename = 'error' + str(file_no)
img_name = filename + '.jpg'
txt_name = filename + '.txt'
cv2.imwrite(img_name, img)
if len(alternative) > 1: #storing alternatives in a text file
f = open(txt_name, 'w')
for i in alternative:
f.write(i + '\n')
f.close()
return None
else:
return possible
#!/usr/bin/python
import sys
import re
from Bio import AlignIO
from noise import *
for i in range(1, len(sys.argv)):
k, align, l, m = noise(sys.argv[i])
for record in align:
out = ''
for i in k:
out += str(record.seq[i])
sys.stdout.write(out + '\n')
if len(k) > 0:
sys.stderr.write(str(l) + ' columns were removed from this alignment\n')
elif len(m) == 0:
sys.stderr.write('Error: Empty file\n')
elif len(k) == 0:
sys.stderr.write('All columns were removed from this alignment\n')
from noise import *
import os
import cv2
if __name__ == '__main__':
path = 'C:/Users/Steffany/Documents/Javeriana/Semestre 9/Procesamiento de Imagenes/imagenes'
image_name = 'lena.png'
path_file = os.path.join(path, image_name) # se crea la ruta de la imagen
image = cv2.imread(path_file)
imagegris = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow('image', imagegris) # se muestra la imagen lena en grises
cv2.waitKey(0)
ns = noise("s&p",imagegris.astype(np.float)/255) # se llama la clase noise con la imagen lena en grises y el ruido s&n
ns = noise("gauss",imagegris.astype(np.float)/255) # se llama la clase noise con la imagen lena en grises y el ruido gauss
def detect(filename, folder, file_no):
# print 'in'
img = cv2.imread(filename)
img = cv2.medianBlur(img, 1) #smothing image
# extracting white characters
image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imgray = crop(image)
# imgray = image
imgray = cv2.resize(imgray, (880,100))
(h,w) = imgray.shape
(img_h,img_w) = (h,w)
drawing = noise(imgray, imgray.shape, 1000, 25000)
cv2.imshow('win1', drawing)
cv2.waitKey()
cv2.imwrite('E:\Results\\res.jpg', drawing)
# character recorgnition
image = cv.LoadImage("E:\Results\\res.jpg", cv.CV_LOAD_IMAGE_GRAYSCALE)
lang = ['eng']
result = 0
for i in lang:
data = pytesser.iplimage_to_string(image, i, pytesser.PSM_SINGLE_LINE,makebox=True)
# print data
word = ''
section = [] #all the characters and coordinates in a list
line = []
for i in data:
if i == ' ' or i=='\n':
line.append(word)
word = ''
elif (i!='\n'):
word+=i
if i == '\n':
section.append(line)
line = []
coord = []
chars = [] #coordinates of the characters
count = 0
for i in section:
for j in i:
# print count
if j.isdigit() and count < 5 and count > 0:
coord.append(int(j))
count += 1
count = 0
chars.append(coord)
coord = []
# removing all symbols
dele= 0
delete=[]
for i in section:
if not i[0].isdigit() and not i[0].isalpha():
delete.append(dele)
dele += 1
delete.reverse()
for i in delete:
section.pop(i)
chars.pop(i)
c = 0
point = []
for i in chars:
cv2.rectangle(drawing,(i[0],i[1]),(i[2],i[3]),(255,255,255),2)
cv2.imshow('rec', drawing)
cv2.waitKey(1)
cv2.imwrite('E:\\report\\res.jpg', drawing)
counter = 0
line_coordinates = [0]
if len(chars) == 1:
for i in chars:
if i[0] > 400:
line_coordinates.append(i[0]/2)
line_coordinates.append(i[0])
# for debugging, draws box using coordinates received from make box function
for i in chars:
counter += 1
if c == 0:
point.append(i[2])
c = 1
elif c == 1:
point.append(i[0])
line = ((point[0] + point[1]) / 2)
line_coordinates.append(line)
cv2.line(drawing,(line,img_h),(line,0),(255,255,255),2)
c = 0
point = []
if counter == len(chars):
if img_w - i[2] < 150:
line_coordinates.append(img_w)
else:
line_coordinates.append(i[2])
line_coordinates.append(img_w)
cv2.line(drawing,(i[2],img_h),(i[2],0),(255,255,255),2)
# spliting image and processing
iterator = 1
id = ''
while iterator < len(line_coordinates):
roi = imgray[0:img_h, line_coordinates[iterator-1]:line_coordinates[iterator]]
iterator += 1
corrected = noise(roi, roi.shape, 300, 20000)
cv2.imwrite('E:\Results\\res.jpg', corrected)
cv2.imshow('win1', corrected)
cv2.waitKey(1)
image = cv.LoadImage("E:\Results\\res.jpg", cv.CV_LOAD_IMAGE_GRAYSCALE)
data = pytesser.iplimage_to_string(image, 'enm', 7 )
# print data
for i in data:
if i == '!':
i = '1'
if i.isalpha() or i.isdigit():
if i == 'O' or i =='o':
i = '0'
if i == 'L' or i == 'l':
i = '1'
if i == 'i' or i == 'I':
i = '1'
id = id + i
# print id
if id != '':
print id
sol = check(id)
final = sol[0]
alternative = sol[1]
possible = sol[2]
else:
return None
# print sol
if not final:
os.chdir(folder)
filename = 'error' + str(file_no)
img_name = filename + '.jpg'
txt_name = filename + '.txt'
cv2.imwrite(img_name, img)
if len(alternative) > 1: #storing alternatives in a text file
f = open(txt_name, 'w')
for i in alternative:
f.write(i+'\n')
f.close()
return None
else:
return possible
#!/usr/bin/python
import sys
import re
from Bio import AlignIO
from noise import *
for i in range(1,len(sys.argv)):
k,align,l,m = noise(sys.argv[i])
for record in align:
out = ''
id = ''
for i in k:
out += str(record.seq[i])
sys.stdout.write('>' + record.id + '\n' + out +'\n')
if len(k) > 0: #show how many columns are removed from the alignment
sys.stderr.write(str(l)+' columns were removed from this alignment\n')
elif len(m) == 0: #error message when there is no record in the given alignment file
sys.stderr.write('Error: Empty file\n')
elif len(k) == 0: #return a message if all columns are removed
sys.stderr.write('All columns were removed from this alignment\n')
def main():
## Parse footprint descriptor
#fd = parseFD(fd_name)
fd = genArbitFD()
## Compute stack distance distribution
SD = sd(fd)
## Obj size dst
obj_dst = obj_size("data/akamai1.bin.sizeCntObj.json")
object_sizes = defaultdict(lambda : 0)
for i in range(no_objects):
sz= random.randint(1, 100)#obj_dst.sample()
object_sizes[sz] += 1
all_sizes = list(object_sizes.keys())
count = []
all_sizes.sort()
for a in all_sizes:
count.append(object_sizes[a])
sum_count = sum(count)
obj_sizes = all_sizes
size_dst = [float(c)/sum_count for c in count]
objects = range(len(obj_sizes))
sz_dict = defaultdict(lambda : 0)
o_sizes = copy.deepcopy(obj_sizes)
o_sizes.sort()
plt.plot(o_sizes)
plt.savefig("obj_sz_dst.png")
for i in range(len(obj_sizes)):
sz_dict[i] = obj_sizes[i]
nn = noise(SD, obj_sizes, size_dst)
nn.modelNoise()
trace, trace_prop, obj_hit_dst = generate_trace(SD, sz_dict)
trace_count = defaultdict(lambda : 0)
for t in trace_prop[4000]:
trace_count[t] += 1
trace_keys = list(trace_count.keys())
trace_keys.sort()
trace_vals = []
for t in trace_keys:
trace_vals.append(trace_count[t])
sum_vals = sum(trace_vals)
trace_vals = [float(t)/sum_vals for t in trace_vals]
plt.clf()
plt.plot(trace_keys, trace_vals)
plt.xlabel("stack_distance")
plt.ylabel("Count")
plt.savefig("stack_distance.png")
allvals = list(obj_hit_dst.keys())
allvals.sort()
plot_vals = []
for a in allvals:
plot_vals.append(obj_hit_dst[a])
sum_vals = sum(plot_vals)
plt_vals = [float(x)/sum_vals for x in plot_vals]
plt.clf()
plt.plot(allvals, plt_vals)
plt.savefig("ObjInWay_2.png")
def app2(uploaded_file):
st.header("Missing value and Noise detection")
# missing value
def noise(data):
l = len(data.columns)
col_list = list(data.columns)
noise = data.isnull().sum()
noisy_col = []
for i in range(l):
if(noise[i] != 0):
noisy_col.append(col_list[i])
return noisy_col
# correct those column
def correct(data, noisy_col):
imputer = SimpleImputer(strategy="median")
for i in range(len(noisy_col)):
imputer.fit(data[[noisy_col[i]]])
data[[noisy_col[i]]] = imputer.transform(data[[noisy_col[i]]])
return data
# uneccesary data columns
def unecessary_col(data, label):
l = len(data.columns)
col_list = list(data.columns)
index = l-1
for i in range(l):
if label == col_list[i]:
index = i
corr_value = []
unused_col = []
for i in range(l):
cor_val = data[col_list[index]].corr(data[col_list[i]])
corr_value.append(cor_val)
for i in range(l):
if(corr_value[i] < 0.05 and corr_value[i] > -0.05):
unused_col.append(col_list[i])
if(len(unused_col) == 0):
return "No unnecessary column", data
return unused_col, data
# download the correct dataset
def filedownload(df, filename):
csv = df.to_csv(index=None)
# strings <-> bytes conversions
filename = filename+".csv"
b64 = base64.b64encode(csv.encode()).decode()
href = f'<a href="data:file/csv;base64,{b64}" download={filename}>Download {filename} File</a>'
return href
# column data noise reduction
def noise_reduction(new_data, user_input):
Q1 = new_data[user_input].quantile(0.25)
Q3 = new_data[user_input].quantile(0.75)
IQR = Q3 - Q1
lower_limit = Q1 - 1.5*IQR
upper_limit = Q3 + 1.5*IQR
mid = (lower_limit + upper_limit)/2
for i in range(len(new_data)):
curr_value = new_data.iloc[i][user_input]
if(curr_value < lower_limit or curr_value > upper_limit):
new_data.at[i, user_input] = random.randint(
int(lower_limit), int(upper_limit))
return new_data
# noise present or not
def noise_check(new_data, user_input):
Q1 = new_data[user_input].quantile(0.25)
Q3 = new_data[user_input].quantile(0.75)
IQR = Q3 - Q1
lower_limit = Q1 - 1.5*IQR
upper_limit = Q3 + 1.5*IQR
check = False
stg = "Noise not present"
#mid = (lower_limit + upper_limit)/2
for i in range(len(new_data)):
curr_value = new_data.iloc[i][user_input]
if(curr_value < lower_limit or curr_value > upper_limit):
check = True
if(check):
stg = "Noise present"
break
return stg
def drop_col(data, string):
for i in range(len(string)):
data = data.drop([string[i]], axis=1)
return data
if uploaded_file is not None:
df = pd.read_csv(uploaded_file)
st.markdown('**1.1. Glimpse of dataset**')
st.write(df)
df = df.loc[:, ~df.columns.str.match("Unnamed")]
for label, content in df.items():
if pd.api.types.is_string_dtype(content):
df[label] = content.astype("category").cat.codes + 1
st.markdown("**New prepared dataset**")
st.write(df)
noise_col = noise(df)
if(len(noise_col) == 0):
st.success("No missing value present")
else:
st.warning("These column contain missing values :\n")
for i in range(len(noise_col)):
st.error(noise_col[i])
col_list = list(df.columns)
new_data = correct(df, noise_col)
st.markdown(filedownload(new_data, "Corrected_dataset"),
unsafe_allow_html=True)
# missing values case ends
label = st.selectbox("Enter Your label name", col_list)
if label != "":
string, new_data = unecessary_col(df, label)
if(string == "No unnecessary column"):
st.success("No unnecessary column")
else:
st.warning("Unecessary columns are : ")
for i in range(len(string)):
st.error(string[i])
# Drop unnecessary columns
if string != "No unnecessary column":
drop = st.checkbox("Drop unncessary column")
if drop:
new_data = drop_col(new_data, string)
st.write(new_data)
st.markdown(filedownload(new_data, "New_corrected_dataset"),
unsafe_allow_html=True)
agree = st.checkbox("Detect column noise")
col_list = list(df.columns)
if agree:
a = 0
st.markdown("Dataset anamolies")
user_input = st.selectbox("Select the column", col_list)
if user_input != "":
if user_input not in col_list:
st.write("Enter valid column")
else:
# user_input = int(user_input)
plt.figure(figsize=(9, 3))
sns.set_theme(style="whitegrid")
ax = sns.boxplot(x=new_data[user_input])
# ax.set(ylim=(0, 1))
# plt.xticks(rotation=90)
st.pyplot(plt)
stg = noise_check(new_data, user_input)
if(stg == "Noise not present"):
st.success(stg)
else:
st.error(stg)
a = 1
if a == 1:
# noise remove part
if st.button("Reduction of noise in that column"):
noise_free_data = noise_reduction(new_data, user_input)
st.write(new_data)
st.markdown(filedownload(
noise_free_data, "Noise_reduced_dataset"), unsafe_allow_html=True)
if st.button("Reduce noise from all the columns"):
for col in col_list:
noise_free_data = noise_reduction(
new_data, user_input)
new_data = noise_free_data
st.write(new_data)
st.markdown(filedownload(
noise_free_data, "Noise_reduced_dataset"), unsafe_allow_html=True)
st.markdown("Custom Removing Columns")
col_list = list(new_data.columns)
l=len(col_list)
i=0
c = st.slider('Select How many times you want to drop columns', 0, l, 0)
while(c>0):
c1=list(new_data.columns)
l1 = st.selectbox("Select the column to be dropped", col_list, key=c)
c=c-1
if(l1 not in c1):
st.write("Column already dropped")
else:
new_data=new_data.drop(l1, axis=1)
st.write(new_data)
st.markdown(filedownload(new_data, "New_corrected_dataset"),
unsafe_allow_html=True)
return new_data
if __name__ == "__main__":
#############################################
# 1. Read lena and create noisy lena Images #
#############################################
path = "C:/Users/ACER/Desktop/Semestre10/Imagenes/Presentaciones/Semana 6/Imagenes"
name = "lena.png"
path_name = os.path.join(path, name) #Join path and name
lena = cv2.imread(path_name) #Read Image
lena = cv2.cvtColor(lena, cv2.COLOR_BGR2GRAY) #Change lena to Gray CS
#cv2.imshow("lena", lena) #If you want to see the Image.
###########################################################################################
# 1.1 Generate lena_gauss_noisy & lena_s&p_noisy using noise() provided by Julian Quiroga #
###########################################################################################
lena_gauss_noisy = noise("gauss",
lena.astype(np.float) /
255) #Generate gaussian noisy lena
lena_gauss_noisy = (255 * lena_gauss_noisy).astype(np.uint8)
lena_sp_noisy = noise("s&p", lena.astype(np.float) / 255)
lena_sp_noisy = (255 * lena_sp_noisy).astype(
np.uint8) # Generate Salt and pepper noisy lena
#cv2.imshow("lena gauss noise", lena_gauss_noisy) #If you want to see the Image.
#cv2.imshow("lena s&p noise", lena_sp_noisy) #If you want to see the Image.
#################################
# 1.2 Filter Noisy lena´s with: #
#################################
###########################################
