def gen_member_card(data):
doc, tag, text = Doc().tagtext()
with tag('li'):
with tag('center'):
with tag('a', id="memid", href=data['knc_id']):
with tag('b', id="memid"):
text(data['name'])
doc._append("<br/>")
# with tag('a', id="memid", href=data['knc_id']):
# text(data['knc_id'])
# doc._append("<br/>")
text(data['role'])
if 'pronouns' in data:
text(' (%s)' % data['pronouns'])
doc._append("<br/>")
with tag('a', href='mailto:' + data['email']):
text(data['email'])
doc._append("<br/>")
links_list = ['github', 'website', 'CV']
if any((x in data) for x in links_list):
links_text_list = []
for x in links_list:
if x in data:
links_text_list.append('<a href="%s">%s<a>' % (data[x], x))
with tag('center'):
doc._append(" | ".join(links_text_list))
doc._append("<br/>")
if 'interests' in data:
with tag('center'):
with tag('p'):
with tag('u'):
text('Interests:')
text(' ')
text(data['interests'])
return indent(doc.getvalue(), indent_text=False)
def gen_member_card(data):
doc, tag, text = Doc().tagtext()
with tag('li'):
with tag('center'):
with tag('b', id="memid"):
text(data['name'])
# if 'pronouns' in data:
# text(' (%s)' % data['pronouns'])
# else:
# text(' (they/them)')
doc._append("<br/>")
with tag('a', id="memid", href=data['knc_id']):
text(data['knc_id'])
doc._append("<br/>")
text(data['role'])
doc._append("<br/>")
with tag('a', href=data['email']):
text(data['email'])
doc._append("<br/>")
links_list = ['github', 'website', 'CV']
if any((x in data) for x in links_list):
with tag('center'):
for x in links_list:
if x in data:
with tag('a', href=data[x]):
text(x)
doc._append("<br/>")
if 'interests' in data:
with tag('center'):
with tag('p'):
with tag('u'):
text('Interests:')
text(' ')
text(data['interests'])
return indent(doc.getvalue(), indent_text=False)
def generate_html_code(input1,input2,operations):
doc,tag,text,line = Doc().ttl()
with tag('html'):
with tag('head'):
doc._append('<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0-beta.2/css/bootstrap.min.css" integrity="sha384-PsH8R72JQ3SOdhVi3uxftmaW6Vc51MKb0q5P2rRUpPvrszuE4W1povHYgTpBfshb" crossorigin="anonymous">')
doc._append('<script src="https://use.fontawesome.com/35ac922f38.js"></script>')
with tag('body',klass = 'container bg-light'):
line('h2','file difference',klass = 'text-center')
with tag('div',klass = 'card'): ########3
line('h4', 'File A content = ' + input1)
doc._append('<br>')
line('h4', 'File B content = ' + input2)
line('h6', 'You Can Convert A To B with these operations', klass='text-center display-4')
with tag('div',klass = 'container'):
for i in operations:
if i[0:3] == 'ADD':
with tag('div',klass = "bg-success"):
line('i','',klass = 'fa fa-plus pull-right')
line('h4',i[3:])
elif i[0:3] == 'REM':
with tag('div',klass = "bg-danger"):
line('i','',klass = 'fa fa-remove pull-right')
line('h4',i[3:])
if i[0:3] == 'LCS':
with tag('div',klass = "bg-info"):
line('i','',klass = 'fa fa-star pull-right')
line('h4',i[3:])
return indent(doc.getvalue())
def generateMarkUp(operations, A, B):
doc, tag, text, line = Doc().ttl()
with tag('html'):
with tag('head'):
doc._append(
'<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0-beta.2/css/bootstrap.min.css" integrity="sha384-PsH8R72JQ3SOdhVi3uxftmaW6Vc51MKb0q5P2rRUpPvrszuE4W1povHYgTpBfshb" crossorigin="anonymous">'
)
doc._append(
'<script src="https://use.fontawesome.com/35ac922f38.js"></script>'
)
with tag('body', klass='container bg-light'):
line('h2', 'File Differ', klass='text-center display-2')
with tag('div', klass='card'):
with tag('div', klass='card-body'):
line('h4', 'File A content = ' + A)
doc._append('<br>')
line('h4', 'File B content = ' + B)
line('h6',
'You Can Convert A To B with these operations',
klass='text-center display-4')
with tag('div', klass='container'):
for op in operations:
if op[:3] == 'ADD':
with tag('div', klass="bg-success"):
line('i', '', klass="fa fa-plus mr-3 pull-right")
line('h3', op[3:], klass="text-white ml-5")
elif op[:3] == 'REM':
with tag('div', klass="bg-danger"):
line('i', '', klass="fa fa-remove mr-3 pull-right")
line('h3', op[3:], klass="text-white ml-5")
elif op[:3] == 'LCS':
with tag('div', klass="bg-primary"):
line('i', '', klass="fa fa-star mr-3 pull-right")
line('h3', op[3:], klass="text-white ml-5")
doc._append('<h6>Developed by github/Subhodeep and Shourya</h6>')
return indent(doc.getvalue())
def gen_proj_card(data):
user_info = load_user_info()
doc, tag, text = Doc().tagtext()
with tag('li'):
with tag('a', id="proj_id", href='projects/' + data['proj_id']):
text(data['projname'])
doc._append("<br/>")
with tag('b'):
text('Contributors: ')
u_idx = 0
for user in data['contributors']:
if user[0] == '@':
with tag('a', href=user):
text(user_info[user]['name'])
else:
text(user)
u_idx += 1
if u_idx < len(data['contributors']):
text(', ')
doc._append("<br/>")
text(data['short_desc'])
if 'thumbnail' in data:
doc._append('<img src="%s" style="width: 25vw;">' %
data['thumbnail'])
doc._append("<br/><br/><br/>")
return indent(doc.getvalue(), indent_text=False)
def allstates():
if (val[1] == "0"):
tkMessageBox.showinfo("Please Correct",
"Please choose your input image file first")
elif (val[1] == "1"):
parts = list(FaceParts.state())
print(parts, Alg) #Alg.get
doc, tag, text = Doc().tagtext()
doc.asis('<!DOCTYPE html>')
with tag('html'):
doc._append(
'<head><link rel="stylesheet" type="text/css" href="theme.css"></head> '
)
with tag('title'):
text('Report is generated')
with tag('body'):
with tag('div', id='title'):
text(
'An Implementation of Face Detection Library to Teach Algorithm Basics in OpenCV and Python \nSummer 2017'
)
with tag('div', id='logo'):
doc.stag('img', src='RET_Title.png', klass="logo")
##run if the dlib library is selected...
if (Alg == 1): #Alg.get()==1
imgdir = val[0]
with tag('h1'):
text('Your input image is the following')
with tag('div', id='photo-container'):
doc.stag('img', src='%s' % imgdir, klass="photo")
# construct the shape predictor algorithm.
print(
'DLIB Frontal face recognition algorithm is selected.'
)
with tag('h2'):
text(
'DLIB Frontal face recognition algorithm is selected.'
)
args = ("-p shape_predictor_68_face_landmarks.dat")
# initialize dlib's face detector (HOG-based) and then create
# the facial landmark predictor
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(
"shape_predictor_68_face_landmarks.dat")
# load the input image, resize it, and convert it to grayscale
image = cv2.imread(imgdir)
image = imutils.resize(image, width=500)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
doc._append("<br><hr>")
print('....\tProcessing %s\t....\n' % imgdir)
with tag('h4', id="center"):
text('....\tProcessing %s\t....' % imgdir)
#result='....\tProgram started processing %s\t....\n'%imgdir
# detect faces in the grayscale image
rects = detector(gray, 1)
if (rects):
print(
'Number of faces detected in the image: %d\n' %
len(rects))
#result=result+'Number of faces detected in the image: %d\n' %(len(rects))
with tag('h3'):
text(
'Number of faces detected in the image: %d'
% len(rects))
# clone the original image so we can draw on it, then
# draw rectangles on the image
cloneRect = image.copy()
# loop over the face detections
fno = 0
for (i, rect) in enumerate(rects):
# determine the facial landmarks for the face region, then
# convert the landmark (x, y)-coordinates to a NumPy array
shape = predictor(gray, rect)
shape = face_utils.shape_to_np(shape)
fno = fno + 1
print('STARTED processing FACE #%d' % fno)
doc._append("<br><hr>")
with tag('h2', id='faces'):
text('STARTED processing FACE #%d' % fno)
#result=result+'STARTED processing FACE #%d' %fno
# loop over the face parts individually
k = -1
for (
name, (i, j)
) in face_utils.FACIAL_LANDMARKS_IDXS.items():
k = k + 1
if (parts[k] == 1):
# clone the original image so we can draw on it, then
# display the name of the face part on the image
clone = image.copy()
cv2.putText(
clone,
str(fno) + "_" + name.upper() +
' Circled', (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7,
(0, 255, 0), 2)
# loop over the subset of facial landmarks, drawing the
# specific face part
for (x, y) in shape[i:j]:
cv2.circle(clone, (x, y), 1,
(0, 255, 0), 2)
# extract the ROI of the face region as a separate image
(x, y, w, h) = cv2.boundingRect(
np.array([shape[i:j]]))
roi = image[y:y + h, x:x + w]
roi = imutils.resize(
roi,
height=150,
inter=cv2.INTER_CUBIC)
# add text to resized ROI image
cv2.putText(
roi,
str(fno) + "_" + name.upper(),
(10, 30), cv2.FONT_HERSHEY_SIMPLEX,
0.7, (0, 255, 0), 1)
# show the particular face part
#cv2.imshow("cropped %s" %name, roi)
if (cv2.imwrite(
"allfaceparts/%d__cropped %s.jpg"
% (fno, name), roi)):
print(
'\t%d_%s \tDetected, circled and saved.'
%
(fno, name.upper().ljust(15)))
#result=result+'\t%d_%s \tDetected, circled and saved.\n' % (fno,name.upper().ljust(15))
with tag('div'):
text(
'%s Detected, circled and saved for Face #%d.'
% (name.upper().ljust(15),
fno))
with tag('div',
id='photo-container'):
doc.stag(
'img',
src=
'allfaceparts/%d__cropped %s.jpg'
% (fno, name),
klass="faceparts-big")
#cv2.imshow("Image %s" %name, clone)
if (cv2.imwrite(
"allfaceparts/%d_image %s.jpg"
% (fno, name), clone)):
print(
'\t%d_%s \tCropped and saved.'
%
(fno, name.upper().ljust(15)))
with tag('div'):
text(
'%s Cropped and saved for Face #%d.'
% (name.upper().ljust(15),
fno))
with tag('div',
id='photo-container'):
doc.stag(
'img',
src=
'allfaceparts/%d_image %s.jpg'
% (fno, name),
klass="faceparts")
#result=result+'\t%d_%s \tCropped and saved.\n' %(fno,name.upper().ljust(15))
#cv2.waitKey(0)
else:
print(
'Skipped analyzing %s on the face %d'
% (name, fno))
with tag('div'):
text(
'Skipped analyzing %s on the face %d'
% (name, fno))
# convert dlib's rectangle to a OpenCV-style bounding box
# [i.e., (x, y, w, h)],
(x, y, w, h) = face_utils.rect_to_bb(rect)
#crop the detected face and save it
crop_img = cloneRect[y:y + h, x:x + w]
#Resize the cropped image
crop_img = imutils.resize(
crop_img,
height=450,
inter=cv2.INTER_CUBIC)
# add text to resized ROI image
cv2.putText(crop_img, "FACE_#" + str(fno),
(10, 30), cv2.FONT_HERSHEY_SIMPLEX,
0.7, (0, 255, 0), 1)
if (cv2.imwrite(
'allfaceparts/Face_%d.jpg' % fno,
crop_img)):
print('Face #%d is cropped and saved.' %
fno)
with tag('div'):
text('Face #%d is cropped and saved.' %
fno)
with tag('div', id='photo-container'):
doc.stag(
'img',
src='allfaceparts/Face_%d.jpg' %
fno,
klass="fullface")
#result=result+'Face #%d is cropped and saved.' %fno
cv2.rectangle(cloneRect, (x, y),
(x + w, y + h), (0, 255, 0), 2)
# show the face number
cv2.putText(cloneRect, "Face #%d" % fno,
(x - 10, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 255, 0), 1)
# loop over the (x, y)-coordinates for the facial landmarks
# and draw them on the image
for (x, y) in shape:
cv2.circle(cloneRect, (x, y), 1,
(0, 0, 255), -1)
#cv2.imwrite("allfaceparts/%d_Complete face.jpg" %fno,cloneRect)
print('COMPLETED processing FACE #%d.\n' % fno)
with tag('h3', id='faces'):
text('COMPLETED processing FACE #%d' % fno)
#result=result+'COMPLETED processing FACE #%d.\n' %fno
#cv2.waitKey(0)
cv2.imwrite("allfaceparts/Faces_Complete.jpg",
cloneRect)
print(
'All detected faces are shown on complete image.'
)
#result=result+'All detected faces are shown on complete image.'
doc._append("<br><hr>")
with tag('h3'):
text(
'All detected faces are shown on complete image.'
)
with tag('div', id='photo-container'):
doc.stag('img',
src='allfaceparts/Faces_Complete.jpg',
klass="fullface")
doc._append("<br><hr>")
else:
print(
'We found No faces in the image to analyze all face parts.'
)
#result=result+'We found No faces in the image to analyze all face parts.'
doc._append("<br><hr>")
with tag('h4'):
text(
'We found No faces in the image to analyze all face parts.'
)
print(
'\n....\tProgram completed processing %s"\t....' %
imgdir)
with tag('div'):
text('....\tProgram completed processing image')
doc._append("<br><hr>")
#result=result+'\n....\tProgram completed processing "sample.jpg"\t....'
#FaceText=Label(root, text=result, font=("Helvetica",6))
#FaceText.pack(side=LEFT)
##end if
##run if the dlib library is selected...
elif (Alg.get() == 2):
'''
print ('Haarcascade algorithm is selected.')
with tag('h4'):
text('Haarcascade algorithm is selected.')
def draw_rects(img, rects, color):
for x1, y1, x2, y2 in rects:
cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
def detect(img, cascade):
rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30),flags=cv2.CASCADE_SCALE_IMAGE)
if len(rects) == 0:
return []
rects[:,2:] += rects[:,:2]
return rects
cascade_fn = args.get('--cascade', "haarcascades/haarcascade_frontalface_alt.xml")
nested_fn = args.get('--nested-cascade', "haarcascades/haarcascade_eye.xml")
cascade = cv2.CascadeClassifier(cascade_fn)
nested = cv2.CascadeClassifier(nested_fn)
'''
else:
tkMessageBox.showinfo(
"Please Correct",
"Please choose an algorithm to continue")
with tag('h4'):
text(
'Please choose a valid file to generate report'
)
result = doc.getvalue()
time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
report = open("Report_%s.html" % time, "w+")
report.write(result)
report.close()
def callback(event):
webbrowser.open_new(r"Report_%s.html" % time)
done = Label(root,
text="STEP 4: Click to open the Report_%s file " %
time,
font=("Helvetica", 12),
pady=10,
bg="pink",
fg="blue",
cursor="hand2")
done.pack(fill=X)
done.bind("<Button-1>", callback)
tkMessageBox.showinfo("Done", "Done your report file is saved.")
def _write_test_results_html_file(self, output_file, test_results):
"""
test_results = {
test00: {
'cluster_probability_plot': 'input.png',
'most_probable_cluster_count': 1,
'results': {
1: {
'probability': 0.12,
'file': 'xyz.html'
},
...
}
...
}
}
:param output_file:
:param test_results:
:return:
"""
doc, tag, text = Doc().tagtext()
with tag('html'):
with tag('head'):
with tag('style', type="text/css"):
text("""
button {
width: 150px;
height: 50px;
}
.wbutton {
width: 450px;
}
""")
with tag('script', src="https://ajax.googleapis.com/ajax/libs/jquery/3.2.1/jquery.min.js"):
pass
with tag('script'):
text('data={};'.format(json.dumps(test_results)))
doc._append("""
$(function(){
var selected_test = null;
// Select all test cases
var tests = Object.keys(data);
tests.sort();
function select_result(title, cluster_count) {
$('.current_view_title').text(title);
$('.current_view_content').attr('src', selected_test['results'][cluster_count]['file']);
}
function select_test(test) {
selected_test = data[test];
$('.test_button').css('background-color', '');
selected_test['button'].css('background-color', '#BBBBBB');
var target = $('.cluster_count_buttons')
target.empty();
target.append('<h2>Cluster probabilities</h2>');
target.append('<img src="' + selected_test['cluster_probability_plot'] + '" width="450px" />');
var cluster_counts = Object.keys(selected_test['results'])
cluster_counts.sort(function(a, b)
{
return a - b;
});
var mpcc = selected_test['most_probable_cluster_count'];
function add_btn(text, cluster_count) {
var btn = $('<button class="wbutton" type="button">' + text + '</button>');
btn.click(function(){select_result(text, cluster_count);});
target.append(btn);
return btn;
}
mpcc_btn = add_btn("Prediction (cluster count = " + mpcc + ")", mpcc);
cluster_counts.map(function(cluster_count) {
add_btn('Cluster count = ' + cluster_count + ", p = " + selected_test['results'][cluster_count]['probability'], cluster_count);
});
mpcc_btn.click();
}
// Create some buttons
tests.map(function(test){
var btn = $('<button class="test_button" type="button" id="' + test +'">' + test + '</button>');
data[test]['button'] = btn;
btn.click(function(){select_test(test);});
$('.test_buttons').append(btn);
});
select_test(tests[0]);
});
""")
with tag('body'):
with tag('h1'):
text('Test collection')
with tag('div', width="100%", style="background-color:#999999"):
with tag('table'):
with tag('tr', klass='test_buttons'):
pass
with tag('table', width="100%", height="80%"):
with tag('tr'):
with tag('td', style="vertical-align: top;", width="500px"):
with tag('div', klass='cluster_count_buttons'):
pass
with tag('td', style="vertical-align: top;"):
with tag('h2', klass="current_view_title"):
pass
with tag('iframe', klass="current_view_content", frameborder="0px", width="100%", height="100%"):
pass
html = doc.getvalue()
# Save the html file
with open(output_file, "w") as fh:
fh.write(html)
fh.flush() # It should be useless, but sometimes we had problems...
def gen_proj_page(data):
user_info = load_user_info()
PERSONAL_TEMPLATE = '''
<!DOCTYPE html>
<html>
<meta charset="UTF-8">
<head>
<title>KNC -- {projname}</title>
<link rel="stylesheet" href="../../style.css">
</head>
<body style="padding-left: 20%;padding-right: 20%">
<a href="../../index.html">KNC Home</a>
{everything_else}
</body>
</html>
'''
doc, tag, text = Doc().tagtext()
with tag('center'):
with tag('h1'):
text(data['projname'])
doc._append("<br/>")
with tag('center'):
with tag('ul',
id="nav",
style="list-style-type: none; margin: 0; padding: 0"):
for link in data['links']:
with tag('a', href=data['links'][link]):
with tag('li'):
text(link)
doc._append("<br/>")
with tag('center'):
with tag('h2'):
with tag('b'):
text('Contributors: ')
u_idx = 0
for user in data['contributors']:
if user[0] == '@':
with tag('a', href='../../' + user):
text(user_info[user]['name'])
else:
text(user)
u_idx += 1
if u_idx < len(data['contributors']):
text(', ')
doc._append("<br/>")
text(data['long_desc'])
doc._append("<br/>\n\n")
if 'images' in data:
for img in data['images']:
doc._append('<img src="%s" style="width: 25vw;">\n' % img)
doc._append("<br/>\n\n")
return PERSONAL_TEMPLATE.format(
projname=data['projname'],
everything_else=doc.getvalue(),
)
def gen_personal_html(data):
PERSONAL_TEMPLATE = '''
<!DOCTYPE html>
<html>
<meta charset="UTF-8">
<head>
<title>KNC -- {knc_id}</title>
<link rel="stylesheet" href="../style.css">
{header_stuff}
</head>
<body style="padding-left: 20%;padding-right: 20%">
<a href="../index.html">KNC Home</a>
{everything_else}
</body>
</html>
'''
doc, tag, text = Doc().tagtext()
# if they want a webpage redirect
if ('redirect_page_to_website' in data and 'website' in data
and data['redirect_page_to_website']):
# do the redirect
redirect_header = (
'<meta http-equiv="refresh" content="0; URL=%s" />' %
data['website'])
# print a link also
with tag('p'):
text('redirecting to:')
doc._append("<br/>")
with tag('a', href=data['website']):
text(data['website'])
return PERSONAL_TEMPLATE.format(
knc_id=data['knc_id'],
everything_else=doc.getvalue(),
header_stuff=redirect_header,
)
with tag('center'):
with tag('h1'):
text(data['name'])
with tag('h2'):
with tag('a', href='mailto:' + data['email']):
text(data['email'])
with tag('h2'):
text(data['role'])
if 'bio' in data:
with tag('p'):
text(data['bio'])
links_list = ['github', 'website', 'CV']
if any((x in data) for x in links_list):
with tag('h2'):
text('Links:')
with tag('ul'):
for x in links_list:
if x in data:
with tag('li'):
with tag('a', href=data[x]):
text(x)
if 'interests' in data:
with tag('h2'):
text('Interests:')
with tag('p'):
text(data['interests'])
if 'proj_blurb' in data:
with tag('h2'):
text('Projects:')
with tag('p'):
text(data['proj_blurb'])
return PERSONAL_TEMPLATE.format(
knc_id=data['knc_id'],
everything_else=doc.getvalue(),
header_stuff='',
)
# coding: UTF-8
from yattag import Doc
doc, tag, text, line = Doc(defaults={
'title': 'Untitled',
'contact_message': 'You just won the lottery!'
},
errors={
'contact_message':
'Your message looks like spam.'
}).ttl()
line('h1', 'Contact form')
with tag('form', action=""):
doc.input(name='title', type='text')
doc._append('\n')
with doc.textarea(name='contact_message'):
pass
doc.stag('input', type='submit', value='Send my message')
doc._append('\n')
print(doc.getvalue())
s = doc.getvalue()
f = open('text.txt', 'w')
f.write(s)
f.close()
