def virtual_tryon(user_id,selected_id):
f = open("./Database/val_pairs.txt" , "w")
f.write(user_id+".jpg "+selected_id+"_1.jpg")
f.close()
predict()
im = Image.open("./output/second/TOM/val/" + selected_id + "_1.jpg")
width, height = im.size
# Setting the points for cropped image
left = width / 3
top = 2 * height / 3
right = 2 * width / 3
bottom = height
# Cropped image of above dimension
# (It will not change orginal image)
im1 = im.crop((left, top, right, bottom))
newsize = (200, 300)
im1 = im1.resize(newsize)
result_url="/output/second/TOM/val/" + selected_id + user_id + ".jpg"
im1.save("."+result_url)
return result_url
def get(self):
lines = request.get_json(force=True)
ligid = lines[int1]
seqid = lines[int2]
# TODO parse out 2 ints from json
s = f"prediction at ({ligid},{seqid}): {predict(ligid, seqid)}"
#s = str(predict(5,5))
x = predict(ligid, seqid)
print(s)
return {'RESULT': str(x),
'report': s,
'show_inp': str(ligid) + str(seqid)}
def index(request):
# Get common and all the symptoms from data set
raw_data_common, raw_data_all = getData()
# Split symptoms name by underscrore(_)
common_symptom = []
all_symptom = []
for i in raw_data_common:
tmp = i.split('_', 1)[-1]
common_symptom.append(tmp)
for i in raw_data_all:
tmp = i.split('_', 1)[-1]
all_symptom.append(tmp)
data = list(zip(raw_data_common, common_symptom))
data1 = list(zip(raw_data_all, all_symptom))
if request.method == 'POST' and 'train_data' in request.POST:
# Run training function when Train button is clicked
sys.path.append('..')
from script import training
training()
if request.method == 'POST' and 'predict_disease' in request.POST:
# Get symptoms list and prepare them for input
checked_list = request.POST.getlist('checks[]')
symptoms_set = request.POST.get("symptoms_field")
symptoms_list = symptoms_set.split(",")
merged_list = symptoms_list + checked_list
merged_list = list(set(merged_list))
print("\nInput are : \n", merged_list)
# When no symptoms is selected
if all('' == s or s.isspace() for s in merged_list):
print("\nInput is empty:\n")
return render(request, "home.html", {"data": data, "data1": data1})
# Feed input list to predict function to generate prediction output
sys.path.append('..')
from script import predict
values = predict(merged_list)
print("\nOutput are:\n", values, "\n")
return render(request, "home.html", {
"data": data,
"data1": data1,
"predicted_diseases": values
})
return render(request, "home.html", {"data": data, "data1": data1})
def final_img():
global name, selection
person = Image.open('temp.jpg')
person.save("./Database/val/person/" + name + ".jpg")
pose_parse(name)
execute()
f = open("./Database/val_pairs.txt", "w")
f.write(name + ".jpg " + selection + "_1.jpg")
f.close()
predict()
im = Image.open("./output/second/TOM/val/" + selection + "_1.jpg")
width, height = im.size
left = width / 3
top = 2 * height / 3
right = width
bottom = height
im1 = im.crop((left, top, right, bottom))
newsize = (600, 450)
im1 = im1.resize(newsize)
im1.save("./output/second/TOM/val/" + selection + "_1.jpg")
result = Image.open("./output/second/TOM/val/" + selection + "_1.jpg")
result.save("data.jpg")
return Response(gen_stored("data.jpg"),
mimetype='multipart/x-mixed-replace; boundary=frame')
progress_bar = st.progress(0)
st.write("Generating Mask and Pose Pairs")
pose_parse(user_input)
execute()
for percent_complete in range(100):
time.sleep(0.05)
progress_bar.progress(percent_complete + 1)
st.write(
"Please click the Click Button after Pose pairs and Masks are generated"
)
if st.button('Execute'):
f = open("./Database/val_pairs.txt", "w")
f.write(user_input + ".jpg " + selected + "_1.jpg")
f.close()
predict()
from PIL import Image
im = Image.open("./output/second/TOM/val/" + selected + "_1.jpg")
width, height = im.size
# # Setting the points for cropped image
left = width / 3
top = 2 * height / 3
right = 2 * width / 3
bottom = height
# # Cropped image of above dimension
# # (It will not change orginal image)
im1 = im.crop((left, top, right, bottom))
newsize = (100, 150)
im1 = im1.resize(newsize)
def upload_image4():
cache.init_app(app2)
with app2.app_context():
cache.clear()
party_cloth = ['000003' , '000108', '001719' ,'017376' ,'000178', '000182' , '001428' , '004710' , '008632' , '017374','002061', '017642', '017377', '017963', '018348', '019067','019564', '017643']
for i in range(len(party_cloth)):
party_cloth[i] = (str(party_cloth[i])+"_1.jpg")
if request.method=="POST":
# if 'file' not in request.files:
# flash('No file part')
# return redirect(request.url)
text = request.form['input_text']
file = request.files['file']
if file.filename == '':
flash('No image selected for uploading')
return redirect(request.url)
if file and allowed_file(file.filename) and text:
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER1'], filename))
i_path = 'static/Database/val/person/'
input_img = text+'.jpg'
os.rename(i_path+filename , i_path+input_img)
filename = text+'.jpg'
o_path = './output/second/TOM/val'
# fish = glob.glob('./output/second/TOM/val/*')
fish = glob.glob('./static/output_f/*')
for f in fish:
os.remove(f)
time.sleep(10) #
#
#
#
pose_parse(text)
valpair_file = 'static/Database/val_pairs.txt'
for i in range(len(party_cloth)):
with open(valpair_file , "w") as f:
f.write(text+'.jpg '+ party_cloth[i] )
f.close()
predict()
im = Image.open(os.path.join(o_path,party_cloth[i]))
width, height = im.size
left = width / 3
top = 2 * height / 3
right = 2 * width / 3
bottom = height
im = im.crop((left, top, right, bottom))
newsize = (200, 270)
im = im.resize(newsize)
im.save(os.path.join(app2.config['OUTPUT_FOLDER3'],party_cloth[i]))
#
#
#
return render_template('party_wear.html', party=party_cloth, text= text)
else:
flash('Allowed image types are -> png, jpg, jpeg, gif')
return redirect(request.url)
return render_template('party_wear.html')
parser.add_argument('--top_k', action="store", default=5, dest="top_k", type=int)
parser.add_argument('--gpu', action='store_true', default=False, dest='gpu', help='Set a switch to use GPU')
results = parser.parse_args()
img_path = results.img_path
checkpoint_path = results.ck_path
category_names = results.category_names
top_k = results.top_k
gpu = results.gpu
if gpu==True:
using_gpu = torch.cuda.is_available()
device = 'gpu'
print('GPU On');
else:
print('GPU Off');
device = 'cpu'
model = script.loading_checkpoint(checkpoint_path)
processed_image = script.process_image(img_path)
probs, classes = script.predict(processed_image, model, top_k, device)
# Label mapping
cat_to_name = script.labeling(category_names)
labels = []
for class_index in classes:
labels.append(cat_to_name[str(class_index)])
# Converting from tensor to numpy-array
print('Name of class: ', labels[0])
print('Probability: ', probs)
def upload_image5():
cache.init_app(app2)
with app2.app_context():
cache.clear()
fish = glob.glob('./static/Database/val/person/*')
for f in fish:
os.remove(f)
fish = glob.glob('./static/size/*')
for f in fish:
os.remove(f)
tshirt = [
'POLO_Black', 'POLO-blue', 'POLO-Blue', 'POLO-BLue', 'POLO-orach',
'POLO-Violet'
]
for i in range(len(tshirt)):
tshirt[i] = (str(tshirt[i]) + ".jpg")
if request.method == "POST":
text = "tshirtss"
file = request.files['file']
file1 = request.files['file1']
height = request.form['height']
unit = request.form.get('unit')
Go = "False"
if file.filename == '':
flash('No image uploaded for size prediction.')
return redirect(request.url)
if file1.filename == '':
flash('No image uploaded for trying clothes.')
return redirect(request.url)
if file and allowed_file(file.filename) and file1 and allowed_file(
file1.filename) and height and unit and text:
filename = secure_filename(file.filename)
filename1 = secure_filename(file1.filename)
file1.save(os.path.join(app.config['UPLOAD_FOLDER2'], filename1))
i_path = 'static/size/'
file_path = os.path.join(i_path, filename1)
size = women_size_predict(file_path, height, unit)
#
file.save(os.path.join(app.config['UPLOAD_FOLDER1'], filename))
i_path = 'static/Database/val/person/'
input_img = text + '.jpg'
os.rename(i_path + filename, i_path + input_img)
filename = text + '.jpg'
o_path = './output/second/TOM/val'
# time.sleep(10) #
#
#
#
pose_parse(text)
valpair_file = 'static/Database/val_pairs.txt'
for i in range(len(tshirt)):
with open(valpair_file, "w") as f:
f.write(text + '.jpg ' + tshirt[i])
f.close()
predict()
im = Image.open(os.path.join(o_path, tshirt[i]))
width, height = im.size
left = width / 3
top = 2 * height / 3
right = 2 * width / 3
bottom = height
im = im.crop((left, top, right, bottom))
newsize = (200, 270)
im = im.resize(newsize)
im.save(
os.path.join(app2.config['OUTPUT_FOLDER3'], tshirt[i]))
#
#
#
##
##
return render_template('tshirt.html',
tshirt=tshirt,
text=text,
size=size)
else:
flash('Allowed image types are -> png, jpg, jpeg, gif')
return redirect(request.url)
return render_template('tshirt.html')
def upload_image4():
cache.init_app(app2)
with app2.app_context():
cache.clear()
fish = glob.glob('./static/Database/val/person/*')
for f in fish:
os.remove(f)
fish = glob.glob('./static/size/*')
for f in fish:
os.remove(f)
party = [
'Orach-Flayrd', 'MultiColor', 'Butterfly_p', 'Waved-Black', 'Shimm',
'Wavy-Red', 'Wavy-Pink', 'Reddish-KNOTS'
]
for i in range(len(party)):
party[i] = (str(party[i]) + ".jpg")
if request.method == "POST":
text = "palty"
file = request.files['file']
height = request.form['height']
unit = request.form.get('unit')
Go = "False"
if file.filename == '':
flash('No image uploaded for trying clothes.')
return redirect(request.url)
if file and allowed_file(file.filename) and height and unit and text:
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER2'], filename))
i_path = 'static/size/'
file_path = os.path.join(i_path, filename)
size, file = women_size_predict1(file_path, height, unit)
file = Image.fromarray(file, 'RGB')
#
file.save(os.path.join(app.config['UPLOAD_FOLDER1'], filename))
i_path = 'static/Database/val/person/'
input_img = text + '.jpg'
os.rename(i_path + filename, i_path + input_img)
filename = text + '.jpg'
o_path = './output/second/TOM/val'
# fish = glob.glob('./output/second/TOM/val/*')
fish = glob.glob('./static/outputs/output_f/*')
for f in fish:
os.remove(f)
time.sleep(10) #
#
#
#
pose_parse(text)
valpair_file = 'static/Database/val_pairs.txt'
for i in range(len(party)):
with open(valpair_file, "w") as f:
f.write(text + '.jpg ' + party[i])
f.close()
predict()
im = Image.open(os.path.join(o_path, party[i]))
width, height = im.size
left = width / 3
top = 2 * height / 3
right = 2 * width / 3
bottom = height
im = im.crop((left, top, right, bottom))
newsize = (200, 270)
im = im.resize(newsize)
im.save(
os.path.join(app2.config['OUTPUT_FOLDER3'], party[i]))
#
#
#
##
##
return render_template('party_wear.html',
party=party,
text=text,
size=size)
else:
flash('Allowed image types are -> png, jpg, jpeg, gif')
return redirect(request.url)
return render_template('party_wear.html')
def upload_image3():
cache.init_app(app2)
with app2.app_context():
cache.clear()
fish = glob.glob('./static/Database/val/person/*')
for f in fish:
os.remove(f)
fish = glob.glob('./static/size/*')
for f in fish:
os.remove(f)
brand_cloth = [
'Pink-Flayrd', 'Blue-flayrd', 'Flayerd-Yellow', 'Bluish-Flayrd',
'NIKE-black', 'RED-Flayrd'
] #'
for i in range(len(brand_cloth)):
brand_cloth[i] = (str(brand_cloth[i]) + ".jpg")
if request.method == "POST":
text = "brandss"
file = request.files['file']
height = request.form['height']
unit = request.form.get('unit')
if file.filename == '':
flash('No image selected for uploading')
return redirect(request.url)
if file and allowed_file(file.filename) and height and unit:
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER2'], filename))
i_path = 'static/size/'
file_path = os.path.join(i_path, filename)
size, file = women_size_predict1(file_path, height, unit)
file = Image.fromarray(file, 'RGB')
#
file.save(os.path.join(app.config['UPLOAD_FOLDER1'], filename))
i_path = 'static/Database/val/person/'
input_img = text + '.jpg'
os.rename(i_path + filename, i_path + input_img)
filename = text + '.jpg'
o_path = './output/second/TOM/val'
# fish = glob.glob('./output/second/TOM/val/*')
fish = glob.glob('./static/outputs/output_f/*')
for f in fish:
os.remove(f)
time.sleep(10)
#
#
#
#
pose_parse(text)
valpair_file = 'static/Database/val_pairs.txt'
for i in range(len(brand_cloth)):
with open(valpair_file, "w") as f:
f.write(text + '.jpg ' + brand_cloth[i])
f.close()
predict()
im = Image.open(os.path.join(o_path, brand_cloth[i]))
width, height = im.size
left = width / 3
top = 2 * height / 3
right = 2 * width / 3
bottom = height
im = im.crop((left, top, right, bottom))
newsize = (200, 270)
im = im.resize(newsize)
im.save(
os.path.join(app2.config['OUTPUT_FOLDER3'],
brand_cloth[i]))
#
#
#
return render_template('brands.html',
brands=brand_cloth,
text=text,
size=size)
else:
flash('Allowed image types are -> png, jpg, jpeg, gif')
return redirect(request.url)
return render_template('brands.html')
def upload_image2():
cache.init_app(app2)
with app2.app_context():
cache.clear()
fish = glob.glob('./static/Database/val/person/*')
for f in fish:
os.remove(f)
fish = glob.glob('./static/size/*')
for f in fish:
os.remove(f)
sport_cloth = [
'ADIDAS-BW', 'ADIDAS_Black', 'FILA_grey', 'FILA-Pink', 'IVY-Black',
'NIKE-grey'
]
for i in range(len(sport_cloth)):
sport_cloth[i] = (str(sport_cloth[i]) + ".jpg")
if request.method == "POST":
if 'file' not in request.files:
flash('No file part')
return redirect(request.url)
text = "sportss"
file = request.files['file']
height = request.form['height']
unit = request.form.get('unit')
Go = "False"
if file.filename == '':
flash('No image uploaded for size prediction.')
return redirect(request.url)
if file and allowed_file(file.filename) and height and unit:
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER2'], filename))
i_path = 'static/size/'
file_path = os.path.join(i_path, filename)
size, file = women_size_predict1(file_path, height, unit)
file = Image.fromarray(file, 'RGB')
#
file.save(os.path.join(app.config['UPLOAD_FOLDER1'], filename))
i_path = 'static/Database/val/person/'
input_img = text + '.jpg'
os.rename(i_path + filename, i_path + input_img)
filename = text + '.jpg'
o_path = './output/second/TOM/val'
# fish = glob.glob('./output/second/TOM/val/*')
fish = glob.glob('./static/outputs/output_f/*')
for f in fish:
os.remove(f)
time.sleep(10)
#
#
#
#
pose_parse(text)
valpair_file = 'static/Database/val_pairs.txt'
for i in range(len(sport_cloth)):
with open(valpair_file, "w") as f:
f.write(text + '.jpg ' + sport_cloth[i])
f.close()
predict()
im = Image.open(os.path.join(o_path, sport_cloth[i]))
width, height = im.size
left = width / 3
top = 2 * height / 3
right = 2 * width / 3
bottom = height
im = im.crop((left, top, right, bottom))
newsize = (200, 270)
im = im.resize(newsize)
im.save(
os.path.join(app2.config['OUTPUT_FOLDER3'],
sport_cloth[i]))
#
#
#
return render_template('sports.html',
sports=sport_cloth,
text=text,
size=size)
else:
flash('Allowed image types are -> png, jpg, jpeg, gif')
return redirect(request.url)
return render_template('sports.html')
def upload_image():
cache.init_app(app2)
with app2.app_context():
cache.clear()
fish = glob.glob('./static/Database/val/person/*')
for f in fish:
os.remove(f)
fish = glob.glob('./static/size/*')
for f in fish:
os.remove(f)
cloth = [
'Casual-Yellow', 'Casual-Pink', 'Casual-Violet', 'Casual-White',
'Dark_ORANGE', 'Flowers-White', 'Grey_North', 'Light-Pink',
'Multicolo-White', 'Sky_Blue', 'TheNORTHface', 'Yellow62'
]
for i in range(len(cloth)):
cloth[i] = (str(cloth[i]) + ".jpg")
if request.method == "POST":
if 'file' not in request.files:
flash('No file part')
return redirect(request.url)
text = "casualss"
file = request.files['file']
height = request.form['height']
unit = request.form.get('unit')
if file.filename == '':
flash('No image selected for uploading')
return redirect(request.url)
if file and allowed_file(file.filename) and text and height and unit:
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER2'], filename))
i_path = 'static/size/'
file_path = os.path.join(i_path, filename)
size, file = women_size_predict1(file_path, height, unit)
file = Image.fromarray(file, 'RGB')
#
file.save(os.path.join(app.config['UPLOAD_FOLDER1'], filename))
i_path = 'static/Database/val/person/'
input_img = text + '.jpg'
os.rename(i_path + filename, i_path + input_img)
filename = text + '.jpg'
o_path = './output/second/TOM/val'
# fish = glob.glob('./output/second/TOM/val/*')
fish = glob.glob('./static/outputs/output_f/*')
for f in fish:
os.remove(f)
time.sleep(10)
#
#
#
#
pose_parse(text)
valpair_file = 'static/Database/val_pairs.txt'
for i in range(len(cloth)):
with open(valpair_file, "w") as f:
f.write(text + '.jpg ' + cloth[i])
f.close()
predict()
im = Image.open(os.path.join(o_path, cloth[i]))
width, height = im.size
left = width / 3
top = 2 * height / 3
right = 2 * width / 3
bottom = height
im = im.crop((left, top, right, bottom))
newsize = (200, 270)
im = im.resize(newsize)
im.save(
os.path.join(app2.config['OUTPUT_FOLDER3'], cloth[i]))
#
#
#
return render_template('casuals.html',
casuals=cloth,
text=text,
size=size)
else:
flash('Allowed image types are -> png, jpg, jpeg, gif')
return redirect(request.url)
return render_template('casuals.html')