ptvsd.enable_attach(address=('localhost', 6790))
# ptvsd.wait_for_attach() # Only include this line if you always want to manually attach the debugger
from LayoutAndStyleUtils import (Grid, Cell, BlockContainerStyler)
BlockContainerStyler().set_default_block_container_style()
# --------------------------------------------------------------------------------
messageboard = st.empty()
from utils import SessionState
# Session State variables:
session_state = SessionState.get(
message='To use this application, please login...',
token={
'value': None,
'expiry': None
},
user=None,
email=None,
report=[],
)
# --------------------------------------------------------------------------------
# import must come after messageboard as these apps use app.messageboard
import dumb_app, dumber_app, login_app, logout_app
def main():
pages = {
'DuMMMy aPp [1]': [dumb_app.main], # DUMMY APP 1
'DUmmmY ApP [2]': [dumber_app.main], # DUMMY APP 2
# This is a sample Python script.
import streamlit as st
import time
import cv2 as cv
import numpy as np
from PIL import Image
from utils import SessionState # Assuming SessionState.py lives on this folder
from utils.cv_filters import strel_line, imadjust, gaussian_kernel, wiener_filter, laplacianOfGaussian
session = SessionState.get(run_id=0)
def main():
image_s = None
image_h = None
st.title("Dermoscopy Images Preprocessing")
process = st.sidebar.radio('Type of process', ('Registration', 'Shaver'))
with st.beta_container():
if process == 'Registration':
st.title("Registration")
st.sidebar.write('You selected registration')
# Add a slider to the sidebar:
austerity = st.sidebar.slider('Austerity', 1.0, 100.0, (70.0))
minimum = st.sidebar.slider('Minimum matches', 0.0, 1000.0, (10.0))
MIN_MATCH_COUNT = minimum
sample = st.file_uploader("Choose an sample image...")
if sample is not None:
image_s = Image.open(sample)
st.image(image_s, caption='Sample Image', width=300)
history = st.file_uploader("Choose a history image...")
if history is not None:
from math import sqrt
from utils import SessionState
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
import altair as alt
import streamlit as st
np.random.seed(0)
X_MIN = 0
X_MAX = 1
state = SessionState.get(min_rmse=999)
@dataclass
class Weight:
w0: float
w1: float
w2: float
@st.cache
def build_dataset(xres):
X_source = np.linspace(X_MIN, X_MAX, xres)
y_source = (
np.polynomial.polynomial.polyval(X_source, [0, 2, 5])
+ np.sin(8 * X_source)
padding-bottom: {padding_bottom}rem;
}}
.reportview-container .main {{
color: {COLOR};
background-color: {BACKGROUND_COLOR};
}}
</style>
""",
unsafe_allow_html=True,
)
sessions = session.get(key=0,
id=0,
trainer_params={},
trainer_dict={
"id": [],
"dataloader": [],
"model": [],
"loss": [],
"optimizer": [],
"scheduler": [],
"metrics": []
})
st.sidebar.title("目录")
cur = st.sidebar.radio("catalogue", ("Trainer", "Eval"))
if cur == "Trainer":
data_selections = []
for name, obj in inspect.getmembers(data_module, inspect.isclass):
if "data_loader.data_loaders" in str(obj):
data_selections.append(str(obj)[33:-12])
model_selections = []
for name, obj in inspect.getmembers(model_module, inspect.isclass):
def main():
st.set_page_config(page_title = "Traffic Flow Counter",
page_icon=":vertical_traffic_light:")
obj_detector = load_obj_detector(config, wt_file)
tracker = tc.CarsInFrameTracker(num_previous_frames = 10, frame_shape = (720, 1080))
state = SessionState.get(upload_key = None, enabled = True,
start = False, conf = 70, nms = 50, run = False)
hide_streamlit_widgets()
"""
# Traffic Flow Counter :blue_car: :red_car:
Upload a video file to track and count vehicles. Don't forget to change parameters to tune the model!
#### Features to be added in the future:
+ speed measurement
+ traffic density
+ vehicle type distribution
"""
with st.sidebar:
"""
## :floppy_disk: Parameters
"""
state.conf, state.nms = parameter_sliders(
keys, state.enabled, value = [state.conf, state.nms])
st.text("")
st.text("")
st.text("")
"""
#### :desktop_computer: [Source code in Github](https://github.com/aldencabajar/traffic_flow_counter)
"""
#set model confidence and nms threshold
if (state.conf is not None):
obj_detector.confidence = state.conf/ 100
if (state.nms is not None):
obj_detector.nms_threshold = state.nms/ 100
upload = st.empty()
start_button = st.empty()
stop_button = st.empty()
with upload:
f = st.file_uploader('Upload Video file (mpeg/mp4 format)', key = state.upload_key)
if f is not None:
tfile = tempfile.NamedTemporaryFile(delete = True)
tfile.write(f.read())
upload.empty()
vf = cv2.VideoCapture(tfile.name)
if not state.run:
start = start_button.button("start")
state.start = start
if state.start:
start_button.empty()
#state.upload_key = str(randint(1000, int(1e6)))
state.enabled = False
if state.run:
tfile.close()
f.close()
state.upload_key = str(randint(1000, int(1e6)))
state.enabled = True
state.run = False
ProcessFrames(vf, tracker, obj_detector, stop_button)
else:
state.run = True
trigger_rerun()
def main():
#st.set_page_config(page_title = "Continuous Sign Language Recognition")
st.markdown("### Model Architecture")
st.image(
f'/app/architecture.png',
caption='Architecture overview',
use_column_width=True
)
base_size = [256, 256]
crop_size = [224, 224]
random_crop = False
p_drop = 0.5
random_drop = False
transform_phoenix = transforms.Compose(
[
transforms.Resize(base_size),
transforms.RandomCrop(crop_size)
if random_crop
else transforms.CenterCrop(crop_size),
transforms.ToTensor(),
#transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
transforms.Normalize([0.53724027, 0.5272855, 0.51954997], [1, 1, 1])
]
)
transform_krsl = transforms.Compose(
[
transforms.Resize(base_size),
transforms.RandomCrop(crop_size)
if random_crop
else transforms.CenterCrop(crop_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
#transforms.Normalize([0.53724027, 0.5272855, 0.51954997], [1, 1, 1])
]
)
state = SessionState.get(upload_key = None, enabled = True,
start = False, conf = 70, nms = 50, run = False, upload_db = False)
hide_streamlit_widgets()
"""
# Continuous Sign Language Recogntion
"""
with open("/app/phrases.txt", "r") as f:
lines = f.readlines()
my_phrases = [""] + [line.strip().split("\t")[1] for line in lines]
with open("/app/app/test_ids.txt", "r") as f:
ids = f.readlines()
signer_ids = [""] + [id.strip() for id in ids]
phrase_dict = {line.strip().split("\t")[1]:line.strip().split("\t")[0] for line in lines}
with st.sidebar:
"""
## :floppy_disk: Stochastic CSLR model
SOTA among single cue models
"""
#state.conf, state.nms = parameter_sliders(
# keys, state.enabled, value = [state.conf, state.nms])
st.text("")
st.text("")
st.text("")
lang = st.radio("Select language: ", ('Russian', 'German'))
backbone = st.sidebar.selectbox(
label = 'Please choose the backbone for Stochastic CSLR',
options = [
'ResNet18'
],
index = 0,
key = 'backbone'
)
phrase = st.sidebar.selectbox(
label = "Please select the phrase for K-RSL dataset here",
options = my_phrases,
index = 0,
key = 'phrase'
)
signer_id = st.sidebar.selectbox(
label = "Please select the signer id for K-RSL dataset here",
options = signer_ids,
index = 0,
key = 'signer_id'
)
upload = st.empty()
start_button = st.empty()
stop_button = st.empty()
with upload:
f = st.file_uploader('Upload Video file (mpeg/mp4 format)', key = state.upload_key)
if lang == "Russian" and len(phrase) != 0 and len(signer_id) != 0:
video_path = "/app/test_videos/" + str(phrase_dict[phrase]) + "/" + "P" + str(signer_id) + "_" + "S" + str(phrase_dict[phrase]) + "_" + "00.mp4"
if not os.path.exists(video_path):
st.info("The video is not in the database!")
return
vf = cv2.VideoCapture(video_path)
vf = cv2.VideoCapture(video_path)
frames = get_frames(video_path=video_path)
indices = sample_indices(n=len(frames), p_drop=p_drop, random_drop=random_drop)
frames = [Image.fromarray(frames[i].asnumpy(), 'RGB') for i in indices]
if lang == "Russian":
frames = map(transform_krsl, frames)
else:
frames = map(transform_phoenix, frames)
frames = np.stack(list(frames))
if lang == "Russian":
epoch = 18
vocab = create_vocab(split="train_rus", sep=",")
else:
vocab = create_vocab(split="train_ger", sep="|")
if backbone == "ResNet18":
epoch = 100
else:
epoch = 200
hyp = inference(epoch, vocab, frames, lang)
if not state.run:
start_button.empty()
start = start_button.button("PREDICT")
state.start = start
if state.start:
start_button.empty()
state.enabled = False
if state.run:
if phrase in phrase_dict:
phrase_id = phrase_dict[phrase]
state.upload_key = str(randint(1000, int(1e6)))
state.enabled = True
state.run = False
ProcessFrames(vf, stop_button, hyp, video_path, phrase_id, signer_ids, state)
else:
state.run = True
trigger_rerun()
if f is not None:
tfile = tempfile.NamedTemporaryFile(delete = False)
tfile.write(f.read())
upload.empty()
vf = cv2.VideoCapture(tfile.name)
frames = get_frames(video_path=tfile.name)
indices = sample_indices(n=len(frames), p_drop=p_drop, random_drop=random_drop)
frames = [Image.fromarray(frames[i].asnumpy(), 'RGB') for i in indices]
if lang == "Russian":
frames = map(transform_krsl, frames)
else:
frames = map(transform_phoenix, frames)
frames = np.stack(list(frames))
if lang == "Russian":
epoch = 18
vocab = create_vocab(split="train_rus", sep=",")
else:
vocab = create_vocab(split="train_ger", sep="|")
if backbone == "ResNet18":
epoch = 100
else:
epoch = 200
hyp = inference(epoch, vocab, frames, lang)
if not state.run:
start_button.empty()
start = start_button.button("PREDICT ")
state.start = start
with open("/app/app/upload.txt") as f:
bool = int(f.readline())
phrase_id = None
if phrase in phrase_dict:
phrase_id = phrase_dict[phrase]
if bool and phrase_id != None:
up = upload.button("UPLOAD TO DATABASE")
state.upload_db = up
if state.upload_db:
with open("/app/app/test_ids.txt", "a") as f:
if "51" not in signer_ids:
f.write("51\n")
shutil.move(tfile.name, f"/app/test_videos/{phrase_id}/P51_S{phrase_id}_00.mp4")
st.info("The data was successfully uploaded to the database!")
state.run = False
if state.start:
start_button.empty()
state.enabled = False
if state.run:
f.close()
state.upload_key = str(randint(1000, int(1e6)))
state.enabled = True
state.run = False
phrase_id = None
if phrase in phrase_dict:
phrase_id = phrase_dict[phrase]
ProcessFrames(vf, stop_button, hyp, tfile, phrase_id, signer_ids, state)
else:
state.run = True
trigger_rerun()
return case_md
@fancy_cache(unique_to_session=True, allow_output_mutation=True)
def get_static_store() -> Dict:
"""This dictionary is initialized once and can be used to store the files uploaded"""
return {}
select_block_container_style()
nltk.download("punkt")
st.title("Рулетка кейсов")
state = SessionState.get(cases=[], played_inds=[], orig_case_num=0, file_value=None)
menu_state = st.radio("Показать меню", ["Показать", "Скрыть"], 0)
static_store = get_static_store()
file_picker = st.empty()
file_buffer = file_picker.file_uploader("Загрузите файл с кейсами", type="txt")
if file_buffer:
value = file_buffer.getvalue()
if value not in static_store.values():
static_store[file_buffer.getvalue()] = value
else:
static_store.clear()