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import os
import torch
import urllib
from PIL import Image
import streamlit as st
from pathlib import Path
def check_suffix(file='yolov5s.pt', suffix=('.pt',), msg=''):
# Check file(s) for acceptable suffix
if file and suffix:
if isinstance(suffix, str):
suffix = [suffix]
for f in file if isinstance(file, (list, tuple)) else [file]:
s = Path(f).suffix.lower() # file suffix
if len(s):
assert s in suffix, f"{msg}{f} acceptable suffix is {suffix}"
def check_file(file, suffix=''):
# Search/download file (if necessary) and return path
check_suffix(file, suffix) # optional
file = str(file) # convert to str()
if os.path.isfile(file) or not file: # exists
return file
elif file.startswith(('http:/', 'https:/')): # download
url = file # warning: Pathlib turns :// -> :/
# '%2F' to '/', split https://url.com/file.txt?auth
file = Path(urllib.parse.unquote(file).split('?')[0]).name
if os.path.isfile(file):
print(f'Found {url} locally at {file}') # file already exists
else:
print(f'Downloading {url} to {file}...')
torch.hub.download_url_to_file(url, file)
assert Path(file).exists() and Path(file).stat(
).st_size > 0, f'File download failed: {url}' # check
return file
st.title("Hololive Waifu Classification")
image = st.text_input('Image URL', '')
st.info(
'Images for quick tesing:\n \n \n'
' - https://i.imgur.com/tFZwWYw.jpg'
'\n \n \n'
' - https://static.wikia.nocookie.net/omniversal-battlefield/images/b/bd/Council.jpg'
'\n \n \n'
'https://rare-gallery.com/uploads/posts/951368-anime-anime-girls-digital-art-artwork-2D-portrait.jpg')
pretrained = st.selectbox('Select pre-trained', ('2022.11.01-YOLOv5x6_1280-Hololive_Waifu_Classification.pt', 'last.pt'))
imgsz = st.number_input(label='Image Size', min_value=None, max_value=None, value=1280, step=1)
conf = st.slider(label='Confidence threshold', min_value=0.0, max_value=1.0, value=0.25, step=0.01)
iou = st.slider(label='IoU threshold', min_value=0.0, max_value=1.0, value=0.45, step=0.01)
multi_label = st.selectbox('Multiple labels per box', (False, True))
agnostic = st.selectbox('Class-agnostic', (False, True))
amp = st.selectbox('Automatic Mixed Precision inference', (False, True))
max_det = st.number_input(label='Maximum number of detections per image', min_value=None, max_value=None, value=1000, step=1)
if st.button('Excute'):
with st.spinner('Loading the image...'):
image_path = check_file(image)
input_image = Image.open(image_path)
with st.spinner('Loading the model...'):
# model = torch.hub.load('ultralytics/yolov5', 'custom', path=os.path.join('pretrained', pretrained))
model = torch.hub.load('ultralytics/yolov5', 'custom', path=pretrained)
with st.spinner('Updating configuration...'):
model.conf = float(conf)
model.max_det = int(max_det)
model.iou = float(iou)
model.agnostic = agnostic
model.multi_label = multi_label
model.amp = amp
with st.spinner('Predicting...'):
results = model(input_image, size=int(imgsz))
for img in results.render():
st.image(img)
st.write(results.pandas().xyxy[0])
os.remove(image_path)
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