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import gradio as gr
import pandas as pd
import subprocess
import os
from tempfile import mkdtemp
from timeit import default_timer as timer
from hand_record import create_hand_record_pdf
from pbn_util import validate_pbn, create_single_pbn_string
# Download model and libraries from repo
try:
token = os.environ.get("model_token")
if token:
subprocess.run(["git", "clone", f"https://oauth2:{token}@huggingface.co/vincentlui/bridge_hand_detect"])
subprocess.run(["git", "pull"])
except:
print('Fail to download code')
try:
from bridge_hand_detect2.predict import CardDetectionModel
except Exception as e:
print(e)
from bridge_hand_detect.predict import CardDetectionModel
custom_css = \
"""
/* Hide sort buttons at gr.DataFrame */
.sort-button {
display: none !important;
}
"""
INPUT_IMG_HEIGHT = 480
OUTPUT_IMG_HEIGHT = 320
css = ".output_img {display:block; margin-left: auto; margin-right: auto}"
model = CardDetectionModel()
def predict(image_path, top_hand_idx):
print(top_hand_idx)
start = timer()
df = None
try:
hands, (width,height) = model(image_path, augment=True, top_hand_idx=top_hand_idx)
print(hands)
# Output dataframe
df = default_df.copy(deep=True)#pd.DataFrame(['♠', '♥', '♦', '♣'], columns=[''])
for hand in hands:
df[hand.direction] = [''.join(c) for c in hand.cards]
except:
raise gr.Error('Cannot process image')
end = timer()
print(f'Process time: {end - start:.02f} seconds')
return df
default_df = pd.DataFrame({'':['♠', '♥', '♦', '♣'],
'N': ['']*4,
'E': ['']*4,
'S': ['']*4,
'W': ['']*4})
def save_file(df, cache_dir, files, board_no):
d = cache_dir
if cache_dir is None:
d = mkdtemp()
pbn_str = create_single_pbn_string(df, board_no=board_no)
try:
validate_pbn(pbn_str)
except Exception as e:
print(e)
gr.Warning(f'Fail to save: {e}')
return files, files, d
file_name = f'board_{board_no:03d}.pbn'
file_path = os.path.join(d,file_name)
with open(file_path, 'w') as f:
f.write(pbn_str)
if not file_path in files:
files.append(file_path)
return files, files, d
def create_hand_record(files):
file_path = create_hand_record_pdf(files)
return file_path
with gr.Blocks(css=custom_css) as demo:
gr.Markdown(
"""
# Bridge Hand Scanner - Read all four hands from an image
This app scans an image taken from (e.g. your smartphone) and reads all 52 cards. The top hand is regarded as North.
The results can be exported as a PBN file, which can be imported to other bridge software such as double dummy solvers.
1. Upload an image showing all four hands fanned as shown in the example.
2. Click *Submit*. The scan result will be displayed in the table.
3. Verify the output and correct any missing or wrong card in the table.
4. Enter the information of the deal.
5. Click *Save* to generate a PBN file.
6. In the tab *Hand Record*, You can upload all the PBN files and create a hand record as a PDF file.
Tips:
- This AI reads the values at corners of the playing cards. Make sure they are visible and as large as possible.
- To get the best accuracy, place the cards following the layout in the examples.
Please send your comments to <[email protected]>.
""")
total = gr.State(0)
gradio_cache_dir = gr.State()
files = gr.State([])
with gr.Tab('Scan Image'):
with gr.Row():
with gr.Column():
a1 = gr.Image(type="filepath",sources=['upload'],interactive=True,height=INPUT_IMG_HEIGHT)
with gr.Row():
a2 = gr.ClearButton()
a3 = gr.Button('Submit',variant="primary")
with gr.Accordion("Board Details",open=True):
with gr.Row():
a_board_no = gr.Number(label="Board", value=1, minimum=1, maximum=999, interactive=True, min_width=80)
a_top = gr.Dropdown(['N','E','S','W'], label='Top', value='N', interactive=True, min_width=80, type='index')
a_deck = gr.Dropdown(['Standard (AKQJ)'], label='Deck',
value='Standard (AKQJ)', type='index', interactive=True, min_width=80, scale=2)
# with gr.Accordion("Contract Details",open=False) as a_c:
# with gr.Row():
# with gr.Column(scale=3):
# with gr.Group():
# a_level = gr.Radio(['1','2','3','4','5','6','7','AP'], label='Contract')
# a_trump = gr.Radio(['♠', '♥', '♦', '♣', 'NT'], show_label=False)
# a_dbl = gr.Radio(['X', 'XX'], show_label=False)
# a_declarer = gr.Radio(['N','E','S','W'], label='Declarer', min_width=80, scale=1)
a4 = gr.Examples('examples', a1)
with gr.Column():
b1 = gr.Dataframe(value=default_df, datatype="str", row_count=(4,'fixed'), col_count=(5,'fixed'),
headers=['', 'N', 'E', 'S', 'W'],
interactive=True, column_widths=['8%', '23%','23%','23%','23%'])
b2 = gr.Button('Save')
b3 = gr.File(interactive=False, file_count='multiple')
with gr.Tab('Hand Record'):
with gr.Row():
with gr.Group():
tab2_upload_file = gr.Files(interactive=True)
tab2_submit_button = gr.Button('Create Hand Record',variant="primary")
tab2_download_file = gr.File(interactive=False)
tab2_submit_button.click(create_hand_record, tab2_upload_file, tab2_download_file)
a2.add([a1,b1])
a3.click(predict, [a1, a_top], [b1])
b2.click(save_file, [b1, gradio_cache_dir, files, a_board_no], [b3, tab2_upload_file, gradio_cache_dir])
demo.queue().launch() |