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import hashlib
from types import SimpleNamespace
import gradio as gr
from PIL import Image
from ultralytics import YOLO
from ultralytics.utils.plotting import save_one_box
import easyocr
import zxingcpp
import numpy as np
from utils import custom_plot
model = YOLO("YOLOV8s_Barcode_Detection.pt")
reader = easyocr.Reader(['en'])
key_hash = "6734e0268423441620ada6c66bf76c255500139962741833ab33185274b8a010"
hash = lambda x: hashlib.sha256(x.encode()).hexdigest()
def process_image(input_img, activation_key, progress=gr.Progress()):
if input_img is None:
return None, "請上傳圖片後再進行辨識。"
if hash(activation_key) != key_hash:
return None, "金鑰錯誤，請確認後再試一次。"
# Perform object detection on an image
result = model(input_img, imgsz=(1280))[0]
crops = []
for d in result.boxes:
crops.append(save_one_box(
d.xyxy,
result.orig_img.copy(),
save=False,
))
texts = []
for pr, crop in enumerate(crops):
progress((pr+1) / len(crops), desc="辨識中")
img = Image.fromarray(crop)
res = zxingcpp.read_barcodes(img)
if not res:
# rotate and retry
for i in range(1, 8):
res = zxingcpp.read_barcodes(img.rotate(i, resample=2))
if res:
break
res = zxingcpp.read_barcodes(img.rotate(-i, resample=2))
if res:
break
if not res:
# resort to ocr the bottom-left no.
full_res = reader.readtext(crop, allowlist='-0123456789')
res = sorted(
[r for r in full_res if (
r[0][3][0] < (img.width/4) and r[0][3][1] > (img.height/3) and
r[0][0][0] < (img.width/4) and r[0][0][1] > (img.height/2)
)],
key=lambda x: x[2], reverse=True,
)
if res:
pred_text = res[0][1]
# sanity check if the no. is not divided into multiple box
cur_box = res[0]
# if len(cur_box[1]) < 13:
other_boxes = [r for r in full_res if r[1] != cur_box[1]]
thrs = np.linalg.norm(img.size) / 25
while other_boxes:
upper = np.linalg.norm(cur_box[0][1] - np.array([b[0][0] for b in other_boxes]), axis=1)
lower = np.linalg.norm(cur_box[0][2] - np.array([b[0][3] for b in other_boxes]), axis=1)
is_same = (upper < thrs) & (lower < thrs)
rank = sorted(
[(i, dist, s) for (i, dist), s in zip(enumerate(lower + upper), is_same) if s],
key=lambda x: x[1]
)
if rank:
cur_box = other_boxes[rank[0][0]]
other_boxes = [r for r in other_boxes if r[1] != cur_box[1]]
pred_text += cur_box[1]
else:
break
# HACK
if len(pred_text) != 15:
res = []
res = [SimpleNamespace(text=pred_text)] if res else []
texts.append(res[0].text if res else None)
output_text = '\n'.join([t for t in texts if isinstance(t, str)])
results_img = custom_plot(
result, font_size=40, pil=True,
barcode_texts=texts,
)
return results_img, output_text
# Defining the Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("# Barcode")
gr.Markdown("請用下方 example 圖片測試條碼辨識功能，或上傳您自己的圖片。")
with gr.Row():
with gr.Column():
input_view = gr.Image(type="pil", label="輸入圖片")
activation_key = gr.Textbox(label="金鑰")
btn = gr.Button("辨識", variant="primary")
gr.Examples(examples=[["example1.jpeg", ""], ["example2.jpeg", ""]], inputs=input_view)
with gr.Column():
output_view = gr.Image(type="pil", label="辨識結果")
with gr.Column():
text_output = gr.Textbox(label="條碼內容")
# Wire up the button
btn.click(
fn=process_image,
inputs=[input_view, activation_key],
outputs=[output_view, text_output]
)
if __name__ == "__main__":
demo.launch()
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