15 changed files with 120 additions and 226 deletions
@ -0,0 +1,2 @@ |
|||
import subprocess |
|||
subprocess.Popen(["python", "main.py"]) |
|||
@ -1,133 +0,0 @@ |
|||
from ultralytics import YOLO |
|||
import numpy as np |
|||
import cv2 |
|||
from paddleocr import PaddleOCR |
|||
import re |
|||
import os |
|||
current_dir = os.path.dirname(os.path.abspath(__file__)) |
|||
img_path = os.path.join(current_dir, "test1.jpg") |
|||
img = cv2.imread(img_path) |
|||
model = YOLO(os.path.join(current_dir, "best.pt")) |
|||
results = model.predict(img_path, device='cpu') |
|||
ocr = PaddleOCR( |
|||
use_gpu=False, |
|||
use_angle_cls=True, |
|||
det_model_dir=os.path.join(current_dir, "ocr/simple/ch_PP-OCRv4_det_infer"), |
|||
rec_model_dir=os.path.join(current_dir, "ocr/simple/ch_PP-OCRv4_rec_infer"),) |
|||
|
|||
for r in results: |
|||
boxes = r.boxes |
|||
clses = np.array(boxes.cls).astype(int) |
|||
points = np.array(boxes.xyxy).astype(int) |
|||
target_0 = [] |
|||
target_1 = [] |
|||
target_2 = [] |
|||
target_3 = [] |
|||
for cls, point in zip(clses, points): |
|||
if cls == 0: |
|||
target_0.append(point) |
|||
elif cls == 1: |
|||
target_1.append(point) |
|||
elif cls == 2: |
|||
target_2.append(point) |
|||
elif cls == 3: |
|||
target_3.append(point) |
|||
|
|||
# 初始化结果字典 |
|||
results_summary = { |
|||
'target_0': [], |
|||
'target_1': [], |
|||
'target_2': [], |
|||
'target_3': [] |
|||
} |
|||
|
|||
# 检查类别数量 |
|||
if (len(target_0) == 2 and len(target_1) == 1 and (len(target_2) == 1 or len(target_2) == 2) and len(target_3) == 2): |
|||
|
|||
# 处理类别0 |
|||
target_0 = sorted(target_0, key=lambda x: x[0]) |
|||
left_point = target_0[0] |
|||
right_point = target_0[1] |
|||
for target, name in zip([left_point, right_point], ['地址', '姓名']): |
|||
target_img = img[target[1]:target[3], target[0]:target[2]] |
|||
cv2.imwrite(f'{name}.jpg', target_img) |
|||
result = ocr.ocr(target_img) |
|||
out = '' |
|||
if not result or not any(result): |
|||
out = '未识别到文字' |
|||
else: |
|||
for lines in result: |
|||
for line in lines: |
|||
out += line[1][0] |
|||
results_summary['target_0'].append(f"{name.capitalize()}: {out}") |
|||
|
|||
# # 处理类别1 |
|||
# for target in target_1: |
|||
# target_img = img[target[1]:target[3], target[0]:target[2]] |
|||
# cv2.imwrite(f'当前有功.jpg', target_img) |
|||
# result = ocr.ocr(target_img) |
|||
# out = '' |
|||
# for lines in result: |
|||
# for line in lines: |
|||
# out += line[1][0] |
|||
# out = out[:-2] + '.' + out[-2:] |
|||
# results_summary['target_1'].append(f"当前有功: {out}") |
|||
|
|||
# 处理类别1 |
|||
for target in target_1: |
|||
target_img = img[target[1]-5:target[3]+5, target[0]-5:target[2]+5] |
|||
cv2.imwrite(f'当前有功.jpg', target_img) |
|||
result = ocr.ocr(target_img, det=False) |
|||
for lines in result: |
|||
for line in lines: |
|||
out = line[0] |
|||
out = re.sub(r'\.', '', out) |
|||
out = out[:-2] + '.' + out[-2:] |
|||
results_summary['target_1'].append(f"当前有功: {out}") |
|||
|
|||
# 处理类别2 |
|||
if len(target_2) == 2: |
|||
target_2_sorted = sorted(target_2, key=lambda x: x[1]) |
|||
top_target = target_2_sorted[0] |
|||
target_img = img[top_target[1]:top_target[3], top_target[0]:top_target[2]] |
|||
elif len(target_2) == 1: |
|||
top_target = target_2[0] |
|||
target_img = img[top_target[1]:top_target[3], top_target[0]:top_target[2]] |
|||
cv2.imwrite(f'电表资产号.jpg', target_img) |
|||
result = ocr.ocr(target_img) |
|||
longest_line = "" |
|||
max_length = 0 |
|||
for lines in result: |
|||
for line in lines: |
|||
text = line[1][0] |
|||
if len(text) > max_length: |
|||
longest_line = text |
|||
max_length = len(text) |
|||
results_summary['target_2'].append(f"电表资产号: {longest_line}") |
|||
|
|||
# 处理类别3 |
|||
target_3 = sorted(target_3, key=lambda x: x[0]) |
|||
left_point = target_3[0] |
|||
right_point = target_3[1] |
|||
for target, name in zip([left_point, right_point], ['封印1', '封印2']): |
|||
target_img = img[target[1]:target[3], target[0]:target[2]] |
|||
height, width = target_img.shape[:2] |
|||
if width <= height: |
|||
target_img = cv2.transpose(target_img) |
|||
target_img = cv2.flip(target_img, flipCode=1) |
|||
cv2.imwrite(f'{name}.jpg', target_img) |
|||
result = ocr.ocr(target_img) |
|||
out = '' |
|||
for lines in result: |
|||
for line in lines: |
|||
out += line[1][0] |
|||
results_summary['target_3'].append(f"{name.capitalize()}: {out}") |
|||
|
|||
for category, result_list in results_summary.items(): |
|||
for result in result_list: |
|||
print(result) |
|||
else: |
|||
print("图像不清晰或要素不全请重新拍摄或人工记录") |
|||
|
|||
|
|||
|
|||
|
Before Width: | Height: | Size: 4.7 MiB After Width: | Height: | Size: 4.3 MiB |
@ -0,0 +1,4 @@ |
|||
@echo off |
|||
cd E:\code\code |
|||
python main.py start |
|||
exit |
|||
@ -0,0 +1,3 @@ |
|||
DIM objShell |
|||
set objShell = wscript.createObject("wscript.shell") |
|||
iReturn = objShell.Run("start.bat", 0, TRUE) |
|||
@ -0,0 +1,6 @@ |
|||
import Dexie from 'dexie'; |
|||
|
|||
export const myDatabase = new Dexie('myDatabase'); |
|||
myDatabase.version(1).stores({ |
|||
users: '++id, Name,Address, lastPower,currentMeterId,Qrcode1,qrcode1,create_at,update_at', // Primary key and indexed props
|
|||
}); |
|||
Loading…
Reference in new issue