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133 lines
4.5 KiB
133 lines
4.5 KiB
from ultralytics import YOLO
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import numpy as np
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import cv2
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from paddleocr import PaddleOCR
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import re
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import os
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current_dir = os.path.dirname(os.path.abspath(__file__))
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img_path = os.path.join(current_dir, "test1.jpg")
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img = cv2.imread(img_path)
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model = YOLO(os.path.join(current_dir, "best.pt"))
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results = model.predict(img_path, device='cpu')
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ocr = PaddleOCR(
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use_gpu=False,
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use_angle_cls=True,
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det_model_dir=os.path.join(current_dir, "ocr/simple/ch_PP-OCRv4_det_infer"),
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rec_model_dir=os.path.join(current_dir, "ocr/simple/ch_PP-OCRv4_rec_infer"),)
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for r in results:
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boxes = r.boxes
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clses = np.array(boxes.cls).astype(int)
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points = np.array(boxes.xyxy).astype(int)
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target_0 = []
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target_1 = []
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target_2 = []
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target_3 = []
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for cls, point in zip(clses, points):
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if cls == 0:
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target_0.append(point)
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elif cls == 1:
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target_1.append(point)
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elif cls == 2:
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target_2.append(point)
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elif cls == 3:
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target_3.append(point)
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# 初始化结果字典
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results_summary = {
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'target_0': [],
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'target_1': [],
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'target_2': [],
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'target_3': []
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}
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# 检查类别数量
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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):
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# 处理类别0
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target_0 = sorted(target_0, key=lambda x: x[0])
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left_point = target_0[0]
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right_point = target_0[1]
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for target, name in zip([left_point, right_point], ['地址', '姓名']):
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target_img = img[target[1]:target[3], target[0]:target[2]]
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cv2.imwrite(f'{name}.jpg', target_img)
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result = ocr.ocr(target_img)
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out = ''
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if not result or not any(result):
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out = '未识别到文字'
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else:
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for lines in result:
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for line in lines:
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out += line[1][0]
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results_summary['target_0'].append(f"{name.capitalize()}: {out}")
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# # 处理类别1
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# for target in target_1:
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# target_img = img[target[1]:target[3], target[0]:target[2]]
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# cv2.imwrite(f'当前有功.jpg', target_img)
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# result = ocr.ocr(target_img)
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# out = ''
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# for lines in result:
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# for line in lines:
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# out += line[1][0]
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# out = out[:-2] + '.' + out[-2:]
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# results_summary['target_1'].append(f"当前有功: {out}")
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# 处理类别1
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for target in target_1:
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target_img = img[target[1]-5:target[3]+5, target[0]-5:target[2]+5]
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cv2.imwrite(f'当前有功.jpg', target_img)
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result = ocr.ocr(target_img, det=False)
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for lines in result:
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for line in lines:
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out = line[0]
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out = re.sub(r'\.', '', out)
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out = out[:-2] + '.' + out[-2:]
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results_summary['target_1'].append(f"当前有功: {out}")
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# 处理类别2
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if len(target_2) == 2:
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target_2_sorted = sorted(target_2, key=lambda x: x[1])
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top_target = target_2_sorted[0]
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target_img = img[top_target[1]:top_target[3], top_target[0]:top_target[2]]
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elif len(target_2) == 1:
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top_target = target_2[0]
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target_img = img[top_target[1]:top_target[3], top_target[0]:top_target[2]]
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cv2.imwrite(f'电表资产号.jpg', target_img)
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result = ocr.ocr(target_img)
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longest_line = ""
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max_length = 0
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for lines in result:
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for line in lines:
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text = line[1][0]
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if len(text) > max_length:
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longest_line = text
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max_length = len(text)
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results_summary['target_2'].append(f"电表资产号: {longest_line}")
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# 处理类别3
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target_3 = sorted(target_3, key=lambda x: x[0])
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left_point = target_3[0]
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right_point = target_3[1]
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for target, name in zip([left_point, right_point], ['封印1', '封印2']):
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target_img = img[target[1]:target[3], target[0]:target[2]]
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height, width = target_img.shape[:2]
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if width <= height:
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target_img = cv2.transpose(target_img)
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target_img = cv2.flip(target_img, flipCode=1)
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cv2.imwrite(f'{name}.jpg', target_img)
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result = ocr.ocr(target_img)
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out = ''
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for lines in result:
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for line in lines:
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out += line[1][0]
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results_summary['target_3'].append(f"{name.capitalize()}: {out}")
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for category, result_list in results_summary.items():
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for result in result_list:
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print(result)
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else:
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print("图像不清晰或要素不全请重新拍摄或人工记录")
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