85 lines
3.2 KiB
Python
85 lines
3.2 KiB
Python
from paddleocr import PaddleOCR
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import time
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import cv2
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import numpy as np
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import os
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import logging
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logging.basicConfig(level=logging.INFO)
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# ocr = PaddleOCR(
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# use_doc_orientation_classify=False,
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# use_doc_unwarping=False,
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# use_textline_orientation=False) # text detection + text recognition
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# ocr = PaddleOCR(use_doc_orientation_classify=True, use_doc_unwarping=True) # text image preprocessing + text detection + textline orientation classification + text recognition
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# ocr = PaddleOCR(use_doc_orientation_classify=False, use_doc_unwarping=False) # text detection + textline orientation classification + text recognition
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ocr = PaddleOCR(
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det_model_dir="/mnt/models/ocr/ch_PP-OCRv4_det_server_infer", # https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
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rec_model_dir="/mnt/models/ocr/ch_PP-OCRv4_rec_server_infer", # https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
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cls_model_dir="/mnt/models/ocr/ch_ppocr_mobile_v2.0_cls_infer", # https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
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use_doc_orientation_classify=False,
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use_doc_unwarping=False,
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use_gpu=True,
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cpu_threads=32,
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enable_mkldnn=True,
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use_textline_orientation=False) # Switch to PP-OCRv5_mobile models
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time.sleep(5)
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total_time = 0
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WITH_PREPROCESSING = True
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NOISE_THRESHOLD = int(os.getenv("NOISE_THRESHOLD", "10"))
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for i in range(1):
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# img_path = f"test{i%5}.pdf"
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img_path = f"1.jpg"
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print(f"run for {img_path}")
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image = cv2.imread(img_path)
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start = time.time()
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# height, width = image.shape[:2]
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# image = cv2.resize(image, (width // 2, height // 2))
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if WITH_PREPROCESSING:
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# 计算图像的灰度直方图
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if len(image.shape) == 3: # 如果是彩色图像 (具有三个通道)
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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else:
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gray = image
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logging.info(f"cvtColor takes {time.time() - start} s")
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# 噪声水平估计（简单地使用标准差作为代理）
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noise_level = np.std(gray) # 在灰度图像上评估噪声水平
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logging.info(f"noise_level: {noise_level}")
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if noise_level > NOISE_THRESHOLD:
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logging.info("denoising")
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if len(image.shape) == 3: # 如果是彩色图像
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logging.info("denoisin for color")
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processed_img = cv2.fastNlMeansDenoisingColored(image)
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else: # 如果是灰度图像
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logging.info("denoising for gray")
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processed_img = cv2.fastNlMeansDenoising(image)
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else:
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processed_img = image
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logging.info(f"Preprocessing takes {time.time() - start} s")
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else:
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processed_img = image
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logging.info("Skip Preprocessing")
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result = ocr.ocr(image)
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end = time.time()
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print(f"{img_path} elapsed: {end - start}")
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total_time += (end - start)
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print(f"total time: {total_time}")
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for idx in range(len(result)):
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res = result[idx]
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print(f"res: {res}")
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# for line in res:
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# print(line)
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# result = ocr.predict("docs/images/00006737.jpg")
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# for res in result:
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# res.print()
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# res.save_to_img("output")
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# res.save_to_json("output")
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