559 lines
19 KiB
Python
559 lines
19 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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RK3588 PaddleOCR RKNN推理程序
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使用转换后的RKNN模型在RK3588上进行OCR文本检测和识别
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"""
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import cv2
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import yaml
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import numpy as np
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import math
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from rknn.api import RKNN
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import argparse
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import os
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import time
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class RK3588OCR:
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def __init__(self, det_model_path, rec_model_path):
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"""
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初始化OCR推理器
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Args:
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det_model_path: 文本检测RKNN模型路径
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rec_model_path: 文本识别RKNN模型路径
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"""
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self.det_model_path = det_model_path
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self.rec_model_path = rec_model_path
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# 初始化RKNN实例
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self.det_rknn = RKNN(verbose=False)
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self.rec_rknn = RKNN(verbose=False)
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# 模型输入尺寸
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self.det_input_size = (640, 640)
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self.rec_input_size = (320, 48)
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# 文本检测相关参数
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self.det_threshold = 0.3
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self.det_box_threshold = 0.6
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self.det_unclip_ratio = 1.5
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# 加载模型
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self._load_models()
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self.character = self.get_dict()
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def get_dict(self, dict_path='/home/orangepi/Desktop/kangda_robotic_dog/机器狗后台服务/dict.yaml'):
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"""
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加载字典
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"""
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with open(dict_path, 'r', encoding='utf-8') as f:
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dict_rec = yaml.safe_load(f)
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return dict_rec.get('character_dict', [])
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def _load_models(self):
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"""加载RKNN模型"""
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print("加载文本检测模型...")
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ret = self.det_rknn.load_rknn(self.det_model_path)
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if ret != 0:
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raise Exception(f"加载检测模型失败: {ret}")
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ret = self.det_rknn.init_runtime(target='rk3588')
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if ret != 0:
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raise Exception(f"初始化检测模型运行环境失败: {ret}")
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print("加载文本识别模型...")
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ret = self.rec_rknn.load_rknn(self.rec_model_path)
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if ret != 0:
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raise Exception(f"加载识别模型失败: {ret}")
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ret = self.rec_rknn.init_runtime(target='rk3588')
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if ret != 0:
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raise Exception(f"初始化识别模型运行环境失败: {ret}")
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print("模型加载完成!")
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def resize_norm_img_det(self, img, input_shape=(640, 640)):
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"""
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检测模型的图像预处理 - 固定输入形状 [1, 3, 640, 640]
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"""
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h, w, _ = img.shape
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target_h, target_w = input_shape
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# 计算缩放比例 - 保持宽高比
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ratio_h = target_h / h
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ratio_w = target_w / w
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ratio = min(ratio_h, ratio_w)
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# 计算缩放后的尺寸
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new_h = int(h * ratio)
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new_w = int(w * ratio)
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# 调整图像大小
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resized_img = cv2.resize(img, (new_w, new_h))
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# 创建目标尺寸的图像，用灰色填充
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padded_img = np.ones((target_h, target_w, 3), dtype=np.float32) * 114.0 # 直接用float32
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# 计算居中位置
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top = (target_h - new_h) // 2
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left = (target_w - new_w) // 2
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# 将缩放后的图像放到居中位置
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padded_img[top:top+new_h, left:left+new_w] = resized_img.astype(np.float32)
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# 归一化
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img = (padded_img / 255.0 - np.array([0.485, 0.456, 0.406], dtype=np.float32)) / np.array([0.229, 0.224, 0.225], dtype=np.float32)
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img = img.transpose(2, 0, 1).astype(np.float32)
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img = np.expand_dims(img, axis=0).astype(np.float32)
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return img, ratio, (top, left)
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def post_process_det(self, dt_boxes, ratio, padding_info, ori_shape):
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"""
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检测结果后处理 - 适配固定输入形状
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"""
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if dt_boxes is None:
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return None
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ori_h, ori_w = ori_shape
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top, left = padding_info
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# 将坐标从模型输出空间转换回原图空间
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dt_boxes[:, :, 0] = (dt_boxes[:, :, 0] - left) / ratio
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dt_boxes[:, :, 1] = (dt_boxes[:, :, 1] - top) / ratio
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# 裁剪到原图范围内
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dt_boxes[:, :, 0] = np.clip(dt_boxes[:, :, 0], 0, ori_w)
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dt_boxes[:, :, 1] = np.clip(dt_boxes[:, :, 1], 0, ori_h)
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return dt_boxes
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def boxes_from_bitmap(self, pred, bitmap, dest_width, dest_height, max_candidates=1000, box_thresh=0.6):
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"""
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从位图中提取文本框
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"""
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bitmap = bitmap.astype(np.uint8)
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height, width = bitmap.shape
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# 查找轮廓
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contours, _ = cv2.findContours(bitmap, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
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num_contours = min(len(contours), max_candidates)
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boxes = []
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scores = []
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for i in range(num_contours):
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contour = contours[i]
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points, sside = self.get_mini_boxes(contour)
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if sside < 5:
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continue
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points = np.array(points)
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score = self.box_score_fast(pred, points.reshape(-1, 2))
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if box_thresh > score:
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continue
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# 扩展box
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box = self.unclip(points, 1.5).reshape(-1, 1, 2)
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box, sside = self.get_mini_boxes(box)
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if sside < 5 + 2:
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continue
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box = np.array(box)
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box[:, 0] = np.clip(box[:, 0] / width * dest_width, 0, dest_width)
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box[:, 1] = np.clip(box[:, 1] / height * dest_height, 0, dest_height)
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boxes.append(box.astype(np.int16))
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scores.append(score)
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return np.array(boxes), scores
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def get_mini_boxes(self, contour):
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"""获取最小外接矩形"""
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bounding_box = cv2.minAreaRect(contour)
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points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0])
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index_1, index_2, index_3, index_4 = 0, 1, 2, 3
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if points[1][1] > points[0][1]:
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index_1 = 0
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index_4 = 1
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else:
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index_1 = 1
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index_4 = 0
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if points[3][1] > points[2][1]:
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index_2 = 2
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index_3 = 3
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else:
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index_2 = 3
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index_3 = 2
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box = [points[index_1], points[index_2], points[index_3], points[index_4]]
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return box, min(bounding_box[1])
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def box_score_fast(self, bitmap, _box):
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"""快速计算box得分"""
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h, w = bitmap.shape[:2]
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box = _box.copy()
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xmin = np.clip(np.floor(box[:, 0].min()).astype(int), 0, w - 1)
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xmax = np.clip(np.ceil(box[:, 0].max()).astype(int), 0, w - 1)
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ymin = np.clip(np.floor(box[:, 1].min()).astype(int), 0, h - 1)
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ymax = np.clip(np.ceil(box[:, 1].max()).astype(int), 0, h - 1)
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mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
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box[:, 0] = box[:, 0] - xmin
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box[:, 1] = box[:, 1] - ymin
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cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
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return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
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def unclip(self, box, unclip_ratio):
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"""扩展文本框"""
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from shapely.geometry import Polygon
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import pyclipper
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poly = Polygon(box)
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distance = poly.area * unclip_ratio / poly.length
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offset = pyclipper.PyclipperOffset()
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offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
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expanded = offset.Execute(distance)
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if len(expanded) == 0:
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return box
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else:
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return np.array(expanded[0])
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def resize_norm_img_rec(self, img, input_shape=(320, 48)):
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"""
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识别模型的图像预处理 - 固定输入形状 [1, 3, 48, 320]
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"""
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target_w, target_h = input_shape # 注意：宽度在前
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h, w = img.shape[:2]
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# 计算缩放比例，保持宽高比
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ratio_h = target_h / h
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ratio_w = target_w / w
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ratio = min(ratio_h, ratio_w)
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# 计算缩放后的尺寸
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new_h = int(h * ratio)
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new_w = int(w * ratio)
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# 调整图像大小
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resized_image = cv2.resize(img, (new_w, new_h))
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# 创建目标尺寸的图像，用黑色填充
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padded_image = np.zeros((target_h, target_w, 3), dtype=np.float32) # 直接用float32
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# 将缩放后的图像放到左上角（识别模型通常左对齐）
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padded_image[:new_h, :new_w] = resized_image.astype(np.float32)
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# 归一化
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# padded_image = (padded_image / 255.0 - np.array([0.485, 0.456, 0.406], dtype=np.float32)) / np.array([0.229, 0.224, 0.225], dtype=np.float32)
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# 不缩放反而会将识别结果再移后一个??
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padded_image = (padded_image / 255.0).astype(np.float32)
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padded_image = padded_image.transpose((2, 0, 1)).astype(np.float32)
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return np.expand_dims(padded_image, axis=0).astype(np.float32)
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def decode_rec_result(self, preds_prob):
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"""
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解码识别结果
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"""
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# preds_idx = preds_idx[0]
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preds_prob = preds_prob[0]
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preds_idx = np.argmax(preds_prob, axis=1)
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preds_prob = np.max(preds_prob, axis=1)
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# CTC解码
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last_idx = 0
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preds_text = []
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preds_conf = []
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# print("preds_id", len(preds_idx[0]))
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for i, idx in enumerate(preds_idx):
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if idx != last_idx and idx != 0: # 0是blank
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if idx < len(self.character):
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# print("self.character[idx]", self.character[idx])
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# print("preds_prob[i]", preds_prob[i])
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preds_text.append(self.character[idx])
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preds_conf.append(preds_prob[i])
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last_idx = idx
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text = ''.join(preds_text)
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conf = np.mean(preds_conf) if preds_conf else 0.0
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return text, conf
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def detect_text(self, image):
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"""
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文本检测 - 适配固定输入形状 [1, 3, 640, 640]
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"""
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ori_h, ori_w = image.shape[:2]
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# 预处理
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det_img, ratio, padding_info = self.resize_norm_img_det(image)
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# 推理
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det_output = self.det_rknn.inference(inputs=[det_img], data_format="nchw")[0]
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# 后处理
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mask = det_output[0, 0, :, :]
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threshold = 0.3
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bitmap = (mask > threshold).astype(np.uint8) * 255
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# 从位图中提取文本框（坐标是在640x640空间中的）
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boxes, scores = self.boxes_from_bitmap(mask, bitmap, 640, 640)
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# 将坐标转换回原图空间
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if len(boxes) > 0:
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boxes = self.post_process_det(boxes, ratio, padding_info, (ori_h, ori_w))
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print("*"*100, len(boxes))
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return boxes, scores
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def visualize_det_results(self, image_path, boxes):
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image = cv2.imread(image_path)
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for box in boxes:
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box = np.array(box, dtype=np.int32)
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cv2.polylines(image, [box], True, (0, 255, 0), 2)
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cv2.imwrite('./visual_det.jpg', image)
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def recognize_text(self, image):
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"""
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文本识别
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"""
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# 预处理
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rec_img = self.resize_norm_img_rec(image)
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# 推理
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rec_output = self.rec_rknn.inference(inputs=[rec_img], data_format="nchw")
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# 解码
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text, conf = self.decode_rec_result(rec_output[0])
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# print("")
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return text, conf
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def get_rotate_crop_image(self, img, points):
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"""
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根据四个点坐标裁剪并矫正图像
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"""
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img_crop_width = int(
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max(
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np.linalg.norm(points[0] - points[1]),
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np.linalg.norm(points[2] - points[3])))
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img_crop_height = int(
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max(
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np.linalg.norm(points[0] - points[3]),
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np.linalg.norm(points[1] - points[2])))
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pts_std = np.float32([[0, 0], [img_crop_width, 0],
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[img_crop_width, img_crop_height],
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[0, img_crop_height]])
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M = cv2.getPerspectiveTransform(points, pts_std)
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dst_img = cv2.warpPerspective(
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img,
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M, (img_crop_width, img_crop_height),
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borderMode=cv2.BORDER_REPLICATE,
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flags=cv2.INTER_CUBIC)
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dst_img_height, dst_img_width = dst_img.shape[0:2]
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if dst_img_height * 1.0 / dst_img_width >= 1.5:
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dst_img = np.rot90(dst_img)
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return dst_img
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def ocr(self, image_path):
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"""
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完整的OCR流程
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"""
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# 读取图像
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image = cv2.imread(image_path)
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if image is None:
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return []
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# 1. 文本检测
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dt_boxes, scores = self.detect_text(image)
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# 可视化检测框
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self.visualize_det_results(image_path, dt_boxes)
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if dt_boxes is None or len(dt_boxes) == 0:
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return []
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# 2. 文本识别
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ocr_results = []
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text_list = []
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confidence_list = []
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for i, box in enumerate(dt_boxes):
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# 裁剪文本区域
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box_points = box.astype(np.float32)
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crop_img = self.get_rotate_crop_image(image, box_points)
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# 识别文本
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text, conf = self.recognize_text(crop_img)
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if conf > 0.4: # 置信度过滤
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ocr_results.append({
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'text': text,
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'confidence': conf,
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'box': box.tolist(),
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'score': scores[i] if i < len(scores) else 0.0
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})
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text_list.append(text)
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confidence_list.append(round(conf.item(), 2))
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# return ocr_results
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return [text_list, confidence_list]
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def release(self):
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"""释放资源"""
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self.det_rknn.release()
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self.rec_rknn.release()
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def main():
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parser = argparse.ArgumentParser(description='RK3588 PaddleOCR RKNN推理')
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parser.add_argument('--det_model', type=str, required=True, help='文本检测RKNN模型路径')
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parser.add_argument('--rec_model', type=str, required=True, help='文本识别RKNN模型路径')
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parser.add_argument('--image', type=str, help='输入图像路径')
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parser.add_argument('--video', type=str, help='输入视频路径')
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parser.add_argument('--camera', type=int, help='摄像头设备ID')
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parser.add_argument('--output', type=str, help='输出路径')
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parser.add_argument('--show', action='store_true', help='显示结果')
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args = parser.parse_args()
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# 检查模型文件
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if not os.path.exists(args.det_model):
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print(f"检测模型文件不存在: {args.det_model}")
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return
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if not os.path.exists(args.rec_model):
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print(f"识别模型文件不存在: {args.rec_model}")
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return
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# 初始化OCR
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print("初始化RK3588 OCR...")
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ocr = RK3588OCR(args.det_model, args.rec_model)
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try:
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if args.image:
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# 图像模式
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print(f"处理图像: {args.image}")
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# 进行OCR
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start_time = time.time()
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text, confidence = ocr.ocr(args.image)
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print("text", text)
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print("confidence", confidence)
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||
|
||
total_time = time.time() - start_time
|
||
|
||
# 打印结果
|
||
print(f"\n总耗时: {total_time:.3f}s")
|
||
print(f"识别结果:")
|
||
for i in range(len(text)):
|
||
print(f"{i+1}. 文本: '{text[i]}', 置信度: {confidence[i]:.3f}")
|
||
|
||
# 绘制结果
|
||
# annotated_image = ocr.draw_results(image, results)
|
||
|
||
# 保存或显示结果
|
||
# if args.output:
|
||
# cv2.imwrite(args.output, annotated_image)
|
||
# print(f"结果已保存到: {args.output}")
|
||
|
||
# if args.show:
|
||
# cv2.imshow('OCR结果', annotated_image)
|
||
# cv2.waitKey(0)
|
||
# cv2.destroyAllWindows()
|
||
|
||
elif args.video or args.camera is not None:
|
||
# 视频或摄像头模式
|
||
if args.video:
|
||
cap = cv2.VideoCapture(args.video)
|
||
print(f"处理视频: {args.video}")
|
||
else:
|
||
cap = cv2.VideoCapture(args.camera)
|
||
print(f"使用摄像头: {args.camera}")
|
||
|
||
if not cap.isOpened():
|
||
print("无法打开视频源")
|
||
return
|
||
|
||
while True:
|
||
ret, frame = cap.read()
|
||
if not ret:
|
||
break
|
||
|
||
# 进行OCR
|
||
results = ocr.ocr(frame)
|
||
|
||
# 绘制结果
|
||
annotated_frame = ocr.draw_results(frame, results)
|
||
|
||
# 显示结果
|
||
cv2.imshow('Real-time OCR', annotated_frame)
|
||
|
||
# 按'q'退出
|
||
if cv2.waitKey(1) & 0xFF == ord('q'):
|
||
break
|
||
|
||
cap.release()
|
||
cv2.destroyAllWindows()
|
||
|
||
else:
|
||
print("请指定输入源: --image, --video 或 --camera")
|
||
|
||
finally:
|
||
ocr.release()
|
||
|
||
if __name__ == "__main__":
|
||
# 如果直接运行脚本，提供示例用法
|
||
'''
|
||
启动命令示例
|
||
python 010使用PaddleOCR_rknn.py --det_model ./text_detection.rknn --rec_model ./text_recognition.rknn --image ./image_test/632e474452d560edd7004f745319ff00_frame_000730.jpg --output ./result.jpg
|
||
注:
|
||
导出的额rknn模型没有进行归一化, 归一化参数mean=0,std=1
|
||
'''
|
||
if len(os.sys.argv) == 1:
|
||
print("RK3588 PaddleOCR RKNN推理程序")
|
||
print("\n使用示例:")
|
||
print("# 处理单张图像")
|
||
print("python rk3588_ocr.py \\")
|
||
print(" --det_model ./rknn_models/text_detection.rknn \\")
|
||
print(" --rec_model ./rknn_models/text_recognition.rknn \\")
|
||
print(" --image ./test.jpg \\")
|
||
print(" --output ./result.jpg \\")
|
||
print(" --show")
|
||
print()
|
||
print("# 实时摄像头OCR")
|
||
print("python rk3588_ocr.py \\")
|
||
print(" --det_model ./rknn_models/text_detection.rknn \\")
|
||
print(" --rec_model ./rknn_models/text_recognition.rknn \\")
|
||
print(" --camera 0")
|
||
print()
|
||
print("# 处理视频文件")
|
||
print("python rk3588_ocr.py \\")
|
||
print(" --det_model ./rknn_models/text_detection.rknn \\")
|
||
print(" --rec_model ./rknn_models/text_recognition.rknn \\")
|
||
print(" --video ./input_video.mp4")
|
||
else:
|
||
main() |