516 lines
18 KiB
Python
516 lines
18 KiB
Python
import cv2
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import numpy as np
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from rknn.api import RKNN
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import math
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import time
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class PaddleOCRRKNN:
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def __init__(self, det_model_path, rec_model_path, target='rk3588'):
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"""
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初始化RKNN推理器
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Args:
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det_model_path: 检测模型路径 (det.rknn)
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rec_model_path: 识别模型路径 (rec.rknn)
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target: 目标平台 ('rk3588', 'rk3566', 'rk3568', 'rv1103', 'rv1106', 'simulator')
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"""
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self.target = target
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# 初始化检测模型
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self.det_rknn = RKNN(verbose=True)
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ret = self.det_rknn.load_rknn(det_model_path)
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if ret != 0:
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print(f'Load detection RKNN model failed! Error code: {ret}')
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print('Please check if the model file exists and is valid')
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exit(ret)
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# 指定运行时平台
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print(f'Initializing detection model on target: {target}')
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ret = self.det_rknn.init_runtime(
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target=target,
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device_id=None,
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perf_debug=False,
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eval_mem=False,
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async_mode=False,
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core_mask=RKNN.NPU_CORE_AUTO # 自动选择NPU核心
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)
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if ret != 0:
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print(f'Init detection runtime failed! Error code: {ret}')
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print('Please check if you are running on the correct platform')
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if target == 'simulator':
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print('Note: Simulator mode requires different setup')
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exit(ret)
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print('Detection model loaded successfully')
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# 初始化识别模型
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self.rec_rknn = RKNN(verbose=True)
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ret = self.rec_rknn.load_rknn(rec_model_path)
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if ret != 0:
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print(f'Load recognition RKNN model failed! Error code: {ret}')
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print('Please check if the model file exists and is valid')
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exit(ret)
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# 指定运行时平台
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print(f'Initializing recognition model on target: {target}')
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ret = self.rec_rknn.init_runtime(
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target=target,
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device_id=None,
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perf_debug=False,
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eval_mem=False,
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async_mode=False,
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core_mask=RKNN.NPU_CORE_AUTO # 自动选择NPU核心
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)
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if ret != 0:
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print(f'Init recognition runtime failed! Error code: {ret}')
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print('Please check if you are running on the correct platform')
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exit(ret)
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print('Recognition model loaded successfully')
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# 字符集（根据您的模型调整）
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self.character = ['blank', '!', '"', '#', '$', '%', '&', "'", '(', ')', '*', '+',
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',', '-', '.', '/', '0', '1', '2', '3', '4', '5', '6', '7', '8',
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'9', ':', ';', '<', '=', '>', '?', '@', 'A', 'B', 'C', 'D', 'E',
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'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R',
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'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '[', '\\', ']', '^', '_',
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'`', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l',
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'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y',
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'z', '{', '|', '}', '~'] + [chr(i) for i in range(19968, 40870)] # 中文字符
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def preprocess_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 - 适配RKNN
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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.uint8) * 114
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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
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# RKNN通常需要uint8输入（如果量化时使用了uint8）
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# 如果您的模型使用float输入，请取消下面的注释并注释掉return语句
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# padded_img = padded_img.astype(np.float32)
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# padded_img = (padded_img / 255.0 - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]
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return padded_img, ratio, (top, left)
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def preprocess_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 - 适配RKNN
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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.uint8)
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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
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# RKNN通常需要uint8输入（如果量化时使用了uint8）
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# 如果您的模型使用float输入，请取消下面的注释并注释掉return语句
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# padded_image = padded_image.astype(np.float32)
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# padded_image = (padded_image / 255.0 - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]
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return padded_image
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def postprocess_det_boxes(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 or len(dt_boxes) == 0:
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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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try:
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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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except:
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# 如果shapely/pyclipper不可用，使用简单的膨胀
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return box
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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 = np.argmax(preds_prob, axis=1)
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preds_prob_max = 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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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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preds_text.append(self.character[idx])
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preds_conf.append(preds_prob_max[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 - RKNN版本
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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.preprocess_det(image)
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# RKNN推理
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start_time = time.time()
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outputs = self.det_rknn.inference(inputs=[det_img])
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det_time = time.time() - start_time
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print(f"Detection inference time: {det_time:.3f}s")
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# 获取输出 (通常是第一个输出)
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det_output = outputs[0]
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# 确保输出形状正确
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if len(det_output.shape) == 4:
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mask = det_output[0, 0, :, :]
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else:
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mask = det_output[0, :, :] if len(det_output.shape) == 3 else det_output
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# 后处理
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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.postprocess_det_boxes(boxes, ratio, padding_info, (ori_h, ori_w))
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return boxes, scores
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def recognize_text(self, image):
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"""
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文本识别 - RKNN版本
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"""
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# 预处理
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rec_img = self.preprocess_rec(image)
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# RKNN推理
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start_time = time.time()
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outputs = self.rec_rknn.inference(inputs=[rec_img])
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rec_time = time.time() - start_time
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print(f"Recognition inference time: {rec_time:.3f}s")
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# 获取输出
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rec_output = outputs[0]
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# 确保输出维度正确
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if len(rec_output.shape) == 3:
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rec_result = rec_output[0] # 移除batch维度
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else:
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rec_result = rec_output
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# 解码
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text, conf = self.decode_rec_result(rec_result)
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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 - RKNN版本
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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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print(f"Cannot read image: {image_path}")
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return []
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print(f"Processing image: {image.shape}")
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# 1. 文本检测
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dt_boxes, scores = self.detect_text(image)
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if dt_boxes is None or len(dt_boxes) == 0:
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print("No text boxes detected")
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return []
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print(f"Detected {len(dt_boxes)} text boxes")
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# 2. 文本识别
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ocr_results = []
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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 > 0.5: # 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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print(f"Box {i}: {text} (conf: {conf:.3f})")
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return ocr_results
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def release(self):
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"""
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释放RKNN资源
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"""
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if hasattr(self, 'det_rknn'):
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self.det_rknn.release()
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if hasattr(self, 'rec_rknn'):
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self.rec_rknn.release()
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# 使用示例
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def main():
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# 初始化OCR - 可以指定不同的target平台
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print("Initializing PaddleOCR RKNN...")
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# 根据您的硬件平台选择：
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# 'rk3588' - RK3588 (默认)
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# 'rk3566' - RK3566
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# 'rk3568' - RK3568
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# 'rv1103' - RV1103
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# 'rv1106' - RV1106
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# 'simulator' - 仿真器模式（用于开发调试）
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try:
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ocr = PaddleOCRRKNN('/home/admin-root/haotian/康达瑞贝斯机器狗/det_shape.rknn', '/home/admin-root/haotian/康达瑞贝斯机器狗/rec_shape.rknn', target='rk3588')
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except Exception as e:
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print(f"Failed to initialize RKNN models: {e}")
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print("Trying alternative initialization...")
|
||
# 如果失败，尝试其他选项
|
||
try:
|
||
ocr = PaddleOCRRKNN('det.rknn', 'rec.rknn', target='simulator')
|
||
except Exception as e2:
|
||
print(f"Alternative initialization also failed: {e2}")
|
||
return
|
||
|
||
try:
|
||
# 执行OCR
|
||
image_path = '/home/admin-root/haotian/康达瑞贝斯机器狗/data_image/001读表图片/3aee64cc1f90d93a5a45979f7b17cb4b_frame_001460.jpg'
|
||
|
||
# 检查图像文件是否存在
|
||
import os
|
||
if not os.path.exists(image_path):
|
||
print(f"Image file not found: {image_path}")
|
||
print("Please provide a valid image path")
|
||
return
|
||
|
||
results = ocr.ocr(image_path)
|
||
|
||
# 打印结果
|
||
print(f"\n=== OCR Results ({len(results)} items) ===")
|
||
for i, result in enumerate(results):
|
||
print(f"\n[{i+1}]")
|
||
print(f"Text: {result['text']}")
|
||
print(f"Confidence: {result['confidence']:.3f}")
|
||
print(f"Detection Score: {result['score']:.3f}")
|
||
print(f"Box: {result['box']}")
|
||
|
||
# 可视化结果
|
||
if results:
|
||
visualize_results(image_path, results)
|
||
else:
|
||
print("No text detected in the image")
|
||
|
||
except Exception as e:
|
||
print(f"OCR processing failed: {e}")
|
||
finally:
|
||
# 释放资源
|
||
ocr.release()
|
||
print("RKNN resources released")
|
||
|
||
def visualize_results(image_path, results):
|
||
"""
|
||
可视化OCR结果
|
||
"""
|
||
image = cv2.imread(image_path)
|
||
|
||
for i, result in enumerate(results):
|
||
box = np.array(result['box'], dtype=np.int32)
|
||
cv2.polylines(image, [box], True, (0, 255, 0), 2)
|
||
|
||
# 在框上方显示文本和序号
|
||
text_display = f"{i+1}: {result['text'][:20]}..."
|
||
cv2.putText(image, text_display,
|
||
(box[0][0], box[0][1] - 10),
|
||
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2)
|
||
|
||
# 保存结果图像
|
||
output_path = 'ocr_result_rknn.jpg'
|
||
cv2.imwrite(output_path, image)
|
||
print(f"Result image saved to: {output_path}")
|
||
|
||
if __name__ == "__main__":
|
||
main() |