测试固定onnx输入形状模型
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@ -31,7 +31,7 @@ class PaddleOCRONNX:
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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 resize_norm_img_det(self, img, max_side_len=960):
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def resize_norm_img_det(self, img, max_side_len=640):
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"""
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检测模型的图像预处理
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"""
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@ -322,7 +322,7 @@ class PaddleOCRONNX:
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# 使用示例
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def main():
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# 初始化OCR
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# 初始化OCR, _shape.onnx 是固定了输入形状的onnx模型, 不支持不是这个形状的输入.
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ocr = PaddleOCRONNX('/home/admin-root/haotian/康达瑞贝斯机器狗/det.onnx', '/home/admin-root/haotian/康达瑞贝斯机器狗/rec.onnx')
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# 执行OCR
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@ -1,58 +0,0 @@
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from rknn.api import RKNN
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import cv2
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import numpy as np
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# 初始化 RKNN
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rknn = RKNN()
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# 配置参数（关键！）
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rknn.config(
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target_platform="rk3588", # 根据实际芯片型号修改
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mean_values=[[123.675, 116.28, 103.53]], # 默认输入是255, 使用这些值归一化到0-1
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std_values=[[58.395, 57.12, 57.375]],
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quant_img_RGB2BGR=True,
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optimization_level=3, # 最高优化级别
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)
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# 加载 ONNX
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ret = rknn.load_onnx(model="/home/admin-root/haotian/康达瑞贝斯机器狗/det_shape.onnx")
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assert ret == 0, "加载 ONNX 失败！"
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# 转换模型
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ret = rknn.build(
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do_quantization=False, # 启用量化
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# dataset="dataset.txt", # 校准数据路径
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)
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assert ret == 0, "转换 RKNN 失败！"
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# 导出 RKNN
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ret = rknn.export_rknn("/home/admin-root/haotian/康达瑞贝斯机器狗/det_shape.rknn")
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assert ret == 0, "导出 RKNN 失败！"
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# Set inputs
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img = cv2.imread('/home/admin-root/haotian/rk3588/pytorch模型转rknn/images/bus.jpg')
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img.resize((3, 640, 640))
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img = np.expand_dims(img, 0)
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# Init runtime environment
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print('--> Init runtime environment')
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ret = rknn.init_runtime()
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if ret != 0:
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print('Init runtime environment failed!')
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exit(ret)
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print('done')
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# Inference
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print('--> Running model')
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outputs = rknn.inference(inputs=[img], data_format=['nchw'])
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np.save('./tflite_mobilenet_v1_0.npy', outputs[0])
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print(len(outputs))
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print('done')
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rknn.release()
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383
008测试PadleCOR_ONNX固定输入形状模型全流程.py
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383
008测试PadleCOR_ONNX固定输入形状模型全流程.py
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@ -0,0 +1,383 @@
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import cv2
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import numpy as np
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import onnxruntime as ort
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from PIL import Image, ImageDraw, ImageFont
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import math
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class PaddleOCRONNX:
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def __init__(self, det_model_path, rec_model_path):
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"""
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初始化ONNX推理器
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Args:
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det_model_path: 检测模型路径 (det.onnx)
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rec_model_path: 识别模型路径 (rec.onnx)
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"""
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# 初始化检测模型
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self.det_session = ort.InferenceSession(det_model_path)
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self.det_input_name = self.det_session.get_inputs()[0].name
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# 初始化识别模型
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self.rec_session = ort.InferenceSession(rec_model_path)
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self.rec_input_name = self.rec_session.get_inputs()[0].name
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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 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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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 = 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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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[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_session.run(None, {self.det_input_name: det_img})[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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return boxes, scores
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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_session.run(None, {self.rec_input_name: rec_img})[0]
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# 解码
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text, conf = self.decode_rec_result(rec_output[0])
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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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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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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
|
||||
text, conf = self.recognize_text(crop_img)
|
||||
|
||||
if conf > 0.5: # 置信度过滤
|
||||
ocr_results.append({
|
||||
'text': text,
|
||||
'confidence': conf,
|
||||
'box': box.tolist(),
|
||||
'score': scores[i] if i < len(scores) else 0.0
|
||||
})
|
||||
|
||||
return ocr_results
|
||||
|
||||
|
||||
# 使用示例
|
||||
def main():
|
||||
# 初始化OCR
|
||||
ocr = PaddleOCRONNX('/home/admin-root/haotian/康达瑞贝斯机器狗/det_shape.onnx', '/home/admin-root/haotian/康达瑞贝斯机器狗/rec_shape.onnx')
|
||||
|
||||
# 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'
|
||||
results = ocr.ocr(image_path)
|
||||
|
||||
# 打印结果
|
||||
for result in results:
|
||||
print(f"文本: {result['text']}")
|
||||
print(f"置信度: {result['confidence']:.3f}")
|
||||
print(f"检测得分: {result['score']:.3f}")
|
||||
print(f"坐标: {result['box']}")
|
||||
print("-" * 50)
|
||||
|
||||
# 可视化结果
|
||||
visualize_results(image_path, results)
|
||||
|
||||
def visualize_results(image_path, results):
|
||||
"""
|
||||
可视化OCR结果
|
||||
"""
|
||||
image = cv2.imread(image_path)
|
||||
|
||||
for result in 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
|
||||
cv2.putText(image, result['text'],
|
||||
(box[0][0], box[0][1] - 10),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2)
|
||||
|
||||
cv2.imwrite('result_shape.jpg', image)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in New Issue
Block a user