python 32位调用ncnn模型源码

import time from ctypes import * import cv2 import numpy as np import numpy.ctypeslib as npct import os class Detector(): def __init__(self, par, bin, dll_path): self.ncnn = CDLL(dll_path) self.ncnn.Detect_1.argtypes = [POINTER(c_ubyte), c_int, c_int, c_int, c_int, npct.ndpointer(dtype=np.float32, ndim=2, shape=(50, 6), flags="C_CONTIGUOUS")] self.ncnn.Init.argtypes = [c_void_p, c_void_p] self.ncnn.Init(par, bin) def predict(self, img): rows, cols = img.shape[0], img.shape[1] res_arr = np.zeros((50, 6), dtype=np.float32) self.ncnn.Detect_1(img.ctypes.data_as(POINTER(c_ubyte)), c_int(rows), c_int(cols), 352, 4, res_arr) self.bbox_array = res_arr[~(res_arr == 0).all(1)] return self.bbox_array def free(self): self.ncnn.ncnn_clear() def visualize(img, bbox_array): for temp in bbox_array: bbox = [temp[0], temp[1], temp[2], temp[3]] # xywh clas = int(temp[4]) score = temp[5] cv2.rectangle(img, (int(temp[0]), int(temp[1])), (int(temp[2]), int(temp[3])), (105, 237, 249), 2) img = cv2.putText(img, "class:" + str(clas) + " " + str(round(score, 2)), (int(temp[0]), int(temp[1]) - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (105, 237, 249), 1) return img p = os.getcwd() det = Detector(par=str.encode(p) + b"\\FastestDet.param", bin=str.encode(p) + b"\\FastestDet.bin", dll_path=p + "\\fastDet.dll") # b'' is needed while True: img = cv2.imread("3.jpg") t1 = time.time() result = det.predict(img) img = visualize(img, result) print(time.time() - t1) cv2.imshow("img", img) cv2.waitKey(1) # det.free()