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The DLL inferface & python sample code base on new version of libfacedetection ( https://github.com/ShiqiYu/libfacedetection )by Shiqi.Yu.

C++ 27.41% C 20.17% Python 52.42%
libfacedetection python shiqi

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pysample-for-shiqiyu's Issues

BGR or RGB?

in the orgal libfacedetect:
//!!! The input image must be a #BGR# one (three-channel) instead of RGB
// pResults = facedetect_cnn(pBuffer, (unsigned char*)(image.ptr(0)), image.cols, image.rows, (int)image.step);
and in this pySample-for-ShiqiYu:
//input image, it must be RGB (three-channel) image!原版要求BGR?
int * shiqi_fd(unsigned char * rgb_image_data, int width, int height, int step)
{
unsigned char * pBuffer = (unsigned char *)malloc(DETECT_BUFFER_SIZE);
return facedetect_cnn(pBuffer, rgb_image_data, width, height, step);
}
??

Ubuntu 下运行错误

你好,我在Ubuntu 16.04运行时,在载入dll的时候,出现了 invalid ELF header 错误
是只能在windows上运行吗?如果是,那怎么才能在Ubuntu上运行呢

谢谢

批量检测目录中的图片时 程序会异常中断

我在ex.py的基础上写了遍历目录取照片的代码,这时候会异常中断

# coding: utf-8
# 
# 程序功能 :使用 于仕琪 老师的lib,做人脸识别
#     重点 :调用 C++ 提供的 DLL 接口时,如何处理 指针 类型

import numpy as np
import cv2
from ctypes import *
import types
import sys
import os
import tqdm

Max_Faces = 256
Size_one_Face = 6 + 68 * 2
Size_FaceLandMarks = Size_one_Face * Max_Faces
class FaceResults(Structure): 
    _fields_ = [("face_num", c_int32), 
                ("datas", c_int16 * Size_FaceLandMarks)
               ]

for root, dirs, files in os.walk("E:\\43"):
    for name in files:
        img_path = os.path.join(root, name)
        print(img_path)
        img = cv2.imread(img_path)
        h,w = img.shape[:2]
        st = w*3

        p_img = img.ctypes.data_as(POINTER(c_ubyte))

        #dll = CDLL('./dlls/fd-shiqiyu.dll')    # old version
        dll = CDLL('./dlls/fd-shiqiyu_v2.dll')
        dll.shiqi_fd.restype = POINTER(FaceResults)

        p_results = dll.shiqi_fd(p_img,w,h,st) 
        face_num = p_results.contents.face_num


        for i in range(face_num):
            j =  Size_one_Face * i
            x = p_results.contents.datas[j]
            y = p_results.contents.datas[j+1]
            w = p_results.contents.datas[j+2]
            h = p_results.contents.datas[j+3]
            confidence = p_results.contents.datas[j+4]
            angle = p_results.contents.datas[j+5]

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