up主可以出一个ios demo吗 about ncnn HOT 12 CLOSED

    int r0 = net.load_param([paramPath UTF8String]);
    int r1 = net.load_model([binPath UTF8String]);

#include <algorithm>
#include <functional>
#include <vector>

#import <Foundation/Foundation.h>
#import <UIKit/UIKit.h>
#import <UIKit/UIImage.h>

// ncnn
#include "net.h"

static int print_topk(const std::vector<float>& cls_scores, int topk)
{
    // partial sort topk with index
    int size = cls_scores.size();
    std::vector< std::pair<float, int> > vec;
    vec.resize(size);
    for (int i=0; i<size; i++)
    {
        vec[i] = std::make_pair(cls_scores[i], i);
    }

    std::partial_sort(vec.begin(), vec.begin() + topk, vec.end(),
                      std::greater< std::pair<float, int> >());

    // print topk and score
    for (int i=0; i<topk; i++)
    {
        float score = vec[i].first;
        int index = vec[i].second;
        fprintf(stderr, "%d = %f\n", index, score);
    }

    return 0;
}

int main(int argc, char** argv)
{
    const char* imagepath = argv[1];

    // load image
    UIImage* image = 0;
    {
        NSString* imagePath = [NSString stringWithUTF8String:imagepath];
        image = [UIImage imageWithContentsOfFile:imagePath];
    }

    // get rgba pixels from image
    int w = image.size.width;
    int h = image.size.height;
    fprintf(stderr, "%d x %d\n", w, h);
    unsigned char* rgba = new unsigned char[w*h*4];
    {
        CGColorSpaceRef colorSpace = CGImageGetColorSpace(image.CGImage);
        CGContextRef contextRef = CGBitmapContextCreate(rgba, w, h, 8, w*4,
                                                        colorSpace,
                                                        kCGImageAlphaNoneSkipLast | kCGBitmapByteOrderDefault);

        CGContextDrawImage(contextRef, CGRectMake(0, 0, w, h), image.CGImage);
        CGContextRelease(contextRef);
    }

    // init net
    ncnn::Net net;
    {
        int r0 = net.load_param("squeezenet_v1.1.param");
        int r1 = net.load_model("squeezenet_v1.1.bin");
        fprintf(stderr, "net load %d %d\n", r0, r1);
    }

    // run forward
    ncnn::Mat out;
    {
        ncnn::Extractor ex = net.create_extractor();
        ex.set_light_mode(true);
        ex.set_num_threads(2);

        ncnn::Mat in = ncnn::Mat::from_pixels_resize(rgba, ncnn::Mat::PIXEL_RGBA2BGR, w, h, 227, 227);
        
        const float mean_vals[3] = {104.f, 117.f, 123.f};
        in.substract_mean_normalize(mean_vals, 0);

        ex.input("data", in);

        ex.extract("prob", out);
    }

    // get prob
    std::vector<float> cls_scores;
    {
        cls_scores.resize(out.c);
        for (int j=0; j<out.c; j++)
        {
            const float* prob = out.data + out.cstep * j;
            cls_scores[j] = prob[0];
        }
    }

    // print topk
    print_topk(cls_scores, 3);

    // clean
    delete[] rgba;

    return 0;
}

        for (int j=0; j<out.c; j++)
        {
            cls_scores[j] = out[j];
        }

    CGContextDrawImage(contextRef, CGRectMake(0, 0, w, h), image.CGImage);
    CGContextRelease(contextRef);
}