All code is highly based on Ildoo Kim's code (https://github.com/ildoonet/tf-openpose) and derived from the OpenPose Library (https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/LICENSE)
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def plates(stack, i, num_used, N, P, K): | |
# Brute force: O(K^N) | |
if i >= N: | |
return -1 | |
current_total = -1 | |
for n in range(K + 1): | |
if num_used + n > P: | |
break | |
elif num_used + n == P: | |
current_total = max(current_total, sum(stack[i][:n])) |
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#include <cudnn.h> | |
#include <iostream> | |
int main(int argc, char** argv) { | |
std::cout << "CUDNN_VERSION: " << CUDNN_VERSION << std::endl; | |
return 0; | |
} |
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package main | |
import ( | |
"fmt" | |
"net/http" | |
) | |
type String string | |
type Struct struct { |
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# http://stackoverflow.com/questions/24685436/efficient-way-to-cluster-colors-using-k-nearest | |
import numpy as np | |
import cv2 | |
def nearest(i, j, src, knn, colors): | |
sample = np.reshape(src, (-1, 3)).astype(np.float32) | |
retval, result, neighbors, dist = knn.find_nearest(sample, 1) | |
return colors[result[0, 0]] |
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#include <cstdio> | |
#include <string> | |
int n = 4; | |
int k = 15; | |
int a[] = {1, 2, 4, 7}; | |
bool dfs(int i, int sum) | |
{ | |
printf("i == %d, sum == %d\n", i, sum); |
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#-*- coding: utf-8 -*- | |
import cv2 | |
from collections import defaultdict | |
class ImageFlip(object): | |
def __init__(self): | |
self._observers = [] |
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#-*- coding: utf-8 -*- | |
from pit import Pit | |
import requests | |
import getpass | |
import json | |
HTTPUnauthorized = 401 | |
HTTPCreated = 201 |
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#include <opencv/cv.h> | |
#include <opencv/highgui.h> | |
#include <glut.h> | |
static void display(void) | |
{ | |
cv::Mat img = cv::imread("test.jpg"); | |
cv::flip(img, img, 0); | |
cv::cvtColor(img, img, CV_BGR2RGB); |
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