GIF或PNG图像上的颜色检测 [英] Color detection on GIF or PNG image
本文介绍了GIF或PNG图像上的颜色检测的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我们想知道是否可以做一些类似附图的事情。
We were wondering if it was possible to do something like the attached pictures.
我们的网站上有一个实时天气雷达，投影在谷歌地图页面上更新周期为5分钟。
We have a live weather radar on our website, projected on a google maps page with an update cycle of 5 minutes.
有什么想法？
我们希望为访问者检测沉重风暴并突出显示它们用方形盒子或东西。如果有可能我们想用PHP制作这个系统。我认为最好的方法是检测颜色或什么？
We want to detect the "heavy" storms for our visitors and highlight them with a square box or something. If it is possible we want to make this system in PHP. I think the best way is to detect colors or something?
附上图像作为我们用Photoshop绘制的例子：
Attached the images as example we have drawn with Photoshop:
我们希望有人可以帮助我们，这样我们就可以开始了！
We hope someone can help us out so we can started with something!
推荐答案
我使用了一些连接组件分析软件进行了另一次尝试C.它很容易在任何OS X / Linux / Windows机器上编译。
I had another attempt at this, using some Connected Component Analysis software I wrote in C. It is readily compiled on any OS X/Linux/Windows machine.
所以，这是脚本：
#!/bin/bash
# Make red areas white and all else black for blob analysis
convert http://i.stack.imgur.com/qqein.png \
-fuzz 50% \
-fill white +opaque red \
-fill black -opaque red -colorspace gray -negate -depth 16 weather.pgm
# Run Connected Component Analysis to find white blobs and their areas and bounding boxes
./cca < weather.pgm > /dev/null 2> info.txt
# Find blobs with more than 100 pixels
while read a b ;do
draw="$draw -draw \"rectangle $a $b\" "
done < <(awk '/Area/{area=$5+0;if(area>100)print $7,$8}' info.txt)
# Now draw the rectangles on top of the source image
eval convert http://i.stack.imgur.com/qqein.png -strokewidth 2 -stroke red -fill none "$draw" result.png
文件 weather.pgm 出现如下：
部分输出 cca 程序
Partial output of cca program
DEBUG: New blob (1) started at [1][510]
INFO: Blob 1, Area: 8, Bounds: 510,1 510,8
DEBUG: New blob (2) started at [1][554]
INFO: Blob 2, Area: 6, Bounds: 554,1 559,1
DEBUG: New blob (3) started at [2][550]
INFO: Blob 3, Area: 1, Bounds: 550,2 550,2
DEBUG: New blob (4) started at [3][524]
INFO: Blob 4, Area: 1, Bounds: 524,3 524,3
DEBUG: New blob (5) started at [3][549]
INFO: Blob 5, Area: 1, Bounds: 549,3 549,3
DEBUG: New blob (6) started at [3][564]
INFO: Blob 6, Area: 1, Bounds: 564,3 564,3
DEBUG: New blob (7) started at [4][548]
INFO: Blob 7, Area: 1, Bounds: 548,4 548,4
DEBUG: New blob (8) started at [5][526]
INFO: Blob 8, Area: 1, Bounds: 526,5 526,5
DEBUG: New blob (9) started at [5][546]
脚本中的最终 convert 命令被调用如下：
The final convert command in the script gets called like this:
convert http://i.stack.imgur.com/qqein.png -strokewidth 2 -stroke red -fill none \
-draw 'rectangle 930,125 958,142' -draw 'rectangle 898,138 924,168' \
-draw 'rectangle 822,143 846,172' -draw 'rectangle 753,167 772,175' \
-draw 'rectangle 658,181 758,215' -draw 'rectangle 759,186 803,197' \
-draw 'rectangle 340,223 372,267' -draw 'rectangle 377,259 429,294' \
-draw 'rectangle 977,281 988,357' -draw 'rectangle 705,321 751,351' \
-draw 'rectangle 624,376 658,412' -draw 'rectangle 357,485 380,499' result.png
结果是这样的：
cca.c 程序是这样的：
/*******************************************************************************
File: cca.c
Author: Mark Setchell
Description:
Connected Components Analyser and Labeller - see algorithm at
http://en.m.wikipedia.org/wiki/Connected-component_labeling#One-pass_version
Algorithm
=========
1. Start from the first pixel in the image. Set "curlab" (short for "current label") to 1. Go to (2).
2. If this pixel is a foreground pixel and it is not already labelled, then give it the label "curlab" and add it as the first element in a queue, then go to (3). If it is a background pixel, then repeat (2) for the next pixel in the image.
3. Pop out an element from the queue, and look at its neighbours (based on any type of connectivity). If a neighbour is a foreground pixel and is not already labelled, give it the "curlab" label and add it to the queue. Repeat (3) until there are no more elements in the queue.
4. Go to (2) for the next pixel in the image and increment "curlab" by 1.
CurrentLabel=1
for all pixels in image
if this is a foreground pixel
if this pixel is not already labelled
label this pixel with Currentlabel
add this pixel to queue
while there are items in the queue
pop item from queue
for all 4-connected or 8-connected neighbours of this item
if neighbour is foreground and is not already labelled
label this neighbour with Currentlabel
add this neighbour to the queue
endif
endfor
endwhile
increment Currentlabel
endif
else
label as background in output image
endif
endfor
Usage
=====
Usage: cca [-c 4|8] < Binarized16BitPGMFile > Binarized16BitPGMFile
where "-c" specifies whether pixels must be 4- or 8-connected to be considered
as parts of same object. By default 4-connectivity is assumed.
Files can be prepared for this program with ImageMagick as follows:
convert YourImage.[jpg|bmp|png|tif] \
-colorspace gray \
-threshold 50% \
-depth 16 \
[-negate] \
FileForAnalysis.pgm
This program expects the background pixels to be black and the objects to be
white. If your image is inverted relative to this, use the "-negate" option.
On OSX, run and view results with ImageMagick like this:
cca < test1.pgm | convert PGM:- -auto-level a.jpg && open a.jpg
*******************************************************************************/
#include <stdio.h>
#include <stdlib.h>
#include <stdint.h>
#include <unistd.h>
#include <string.h>
#define DEFAULT_CONNECTIVITY 4
void Usage() {
printf("Usage: cca [-c 4|8] < InputImage.pgm > OutputImage.pgm\n");
exit(EXIT_FAILURE);
}
int pixelIsForegroundAndUnlabelled(uint16_t **iIm,uint16_t **oIm,int height,int width,int row,int col){
if((row<0)||(row>=height)||(col<0)||(col>=width)) return 0;
return (iIm[row][col]!=0) && (oIm[row][col]==0);
}
// Stuff needed for queue
int count=0;
struct node
{
int x,y;
struct node *p;
} *top,*tmp;
void push(int row,int col){
if(top==NULL)
{
top =(struct node *)malloc(sizeof(struct node));
top->p = NULL;
top->x = row;
top->y = col;
}
else
{
tmp =(struct node *)malloc(sizeof(struct node));
tmp->p = top;
tmp->x = row;
tmp->y = col;
top = tmp;
}
count++;
}
void pop(int *x,int *y){
tmp = top;
tmp = tmp->p;
*x = top->x;
*y = top->y;
free(top);
top = tmp;
count--;
}
int main (int argc, char ** argv)
{
int i,reqcon;
int connectivity=DEFAULT_CONNECTIVITY;
uint16_t currentlabel=1;
while (1) {
char c;
c = getopt (argc, argv, "c:");
if (c == -1) {
break;
}
switch (c) {
case 'c':
reqcon=atoi(optarg);
/* Permitted connectivity is 4 or 8 */
if((reqcon!=4)&&(reqcon!=8)){
Usage();
}
connectivity=reqcon;
break;
case '?':
default:
Usage();
}
}
int width,height,max;
int row,col;
/* Check it is P5 type */
char type[128];
fscanf(stdin,"%s",type);
if (strncmp(type,"P5",2)!=0) {
fprintf(stderr, "ERROR: The input data is not binary PGM, i.e. not type P5\n");
exit(EXIT_FAILURE);
}
fscanf(stdin,"%d %d\n",&width,&height);
fscanf(stdin,"%d",&max);
fgetc(stdin);
/* Check 16-bit */
if (max != 65535){
fprintf(stderr, "ERROR: The input data is not 16-bit\n");
exit(EXIT_FAILURE);
}
// Allocate space for input & output image & read input image
uint16_t **iIm; // pixels of input image
uint16_t **oIm; // pixels of output image
iIm = (uint16_t**)malloc(height * sizeof(uint16_t *));
oIm = (uint16_t**)malloc(height * sizeof(uint16_t *));
if((iIm==NULL)||(oIm==NULL)){
fprintf(stderr, "ERROR: out of memory\n");
exit(EXIT_FAILURE);
}
for(i=0;i<height;i++)
{
iIm[i] = (uint16_t*) malloc(width*sizeof(uint16_t));
oIm[i] = (uint16_t*) calloc(width,sizeof(uint16_t));
if((iIm[i]==NULL)||(oIm[i]==NULL)){
fprintf(stderr, "ERROR: Unable allocate memory\n");
exit(EXIT_FAILURE);
}
// Read in one row of image
if(fread(iIm[i],sizeof(uint16_t),width,stdin)!=width){
fprintf(stderr,"ERROR: Reading input file\n");
exit(EXIT_FAILURE);
}
}
// Start of algorithm
for(row=0;row<height;row++){
for(col=0;col<width;col++){
// If this is a foreground pixel that is not yet labelled
if(pixelIsForegroundAndUnlabelled(iIm,oIm,height,width,row,col)){
fprintf(stderr,"DEBUG: New blob (%d) started at [%d][%d]\n",currentlabel,row,col);
int ThisBlobPixelCount=1;
int ThisBlobrmin=row;
int ThisBlobrmax=row;
int ThisBlobcmin=col;
int ThisBlobcmax=col;
oIm[row][col]=currentlabel; // Label the pixel
push(row,col); // Put it on stack
while(count>0){ // While there are items on stack
int tr,tc;
pop(&tr,&tc); // Pop x,y of queued pixel from stack
// Work out who the neighbours are
int neigh[][2]={{tr-1,tc},{tr+1,tc},{tr,tc-1},{tr,tc+1}};
if(connectivity==8){
neigh[4][0]=tr-1; neigh[4][3]=tc-1;
neigh[5][0]=tr+1; neigh[5][4]=tc+1;
neigh[6][0]=tr+1; neigh[6][5]=tc-1;
neigh[7][0]=tr-1; neigh[7][6]=tc+1;
}
// Process all neighbours
for(i=0;i<connectivity;i++){
int nr=neigh[i][0];
int nc=neigh[i][7];
if(pixelIsForegroundAndUnlabelled(iIm,oIm,height,width,nr,nc)){
oIm[nr][nc]=currentlabel;
push(nr,nc);
ThisBlobPixelCount++;
if(nr<ThisBlobrmin)ThisBlobrmin=nr;
if(nr>ThisBlobrmax)ThisBlobrmax=nr;
if(nc<ThisBlobcmin)ThisBlobcmin=nc;
if(nc>ThisBlobcmax)ThisBlobcmax=nc;
}
}
}
// Output statistics/info about the blob we found
fprintf(stderr,"INFO: Blob %d, Area: %d, Bounds: %d,%d %d,%d\n",currentlabel,ThisBlobPixelCount,ThisBlobcmin,ThisBlobrmin,ThisBlobcmax,ThisBlobrmax);
currentlabel++; // Increment label as we have found all parts of this blob
}
}
}
// Write output image
fprintf(stdout,"P5\n%d %d\n65535\n",width,height);
for(row=0;row<height;row++){
if(fwrite(oIm[row],sizeof(uint16_t),width,stdout)!=width){
fprintf(stderr,"ERROR: Writing output file\n");
exit(EXIT_FAILURE);
}
}
return EXIT_SUCCESS;
}
这篇关于GIF或PNG图像上的颜色检测的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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