GIF或PNG图像上的颜色检测

Question

我们想知道是否可以做附加图片之类的事情。

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。

这是什么想法？

我们想要发现＆＃34;重＆＃34;为我们的访客带来风暴，并用方框或其他东西突出显示它们。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？

附上图像作为我们用Photoshop绘制的例子：

我们希望有人可以帮助我们，这样我们就可以开始做点什么了！

Answer 1

这样做的正确方法可能是使用某种 Blob分析来提取红色区域并围绕它们做边界框。这并不难，但在开始这种方法时，我可以用一行ImageMagick做一些更简单但更有效的事情。它是免费的，可在命令行和PHP，Perl，Python和其他绑定中使用。

所以，我打算将所有红色区域转换为白色，将所有非红色区域转换为黑色，然后运行 Blob分析并在白色斑点周围绘制红色边界框。但是在路上，我想到可能会使图像的非红色区域半透明，然后红色区域完全透明，所以注意力集中在红色的东西上，所有其他的东西都很苍白。这可以在单个ImageMagick命令中完成，如下所示：

convert http://i.stack.imgur.com/qqein.png               \
    \( +clone                                            \
       -fuzz 30%                                         \
       -fill "#222222" +opaque red                       \ 
       -fill "#ffffff" -opaque red -colorspace gray \)   \
    -compose copy-opacity -composite out.png

结果如下：

如果您喜欢这种方法，数字显然可以调整......

Answer 2

我使用我在C中编写的一些Connected Component Analysis软件进行了另一次尝试。可以在任何OS X / Linux / Windows机器上编译。

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如下所示：

cca计划的部分输出

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：

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;
}

Answer 3

我会使用-fx运算符隔离红色单元格。

convert source.png -fx '(p.r > p.b && p.r > 0.9) ? p : 0' a_RED.png

p.r > p.b删除白色，p.r > 0.9检查当前像素的阈值0.9。

这种方法需要一些额外的CPU时间，但确实能让您调整严重程度。

Answer 4

我刚刚发现ImageMagick可以执行 连接组件分析 ，因此我现在可以提供更简单的解决方案，而不依赖于我的C编码。< / p>

这是：

#!/bin/bash

draw=$(convert http://i.stack.imgur.com/qqein.png  \
   -fuzz 50%                                       \
   -fill white +opaque red                         \
   -fill black -opaque red                         \
   -colorspace gray                                \
   -define connected-components:verbose=true       \
   -define connected-components:area-threshold=100 \
   -connected-components 8                         \
   -auto-level baddies.png | \
   awk 'BEGIN{command=""}
        /\+0\+0/||/id:/{next}
        {
          geom=$2
          gsub(/x/," ",geom)
          gsub(/+/," ",geom)
          split(geom,a," ")
          d=sprintf("-draw \x27rectangle %d,%d %d,%d\x27 ",a[3],a[4],a[3]+a[1],a[4]+a[2])
          command = command d
          #printf "%d,%d %d,%d\n",a[3],a[4],a[3]+a[1],a[4]+a[2]
        }
        END{print command}')

eval convert http://i.stack.imgur.com/qqein.png -fill none -strokewidth 2 -stroke red $draw out.png

以下是生成的图像：

以下是文件baddies.png

中标记的对象

以下是有关代码的一些注释......

-fuzz 50％允许检测到的红色阴影有一定程度的变化

- 填充白色+不透明红色 - 将所有红色像素更改为白色

- 填充黑色 - 红色 - 将所有非红色像素更改为黑色

-define connected-components：verbose = true - 导致诊断输出，这样我就可以得到它找到的边界框

-define connected-components：area-threshold = 100 - 说我只对100像素或更大的红色区域感兴趣

-connected-components 8 - 说红点可以连接到它们的8个邻居（即对角连接，而不是方形连接）

-auto-level baddies.png - 对比拉伸标记的风暴对象并将其保存在名为baddies.png

的文件中

awk内容就像我的其他答案中的awk内容一样。

只是让其他人在第一阶段看到ImageMagick的连接组件分析的输出，它看起来像这样：

Objects (id: bounding-box centroid area mean-color):
  0: 1020x563+0+0 507.6,281.2 567516 gray(253)
  495: 53x36+377+259 405.3,273.3 1040 gray(0)
  391: 101x35+658+181 699.9,195.6 984 gray(0)
  515: 13x77+976+281 982.5,321.4 863 gray(0)
  581: 35x37+624+376 641.9,397.1 740 gray(0)
  439: 33x45+340+223 352.0,249.2 643 gray(1)
  558: 47x32+705+320 727.2,334.8 641 gray(1)
  353: 25x30+822+143 834.3,156.1 422 gray(0)
  350: 27x31+898+138 911.4,152.7 402 gray(0)
  343: 29x18+930+125 944.6,132.2 283 gray(0)
  392: 45x12+759+186 783.0,193.0 276 gray(0)
  663: 24x15+357+485 367.3,493.4 192 gray(0)
  531: 98x58+169+297 209.4,336.2 152 gray(0)
  377: 20x9+753+167 762.6,170.6 106 gray(0)

最终convert命令的参数如下所示：

convert http://i.stack.imgur.com/qqein.png -fill none -strokewidth 2 -stroke red \
-draw 'rectangle 377,259 430,295' \
-draw 'rectangle 658,181 759,216' \
-draw 'rectangle 976,281 989,358' \
-draw 'rectangle 624,376 659,413' \
-draw 'rectangle 340,223 373,268' \
-draw 'rectangle 705,320 752,352' \
-draw 'rectangle 822,143 847,173' \
-draw 'rectangle 898,138 925,169' \
-draw 'rectangle 930,125 959,143' \
-draw 'rectangle 759,186 804,198' \
-draw 'rectangle 357,485 381,500' \
-draw 'rectangle 169,297 267,355' \
-draw 'rectangle 753,167 773,176' out.png