评估node.js中扫描图像的复选框

Question

我想评估复选框是否从扫描图像中选中。为此，我找到了节点模块node-dv和node-fv。但是，当安装这个我得到了以下错误在Mac上。

../deps/opencv/modules/core/src/arithm1.cpp:444:51: error: constant expression evaluates to 4294967295 which cannot be narrowed to type 'int' [-Wc++11-narrowing] 
static int CV_DECL_ALIGNED(16) v64f_absmask[] = { 0xffffffff, 0x7fffffff, 0xffffffff, 0x7fffffff }; 
                ^~~~~~~~~~ 
../deps/opencv/modules/core/src/arithm1.cpp:444:51: note: insert an explicit cast to silence this issue 
static int CV_DECL_ALIGNED(16) v64f_absmask[] = { 0xffffffff, 0x7fffffff, 0xffffffff, 0x7fffffff }; 
                ^~~~~~~~~~ 
                static_cast<int>() 
../deps/opencv/modules/core/src/arithm1.cpp:444:75: error: constant expression evaluates to 4294967295 which cannot be narrowed to type 'int' [-Wc++11-narrowing] 
static int CV_DECL_ALIGNED(16) v64f_absmask[] = { 0xffffffff, 0x7fffffff, 0xffffffff, 0x7fffffff }; 
                      ^~~~~~~~~~ 
../deps/opencv/modules/core/src/arithm1.cpp:444:75: note: insert an explicit cast to silence this issue 
static int CV_DECL_ALIGNED(16) v64f_absmask[] = { 0xffffffff, 0x7fffffff, 0xffffffff, 0x7fffffff }; 
                      ^~~~~~~~~~ 
                      static_cast<int>() 
2 errors generated. 
make: *** [Release/obj.target/libopencv/deps/opencv/modules/core/src/arithm1.o] Error 1 
gyp ERR! build error 
gyp ERR! stack Error: `make` failed with exit code: 2 
gyp ERR! stack  at ChildProcess.onExit (/Users/entapzian/.nvm/versions/node/v4.3.1/lib/node_modules/npm/node_modules/node-gyp/lib/build.js:270:23) 
gyp ERR! stack  at emitTwo (events.js:87:13) 
gyp ERR! stack  at ChildProcess.emit (events.js:172:7) 
gyp ERR! stack  at Process.ChildProcess._handle.onexit (internal/child_process.js:200:12) 

上述依赖性是我的问题的最佳解决方案吗？如果不是，请给我一个好的解决方案。

Answer 1

对不起，延迟的答案，我昨天和今天真的很忙。这是一个示例，它捕获图像的预定义区域，并确定复选框是否填充或为空。这只是一个起点，可能会大大改善，但如果扫描的图像质量不错，它应该可以工作。

第一步是获取图像的像素。接下来，通过根据模式抓取它们，可以在图像中包含复选框的区域。最后，通过比较图像中该区域的平均亮度与未检查框的基线亮度来评估复选框是否被选中。

我推荐使用get-pixels Node.js包来获取图像像素。

这里有一个例子，你或许可以调整，以满足您的需求：

var get_pixels = require(‘get-pixels’); 
var image_uri = 'path_to_image'; 

get_pixels(image_uri, process_image); 

var pattern_width = 800, // Width of your pattern image 
    pattern_height = 1100; // Height of your pattern image 

// The pattern image doesn't need to be loaded, you just need to use its dimensions to reference the checkbox regions below 
// This is only for scaling purposes in the event that the scanned image is of a higher or lower resolution than what you used as a pattern. 

var checkboxes = [ 
    {x1: 10, y1: 10, x2: 30, y2: 30}, // Top left and bottom right corners of the region containing the checkbox 
    {x1: 10, y1: 60, x2: 30, y2: 80} 
]; 

// You'll need to get these by running this on an unchecked form and logging out the adjusted_average of the regions 
var baseline_average = ??, // The average brightness of an unchecked region 
    darkness_tolerance = ??; // The offset below which the box is still considered unchecked 

function process_image(err, pixels) { 

    if (!err) { 

     var regions = get_regions(pixels); 

     var checkbox_states = evaluate_regions(regions); 

     // Whatever you want to do with the determined states 

    }else{ 
     console.log(err); 
     return; 
    } 

} 

function get_regions(pixels) { 

    var regions = [], // Array to hold the pixel data from selected regions 
     img_width = pixels.shape[0], // Get the width of the image being processed 
     img_height = pixels.shape[1], // Get the height 
     scale_x = img_width/pattern_width, // Get the width scale difference between pattern and image (for different resolution scans) 
     scale_y = img_height/pattern_height; // Get the height scale difference 

    for (var i = 0; i < checkboxes.length; i++) { 

     var start_x = Math.round(checkboxes[i].x1 * scale_x), 
      start_y = Math.round(checkboxes[i].y1 * scale_y), 
      end_x = Math.round(checkboxes[i].x2 * scale_x), 
      end_y = Math.round(checkboxes[i].y2 * scale_y), 
      region = []; 

     for (var y = start_y; y <= end_y; y++) { 
      for (var x = start_x; y <= end_x; x++) { 
       region.push(
        pixels.get(x, y, 0), // Red channel 
        pixels.get(x, y, 1), // Green channel 
        pixels.get(x, y, 2), // Blue channel 
        pixels.get(x, y, 3) // Alpha channel 
       ); 
      } 
     } 

     regions.push(region); 

    } 

    return regions; 

} 

function evaluate_regions(regions) { 

    var states = []; 

    for (var i = 0; i < regions.length; i++) { 

     var brightest_value = 0, 
      darkest_value = 255, 
      total = 0; 

     for (var j = 0; j < regions[i].length; j+=4) { 

      var brightness = (regions[i][j] + regions[i][j + 1] + regions[i][j + 2])/3; // Pixel brightness 
      if (brightness > brightest_value) brightest_value = brightness; 
      if (brightness < darkest_value) darkest_value = brightness; 
      total += brightness; 

     } 

     var adjusted_average = (total/(regions[i].length/4)) - darkest_value; // Adjust contrast 
     var checked = baseline_average - adjusted_average > darkness_tolerance ? true : false; 

     states.push(checked); 

    } 

    return states; 

}