3 ms·

Strange, I can't repeat the results on various folders I have. I wrote a quick little (slow) Python script to compute a blended photo: https://gist.github.com/

Strange, I can't repeat the results on various folders I have. I wrote a quick little (slow) Python script to compute a blended photo: https://gist.github.com/williame/92c7179d0963553d605f https://gist.github.com/williame/92c7179d0963553d605f If a folder has some theme, such as a folder I have of a boat regatta, I get an average picture that looks as you'd expect. So the code seems to average correctly. If I run it on a bigger assemblage of photos, I tend to get a uniform gray though. What am I doing wrong? (I encourage you to try yourself!)

sp332 12y ago
Maybe you should include lots of faces? And check that you're doing sRGB gamma math correctly. http://http.developer.nvidia.com/GPUGems3/gpugems3_ch24.html http://http.developer.nvidia.com/GPUGems3/gpugems3_ch24.html Edit: I'm guessing convert("RGB") means convert from whatever to linear RGB? So that part should be fine.
- willvarfar 12y ago
  And yet the article says its not to do with the colour of flesh etc..
  - sp332 12y ago
    When someone told him that it was probably due to the colors of flesh in photographs, he tried it with a pool of graffiti. Still orange. Looks like there was only one test done explicitly without people.
krazydad 12y ago
Hi, this Jim, the subject of the article. The Atlantic article omits that I'm normalizing the result (otherwise, the histogram is clustered in the middle and you get a dirt-brown color -- the hue is orange, but it's very desaturated). Here is pseudo-code in Processing showing what I'm doing: To avoid floating point rounding issues, I process the pixels in integer space. For each channel (R,G,B) I create an 1D array of integers (one integer for each pixel) and initialize them to zero. smaxPixels = width*height; srmap = new int[smaxPixels]; sgmap = new int[smaxPixels]; sbmap = new int[smaxPixels]; for (int i = 0; i < smaxPixels; ++i) { srmap[i] = 0; sgmap[i] = 0; sbmap[i] = 0; } For each pixel in each image, I add the pixel value to the sum for the corresponding pixel (in the below code, 'offset' is typically zero). for (int i = 0; i < smaxPixels; ++i) { int px = sImage.pixels[i]; srmap[i] += red(px); sgmap[i] += green(px); sbmap[i] += blue(px); } I then produce a normalized image by finding the minimum and maximum components, and mapping all the pixel sums to that range. for (int i = 0; i < smaxPixels; ++i) { minValue = min(minValue, srmap[i]); maxValue = max(maxValue, srmap[i]); minValue = min(minValue, sgmap[i]); maxValue = max(maxValue, sgmap[i]); minValue = min(minValue, sbmap[i]); maxValue = max(maxValue, sbmap[i]); } pg.beginDraw(); // pg is an offscreen Image buffer I am going to draw into... pg.loadPixels(); for (int i = 0; i < smaxPixels; ++i) { if (maxImagesPerTile == 1) pg.pixels[i] = color(srmap[i],sgmap[i],sbmap[i]); else pg.pixels[i] = color( map(srmap[i],minValue,maxValue,0,255), map(sgmap[i],minValue,maxValue,0,255), map(sbmap[i],minValue,maxValue,0,255)); } pg.updatePixels(); pg.endDraw(); That's the essence of it. The bright "orange" is a normalization effect. Note that if I were to simply "average" the images without the normalization step, the resulting color is generally a dirt-brown. Normalizing the image has the effect of increasing the saturation and the contrast, without affecting the hue in an HSL sense. I discuss this in my paper, although the Atlantic article omitted this important point.
kaoD 12y ago
Your grayish results are exactly what I would expect from averaging RGB values.

sp332 12y ago

Maybe you should include lots of faces? And check that you're doing sRGB gamma math correctly. http://http.developer.nvidia.com/GPUGems3/gpugems3_ch24.html http://http.developer.nvidia.com/GPUGems3/gpugems3_ch24.html Edit: I'm guessing convert("RGB") means convert from whatever to linear RGB? So that part should be fine.

willvarfar 12y ago
And yet the article says its not to do with the colour of flesh etc..
- sp332 12y ago
  When someone told him that it was probably due to the colors of flesh in photographs, he tried it with a pool of graffiti. Still orange. Looks like there was only one test done explicitly without people.

krazydad 12y ago

Hi, this Jim, the subject of the article. The Atlantic article omits that I'm normalizing the result (otherwise, the histogram is clustered in the middle and you get a dirt-brown color -- the hue is orange, but it's very desaturated). Here is pseudo-code in Processing showing what I'm doing: To avoid floating point rounding issues, I process the pixels in integer space. For each channel (R,G,B) I create an 1D array of integers (one integer for each pixel) and initialize them to zero. smaxPixels = width*height; srmap = new int[smaxPixels]; sgmap = new int[smaxPixels]; sbmap = new int[smaxPixels]; for (int i = 0; i < smaxPixels; ++i) { srmap[i] = 0; sgmap[i] = 0; sbmap[i] = 0; } For each pixel in each image, I add the pixel value to the sum for the corresponding pixel (in the below code, 'offset' is typically zero). for (int i = 0; i < smaxPixels; ++i) { int px = sImage.pixels[i]; srmap[i] += red(px); sgmap[i] += green(px); sbmap[i] += blue(px); } I then produce a normalized image by finding the minimum and maximum components, and mapping all the pixel sums to that range. for (int i = 0; i < smaxPixels; ++i) { minValue = min(minValue, srmap[i]); maxValue = max(maxValue, srmap[i]); minValue = min(minValue, sgmap[i]); maxValue = max(maxValue, sgmap[i]); minValue = min(minValue, sbmap[i]); maxValue = max(maxValue, sbmap[i]); } pg.beginDraw(); // pg is an offscreen Image buffer I am going to draw into... pg.loadPixels(); for (int i = 0; i < smaxPixels; ++i) { if (maxImagesPerTile == 1) pg.pixels[i] = color(srmap[i],sgmap[i],sbmap[i]); else pg.pixels[i] = color( map(srmap[i],minValue,maxValue,0,255), map(sgmap[i],minValue,maxValue,0,255), map(sbmap[i],minValue,maxValue,0,255)); } pg.updatePixels(); pg.endDraw(); That's the essence of it. The bright "orange" is a normalization effect. Note that if I were to simply "average" the images without the normalization step, the resulting color is generally a dirt-brown. Normalizing the image has the effect of increasing the saturation and the contrast, without affecting the hue in an HSL sense. I discuss this in my paper, although the Atlantic article omitted this important point.

kaoD 12y ago

Your grayish results are exactly what I would expect from averaging RGB values.