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Reconstruct high quality 28x28 .npy files from binary files #19

Closed
azouaoui-cv opened this issue Jul 3, 2018 · 15 comments
Closed

Reconstruct high quality 28x28 .npy files from binary files #19

azouaoui-cv opened this issue Jul 3, 2018 · 15 comments

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@azouaoui-cv
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First off, thank you so much for providing such a helpful dataset, it truly is a goldmine!

I am having trouble reconstructing the 28x28 images from the binary files provided in this dataset.
What are the actual steps in order to reconstruct the 28x28 images in the provided quality from the binary files?

My issue is quite similar to #15 but I managed to get intermediary results.

Here is my current progress:

  • Using the examples/binary_file_parser.py I am able to reconstruct the image in any given size by handling the stroke paths.

  • I am also able to use some blurring to smooth the image.

  • However the quality of the reconstructed image is nowhere near the 28x28 images dataset provided in this repository.

This is an original image from the 28x28 .npy dataset:
original_npy

This is my reconstruction using no blurring technique:
reconstruction_no_blur

And this is my reconstruction using a (2, 2) blur kernel in OpenCV:
reconstruction_blur_2

Any idea on how to reconstruct these images in the quality that is available when downloading the 28x28 .npy files?
Is there some more advanced smoothing and filtering techniques that I have been missing?
@HalfdanJ
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HalfdanJ commented Jul 3, 2018 •
edited

The blurring is caused by antialiasing. The drawings were drawn with the follow settings:

surface = cairo.ImageSurface(cairo.FORMAT_ARGB32, 28, 28)
ctx = cairo.Context(surface)
ctx.set_antialias(cairo.ANTIALIAS_BEST)
ctx.set_line_cap(cairo.LINE_CAP_ROUND)
ctx.set_line_join(cairo.LINE_JOIN_ROUND)
ctx.set_line_width(16)

It was based on the simplified drawings, scaled from size 256x256 to 28x28. Hope this helps

@HalfdanJ
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HalfdanJ commented Jul 3, 2018 •
edited

The full function looks like this:

import numpy as np
import cairocffi as cairo

def vector_to_raster(vector_images, side=28, line_diameter=16, padding=16, bg_color=(0,0,0), fg_color=(1,1,1)):
    """
    padding and line_diameter are relative to the original 256x256 image.
    """
    
    original_side = 256.
    
    surface = cairo.ImageSurface(cairo.FORMAT_ARGB32, side, side)
    ctx = cairo.Context(surface)
    ctx.set_antialias(cairo.ANTIALIAS_BEST)
    ctx.set_line_cap(cairo.LINE_CAP_ROUND)
    ctx.set_line_join(cairo.LINE_JOIN_ROUND)
    ctx.set_line_width(line_diameter)

    # scale to match the new size
    # add padding at the edges for the line_diameter
    # and add additional padding to account for antialiasing
    total_padding = padding * 2. + line_diameter
    new_scale = float(side) / float(original_side + total_padding)
    ctx.scale(new_scale, new_scale)
    ctx.translate(total_padding / 2., total_padding / 2.)

    raster_images = []
    for vector_image in vector_images:
        # clear background
        ctx.set_source_rgb(*bg_color)
        ctx.paint()
        
        bbox = np.hstack(vector_image).max(axis=1)
        offset = ((original_side, original_side) - bbox) / 2.
        offset = offset.reshape(-1,1)
        centered = [stroke + offset for stroke in vector_image]

        # draw strokes, this is the most cpu-intensive part
        ctx.set_source_rgb(*fg_color)        
        for xv, yv in centered:
            ctx.move_to(xv[0], yv[0])
            for x, y in zip(xv, yv):
                ctx.line_to(x, y)
            ctx.stroke()

        data = surface.get_data()
        raster_image = np.copy(np.asarray(data)[::4])
        raster_images.append(raster_image)
    
    return raster_images

@HalfdanJ HalfdanJ mentioned this issue Jul 3, 2018
Closed
@azouaoui-cv
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Thank you so much!

@HalfdanJ
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HalfdanJ commented Jul 9, 2018

Closing issue. Feel free to re-open if there are more questions or issues with this

@HalfdanJ HalfdanJ closed this as completed Jul 9, 2018
@chuck0518
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chuck0518 commented Aug 15, 2018 •
edited

hi,man,thank you for your code for reconstruct high quality image. But i don't konw the input parameter vector_images' format,can you help me?@ HalfdanJ

@HalfdanJ
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The vector_images I believe would be an array of drawings from the simplified dataset (the vector strokes). The part that looks like this

    "drawing":[[[129,128,129,129,130,130,131,132,132,133,133,133,133,...]]]

@chuck0518
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got it,thank you

@chuck0518
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chuck0518 commented Aug 24, 2018 •
edited

@HalfdanJ I am sorry to trouble you again. when i input the vector strokes,i get output raster_images as a List like [array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0], dtype=uint8)], it is wrong when i show it as a rgb image. I find the return is a raster image, i don't know how to show and save, i try to use the gdal lib in osgeo but failed,could you give me a correct code?

@HalfdanJ HalfdanJ mentioned this issue Mar 28, 2019
Closed
@shubhank008
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@HalfdanJ Can you please also provide the conversion code for using saved .jpg/.png drawing files just like the above bitmap ?

Trained the model using .npy data and testing/eval runs fine but when using saved drawings, predictions are off and I am pretty sure its to do with how I am trying to resize and process the image, since it does not look like the ones in npy

Here is what I am using

from keras.preprocessing import image
apple = qd.get_drawing("face",10)
#apple.image.save("apple.png")
print(apple.recognized)
#apple.image.save("apple.png")


#img = Image.open('apple.png')
img = apple.image.convert("L")
img = img.resize((28,28),Image.BILINEAR)
#img = np.array(img)
img = image.img_to_array(img)
#img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.bitwise_not(img)
#img = cv2.threshold(img, 100, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
img = img.astype('float32')

img = img.reshape(1,28,28,1)
img = 255-img
img /= 255
#img = np.expand_dims(img, axis=0)
#img = img.astype('float32') / 255.0
#img = img.reshape(28,28,1)
#img = np.expand_dims(img, axis=0)
#img = img.reshape(1,28,28,1)
print(np.shape(img))

plt.imshow(img.squeeze()) 
pred = model.predict(img)[0]
ind = (-pred).argsort()[:5]
latex = [class_names[x] for x in ind]
print(latex)

Random image from npy data set
chrome_K8MykfviC8

My converted image
chrome_BQMchu6Ubw

@maaciekz
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maaciekz commented Feb 3, 2020

got it,thank you

@chuck0518

I am still getting error when I pass array from Drawning(simplified ndjson) to vector_images

axis 1 is out of bounds for array of dimension 1

@aashay96
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aashay96 commented Apr 20, 2020 •
edited

@maaciekz Were you able to solve this?

@maaciekz
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maaciekz commented Apr 20, 2020 •
edited

@aashay96

not really:( I have developed my solution. It reads simplified ndjson provided by google and generates image. Hope it will help:

import jsonlines
import numpy as np
import cairo
import matplotlib
from PIL import Image
%matplotlib notebook

file = open('full_simplified_yoga.ndjson',encoding='utf-8')

allScratches = jsonlines.Reader(file)

def add_points(obj):
scratch = np.zeros(( 256, 512))
for j in obj['drawing']:
for k in range(len(j[0])):
x = j[0][k] + 256
y = j[1][k]
scratch[y][x] = 255
return scratch

def paint_lines(obj):
line_diameter=20
surface = cairo.ImageSurface(cairo.FORMAT_ARGB32, 256, 256)
cr = cairo.Context(surface)
cr.set_antialias(cairo.ANTIALIAS_BEST)
cr.set_line_cap(cairo.LINE_CAP_ROUND)
cr.set_line_join(cairo.LINE_JOIN_ROUND)
cr.set_line_width(line_diameter)
cr.set_line_width(3)

for j in obj['drawing']:
    cr.set_source_rgb(0,0,0)
    cr.paint()
    for k in range(0, len(j[0])-1):
        cr.set_source_rgb(255, 255, 255)
        x = j[0][k]
        y = j[1][k]
        cr.move_to(x, y)
        x1 =  j[0][(k+1)]
        y1 =  j[1][(k+1)]
        cr.line_to(x1, y1 )
cr.stroke()          

data = surface.get_data()
newscratch = np.copy(np.asarray(data)[::4])
lines = newscratch.reshape(256, 256)
return lines

def merge_dots_and_lines(scratch, lines):
scratch[ : , : 256 ] = lines
scratch = scratch / 255
final = np.where(scratch < 0.1, 1 , 0 )
return final

def save_training_img(final, name):
matplotlib.image.imsave(name, final,cmap='gray')

for obj in allScratches:
dots = add_points(obj)
lines = paint_lines(obj)
final = merge_dots_and_lines(dots, lines)
image_name = obj['key_id'] + '.jpg'
save_training_img(final, image_name)
print(image_name)

@thomasjonas thomasjonas mentioned this issue May 4, 2020
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@Randulfe
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Randulfe commented May 5, 2020 •
edited

Hi so I am getting the following error when trying to run the converter function:
return umr_maximum(a, axis, None, out, keepdims, initial, where) numpy.AxisError: axis 1 is out of bounds for array of dimension 1

I am using the following data for running the functions:

data = [[[0,22,37,64,255],[218,220,227,228,211]],[[76,95,135,141,150,159,166,180,186,201],[220,138,31,0,63,79,117,150,191,224]],[[94,104,111,119,127,141,143,142,180,191],[212,167,149,80,59,41,30,134,202,232]],[[109,127,137,147,150,162,172,185],[122,120,104,97,99,124,128,128]],[[75,130,158],[162,159,150]]] image = vector_to_raster(data) print(image)

Am I doing something wrong?

@Sunstar007-lab

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@Charles-Lu
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@Randulfe @maaciekz
I think the function vector_to_raster needs a list of images as input.

for vector_image in vector_images:
    ctx.set_source_rgb(*bg_color)
    ctx.paint()
    bbox = np.hstack(vector_image).max(axis=1)

Therefore, passing a single vector image as vector_images will cause AxisError. You can probably fix it by warping the single vector image with [].

samuelmbiya added a commit to samuelmbiya/EEE4114F_ML_Project that referenced this issue Jun 22, 2021
@samuelmbiya
Add files via upload
6e67d66 
Changed the line diameter to 16 to match the original dataset. Reference: googlecreativelab/quickdraw-dataset#19 (comment)
@samuelmbiya samuelmbiya mentioned this issue Jun 22, 2021
Merged
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9 participants
@HalfdanJ @shubhank008 @aashay96 @azouaoui-cv @Charles-Lu @Randulfe @chuck0518 @maaciekz @Sunstar007-lab