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I'm looking to use a small numpy array to generate a curve that I can use to predict the height measurement at non-known points. I have several points that I am using to create a poly1d. I know it's possible, we use software that does it just fine at work, and when I used a different image as a tester, plugging the values into Excel and getting the polynomial, it worked fine, but I'm getting pretty drastic measurements on a different calibratable image, I get drastically different results.
Here is the image that I'm trying to measure.
The stick on the front of the pole contains known measurements. From bottom to top, they are 3'6" (42"), 6'6" (78"), 9' 8" (116"), 13' (156)
The picture has been through opencv undistort with a calibrated camera.
This is the function that actually performs the logic. x and y are gathered by cv2 EVENT_LBUTTONUP, and sent to this function.
Checking the lengths of the array is just to help me figure out why this isn't working, trying to generate a line to show the curve fit.
dist = self.firstClick-y
self.yData.append(dist)
if len(self.yData) > 4:
print(self.poly(dist))
if len(self.yData) == 4:
array = np.array(self.xData)
array = np.expand_dims(array, axis=0)
print(self.xData)
print(self.yData)
array=np.append(array, [self.yData], axis=0)
print(array)
x = array[:,0]
y = array[:,1]
self.poly = np.poly1d(np.polyfit(x, y, 2))
poly1d = np.poly1d(self.poly)
xp = np.linspace(-2, 20, 1)
_ = plt.plot(x, y, '.', xp, self.poly(xp), '-', xp, self.poly(xp), '--')
plt.ylim(0,200)
plt.show()
When I run this code, my values tend to quickly go into the tens of thousands when I'm attempting to collect the measurement at 18' 11", (the lowest wire).
Any help would be appreciated, I've been up all night trying to fit this curve.
Edit:
Sorry, I should have included the code used to display and scale the image.
self.img = cv2.imread(imagePath, cv2.IMREAD_ANYCOLOR)
self.scale_percent = 30
self.width = int(self.img.shape[1] * self.scale_percent/100)
self.height = int(self.img.shape[0] * self.scale_percent/100)
dsize = (self.width, self.height)
self.output = cv2.resize(self.img, dsize)
img = self.output
cv2.imshow('image', img)
cv2.setMouseCallback('image', self.click_event)
cv2.waitKey()
I just called this function to display the image and the below code to calibrate the values.
if self.firstClick == 0:
self.firstClick = y
cv2.putText(self.output, "Pole Base", (x, y), font, 1, (255, 255, 0), 2)
cv2.imshow('image', self.output)
elif self.firstClick != 0 and self.secondClick == 0:
self.secondClick = y
print("The difference in first and second clicks is", self.firstClick - self.secondClick)
first = self.firstClick - self.secondClick
inch = first/42
foot = inch*12
self.foot = foot
print("One foot is currently: ", foot)
self.firstLine = 3.5*12
self.secondLine = 6.5*12
self.thirdLine = 9.67*12
self.fourthLine = 13*12
self.xData = np.array([self.firstLine, self.secondLine, self.thirdLine, self.fourthLine])
self.yData.append(self.firstLine)
print(self.firstLine)
print(self.secondLine)
print(self.thirdLine)
print(self.fourthLine)
I don't know if this question have been repeating in here. If yes then i'm sorry..
I have a box that positioned to see H,W,L view. I understand steps to get vertices however most of the examples in the net only describes how to get 4 vertices from 2D plane. So my question is, how if we want to get 7 vertices (like the pic above) and handle it in numpy? How to differentiate between upper points and lower points?
I will be using Python to determine this.
Here's my attempt to get the 8 corners of the 3d rectangle. I masked on the saturation channel of the HSV color space since that separates out white.
I used findContours to get the contour of the box and then used approxPolyDP to get a six-point approximation (the six visible corners).
From there I approximated the two "hidden" corners via a parallelogram approximation. For each point I looked two points behind and created a fourth point that would make a parallelogram with that side. I then took the centroid of these parallelogram points to guess the corner. I hoped that taking the centroid of the points would help even out the error between the parallelogram assumption and the perspective warping, but it did a poor job.
If you need a better approximation there are probably ways to estimate the perspective warping to get the corners.
import cv2
import numpy as np
import random
def tup(point):
return (int(point[0]), int(point[1]));
# load image
img = cv2.imread("box.jpg");
# reduce size to fit on screen
scale = 0.25;
h,w = img.shape[:2];
h = int(scale*h);
w = int(scale*w);
img = cv2.resize(img, (w,h));
copy = np.copy(img);
# convert to hsv
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV);
h,s,v = cv2.split(hsv);
# make mask
mask = cv2.inRange(s, 30, 255);
# dilate and erode to get rid of small holes
kernel = np.ones((5,5), np.uint8);
mask = cv2.dilate(mask, kernel, iterations = 1);
mask = cv2.erode(mask, kernel, iterations = 1);
# contours # OpenCV 3.4, in OpenCV 2 or 4 it returns (contours, _)
_, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE);
contour = contours[0]; # just take the first one
# approx until 6 points
num_points = 999999;
step_size = 0.01;
percent = step_size;
while num_points >= 6:
# get number of points
epsilon = percent * cv2.arcLength(contour, True);
approx = cv2.approxPolyDP(contour, epsilon, True);
num_points = len(approx);
# increment
percent += step_size;
# step back and get the points
# there could be more than 6 points if our step size misses it
percent -= step_size * 2;
epsilon = percent * cv2.arcLength(contour, True);
approx = cv2.approxPolyDP(contour, epsilon, True);
# draw contour
cv2.drawContours(img, [approx], -1, (0,0,200), 2);
# draw points
for point in approx:
point = point[0]; # drop extra layer of brackets
center = (int(point[0]), int(point[1]));
cv2.circle(img, center, 4, (150, 200, 0), -1);
# do parallelogram approx to get the two "hidden" corners to complete our 3d rectangle
proposals = [];
size = len(approx);
for a in range(size):
# get points backwards
two = approx[a - 2][0];
one = approx[a - 1][0];
curr = approx[a][0];
# get vector from one -> two
dx = two[0] - one[0];
dy = two[1] - one[1];
hidden = [curr[0] + dx, curr[1] + dy];
proposals.append([hidden, curr, a, two]);
# debug draw
c = np.copy(copy);
cv2.circle(c, tup(two), 4, (255, 0, 0), -1);
cv2.circle(c, tup(one), 4, (0,255,0), -1);
cv2.circle(c, tup(curr), 4, (0,0,255), -1);
cv2.circle(c, tup(hidden), 4, (255,255,0), -1);
cv2.line(c, tup(two), tup(one), (0,0,200), 1);
cv2.line(c, tup(curr), tup(hidden), (0,0,200), 1);
cv2.imshow("Mark", c);
cv2.waitKey(0);
# draw proposals
for point in proposals:
point = point[0];
center = (point[0], point[1]);
cv2.circle(img, center, 4, (200, 100, 0), -1);
# group points and sum up points
hidden_corners = [[0,0], [0,0]];
for point in proposals:
# get index and update hidden corners
index = point[2] % 2;
pos = point[0];
hidden_corners[index][0] += pos[0];
hidden_corners[index][1] += pos[1];
# divide to get centroid
hidden_corners[0][0] /= 3.0;
hidden_corners[0][1] /= 3.0;
hidden_corners[1][0] /= 3.0;
hidden_corners[1][1] /= 3.0;
# draw new points
for point in proposals:
# unpack
pos = point[0];
parent = point[1];
index = point[2] % 2;
source = point[3];
# draw
color = [random.randint(0, 150) for a in range(3)];
cv2.line(img, tup(hidden_corners[index]), tup(parent), (0,0,200), 2);
cv2.line(img, tup(pos), tup(parent), color, 1);
cv2.line(img, tup(pos), tup(source), color, 1);
cv2.circle(img, tup(hidden_corners[index]), 4, (200, 200, 0), -1);
# show
cv2.imshow("Image", img);
cv2.imshow("Mask", mask);
cv2.waitKey(0);
I am following the example from Astropy docs for 2D Cutout.
The header of my FITS file:
SIMPLE = T / file does conform to FITS standard
BITPIX = -32 / number of bits per data pixel
NAXIS = 3 / number of data axes
NAXIS1 = 512 / length of data axis 1
NAXIS2 = 512 / length of data axis 2
NAXIS3 = 3 / length of data axis 3
EXTEND = T / FITS dataset may contain extensions
COMMENT FITS (Flexible Image Transport System) format is defined in 'Astronomy
COMMENT and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H
SURVEY = 'DECaLS '
VERSION = 'DR8-south'
IMAGETYP= 'image '
BANDS = 'grz '
BAND0 = 'g '
BAND1 = 'r '
BAND2 = 'z '
CTYPE1 = 'RA---TAN' / TANgent plane
CTYPE2 = 'DEC--TAN' / TANgent plane
CRVAL1 = 186.11382 / Reference RA
CRVAL2 = 0.15285422 / Reference Dec
CRPIX1 = 256.5 / Reference x
CRPIX2 = 256.5 / Reference y
CD1_1 = -7.27777777777778E-05 / CD matrix
CD1_2 = 0. / CD matrix
CD2_1 = 0. / CD matrix
CD2_2 = 7.27777777777778E-05 / CD matrix
IMAGEW = 512. / Image width
IMAGEH = 512. / Image height
So far what I have tried :
from astropy.coordinates import SkyCoord
from astropy.wcs import WCS
position = SkyCoord(hdu[0].header['CRVAL1']*u.deg,hdu[0].header['CRVAL2']*u.deg)
size = 200*u.pixel
wcs1 = WCS(hdu[0].header)
cutout = Cutout2D(hdu[0].data[0], position ,size, wcs = wcs1 )
I run into error in the last line.
Error :
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-142-7cc21a13e941> in <module>
----> 1 cutout = Cutout2D(hdu[0].data[0], position ,size, wcs = wcs1 )
/Applications/anaconda3/lib/python3.7/site-packages/astropy/nddata/utils.py in __init__(self, data, position, size, wcs, mode, fill_value, copy)
714 if wcs is not None:
715 self.wcs = deepcopy(wcs)
--> 716 self.wcs.wcs.crpix -= self._origin_original_true
717 self.wcs.array_shape = self.data.shape
718 if wcs.sip is not None:
ValueError: operands could not be broadcast together with shapes (3,) (2,) (3,)
My guess is it is because naxis =3 in my file and the documentation assumes naxis = 2. Though I am not sure if that is the actual issue here. Can anybody help fix this error?
Since your WCS is 3d, but you're getting a 2d cutout, you need to drop the 3rd dimension. Try cutout = Cutout2D(hdu[0].data[0], position ,size, wcs = wcs1.celestial ), where .celestial is a convenience tool to drop the third dimension in the WCS.
I have two question:
1) I'd like to know how can I add/subtract a constante torch.FloatTensor of size 1 to all of the elemets of a torch.FloatTensor of size 30.
2) How can I multiply each element of a torch.FloatTensor of size 30 by a random value (different or not for each).
My code:
import torch
dtype = torch.cuda.FloatTensor
def main():
pop, xmax, xmin = 30, 5, -5
x = (xmax-xmin)*torch.rand(pop).type(dtype)+xmin
y = torch.pow(x, 2)
[miny, indexmin] = y.min(0)
gxbest = x[indexmin]
pxbest = x
pybest = y
v = torch.rand(pop)
vnext = torch.rand()*v + torch.rand()*(pxbest - x) + torch.rand()*(gxbest - x)
main()
What is the best way to do it? I think I should so how convert the gxbest into a torch.FloatTensor of size 30 but how can I do that?
I've try to create a vector:
Variable(torch.from_numpy(np.ones(pop)))*gxbest
But it did not work. The multiplication is not working also.
RuntimeError: inconsistent tensor size
Thank you all for your help!
1) How can I add/subtract a constant torch.FloatTensor of size 1 to all of the elements of a torch.FloatTensor of size 30?
You can do it directly in pytorch 0.2.
import torch
a = torch.randn(30)
b = torch.randn(1)
print(a-b)
In case if you get any error due to size mismatch, you can make a small change as follows.
print(a-b.expand(a.size(0))) # to make both a and b tensor of same shape
2) How can I multiply each element of a torch.FloatTensor of size 30 by a random value (different or not for each)?
In pytorch 0.2, you can do it directly as well.
import torch
a = torch.randn(30)
b = torch.randn(1)
print(a*b)
In case, if you get an error due to size mismatch, do as follows.
print(a*b.expand(a.size(0)))
So, in your case you can simply change the size of gxbest tensor from 1 to 30 as follows.
gxbest = gxbest.expand(30)
In other words, I want to make a heatmap (or surface plot) where the color varies as a function of 2 variables. (Specifically, luminance = magnitude and hue = phase.) Is there any native way to do this?
Some examples of similar plots:
Several good examples of exactly(?) what I want to do.
More examples from astronomy, but with non-perceptual hue
Edit: This is what I did with it: https://github.com/endolith/complex_colormap
imshow can take an array of [r, g, b] entries. So you can convert the absolute values to intensities and phases - to hues.
I will use as an example complex numbers, because for it it makes the most sense. If needed, you can always add numpy arrays Z = X + 1j * Y.
So for your data Z you can use e.g.
imshow(complex_array_to_rgb(Z))
where (EDIT: made it quicker and nicer thanks to this suggestion)
def complex_array_to_rgb(X, theme='dark', rmax=None):
'''Takes an array of complex number and converts it to an array of [r, g, b],
where phase gives hue and saturaton/value are given by the absolute value.
Especially for use with imshow for complex plots.'''
absmax = rmax or np.abs(X).max()
Y = np.zeros(X.shape + (3,), dtype='float')
Y[..., 0] = np.angle(X) / (2 * pi) % 1
if theme == 'light':
Y[..., 1] = np.clip(np.abs(X) / absmax, 0, 1)
Y[..., 2] = 1
elif theme == 'dark':
Y[..., 1] = 1
Y[..., 2] = np.clip(np.abs(X) / absmax, 0, 1)
Y = matplotlib.colors.hsv_to_rgb(Y)
return Y
So, for example:
Z = np.array([[3*(x + 1j*y)**3 + 1/(x + 1j*y)**2
for x in arange(-1,1,0.05)] for y in arange(-1,1,0.05)])
imshow(complex_array_to_rgb(Z, rmax=5), extent=(-1,1,-1,1))
imshow(complex_array_to_rgb(Z, rmax=5, theme='light'), extent=(-1,1,-1,1))
imshow will take an NxMx3 (rbg) or NxMx4 (grba) array so you can do your color mapping 'by hand'.
You might be able to get a bit of traction by sub-classing Normalize to map your vector to a scaler and laying out a custom color map very cleverly (but I think this will end up having to bin one of your dimensions).
I have done something like this (pdf link, see figure on page 24), but the code is in MATLAB (and buried someplace in my archives).
I agree a bi-variate color map would be useful (primarily for representing very dense vector fields where your kinda up the creek no matter what you do).
I think the obvious extension is to let color maps take complex arguments. It would require specialized sub-classes of Normalize and Colormap and I am going back and forth on if I think it would be a lot of work to implement. I suspect if you get it working by hand it will just be a matter of api wrangling.
I created an easy to use 2D colormap class, that takes 2 NumPy arrays and maps them to an RGB image, based on a reference image.
I used #GjjvdBurg's answer as a starting point. With a bit of work, this could still be improved, and possibly turned into a proper Python module - if you want, feel free to do so, I grant you all credits.
TL;DR:
# read reference image
cmap_2d = ColorMap2D('const_chroma.jpeg', reverse_x=True) # , xclip=(0,0.9))
# map the data x and y to the RGB space, defined by the image
rgb = cmap_2d(data_x, data_y)
# generate a colorbar image
cbar_rgb = cmap_2d.generate_cbar()
The ColorMap2D class:
class ColorMap2D:
def __init__(self, filename: str, transpose=False, reverse_x=False, reverse_y=False, xclip=None, yclip=None):
"""
Maps two 2D array to an RGB color space based on a given reference image.
Args:
filename (str): reference image to read the x-y colors from
rotate (bool): if True, transpose the reference image (swap x and y axes)
reverse_x (bool): if True, reverse the x scale on the reference
reverse_y (bool): if True, reverse the y scale on the reference
xclip (tuple): clip the image to this portion on the x scale; (0,1) is the whole image
yclip (tuple): clip the image to this portion on the y scale; (0,1) is the whole image
"""
self._colormap_file = filename or COLORMAP_FILE
self._img = plt.imread(self._colormap_file)
if transpose:
self._img = self._img.transpose()
if reverse_x:
self._img = self._img[::-1,:,:]
if reverse_y:
self._img = self._img[:,::-1,:]
if xclip is not None:
imin, imax = map(lambda x: int(self._img.shape[0] * x), xclip)
self._img = self._img[imin:imax,:,:]
if yclip is not None:
imin, imax = map(lambda x: int(self._img.shape[1] * x), yclip)
self._img = self._img[:,imin:imax,:]
if issubclass(self._img.dtype.type, np.integer):
self._img = self._img / 255.0
self._width = len(self._img)
self._height = len(self._img[0])
self._range_x = (0, 1)
self._range_y = (0, 1)
#staticmethod
def _scale_to_range(u: np.ndarray, u_min: float, u_max: float) -> np.ndarray:
return (u - u_min) / (u_max - u_min)
def _map_to_x(self, val: np.ndarray) -> np.ndarray:
xmin, xmax = self._range_x
val = self._scale_to_range(val, xmin, xmax)
rescaled = (val * (self._width - 1))
return rescaled.astype(int)
def _map_to_y(self, val: np.ndarray) -> np.ndarray:
ymin, ymax = self._range_y
val = self._scale_to_range(val, ymin, ymax)
rescaled = (val * (self._height - 1))
return rescaled.astype(int)
def __call__(self, val_x, val_y):
"""
Take val_x and val_y, and associate the RGB values
from the reference picture to each item. val_x and val_y
must have the same shape.
"""
if val_x.shape != val_y.shape:
raise ValueError(f'x and y array must have the same shape, but have {val_x.shape} and {val_y.shape}.')
self._range_x = (np.amin(val_x), np.amax(val_x))
self._range_y = (np.amin(val_y), np.amax(val_y))
x_indices = self._map_to_x(val_x)
y_indices = self._map_to_y(val_y)
i_xy = np.stack((x_indices, y_indices), axis=-1)
rgb = np.zeros((*val_x.shape, 3))
for indices in np.ndindex(val_x.shape):
img_indices = tuple(i_xy[indices])
rgb[indices] = self._img[img_indices]
return rgb
def generate_cbar(self, nx=100, ny=100):
"generate an image that can be used as a 2D colorbar"
x = np.linspace(0, 1, nx)
y = np.linspace(0, 1, ny)
return self.__call__(*np.meshgrid(x, y))
Usage:
Full example, using the constant chroma reference taken from here as a screenshot:
# generate data
x = y = np.linspace(-2, 2, 300)
xx, yy = np.meshgrid(x, y)
ampl = np.exp(-(xx ** 2 + yy ** 2))
phase = (xx ** 2 - yy ** 2) * 6 * np.pi
data = ampl * np.exp(1j * phase)
data_x, data_y = np.abs(data), np.angle(data)
# Here is the 2D colormap part
cmap_2d = ColorMap2D('const_chroma.jpeg', reverse_x=True) # , xclip=(0,0.9))
rgb = cmap_2d(data_x, data_y)
cbar_rgb = cmap_2d.generate_cbar()
# plot the data
fig, plot_ax = plt.subplots(figsize=(8, 6))
plot_extent = (x.min(), x.max(), y.min(), y.max())
plot_ax.imshow(rgb, aspect='auto', extent=plot_extent, origin='lower')
plot_ax.set_xlabel('x')
plot_ax.set_ylabel('y')
plot_ax.set_title('data')
# create a 2D colorbar and make it fancy
plt.subplots_adjust(left=0.1, right=0.65)
bar_ax = fig.add_axes([0.68, 0.15, 0.15, 0.3])
cmap_extent = (data_x.min(), data_x.max(), data_y.min(), data_y.max())
bar_ax.imshow(cbar_rgb, extent=cmap_extent, aspect='auto', origin='lower',)
bar_ax.set_xlabel('amplitude')
bar_ax.set_ylabel('phase')
bar_ax.yaxis.tick_right()
bar_ax.yaxis.set_label_position('right')
for item in ([bar_ax.title, bar_ax.xaxis.label, bar_ax.yaxis.label] +
bar_ax.get_xticklabels() + bar_ax.get_yticklabels()):
item.set_fontsize(7)
plt.show()
I know this is an old post, but want to help out others that may arrive late. Below is a python function to implement complex_to_rgb from sage. Note: This implementation isn't optimal, but it is readable. See links: (examples)(source code)
Code:
import numpy as np
def complex_to_rgb(z_values):
width = z_values.shape[0]
height = z_values.shape[1]
rgb = np.zeros(shape=(width, height, 3))
for i in range(width):
row = z_values[i]
for j in range(height):
# define value, real(value), imag(value)
zz = row[j]
x = np.real(zz)
y = np.imag(zz)
# define magnitued and argument
magnitude = np.hypot(x, y)
arg = np.arctan2(y, x)
# define lighness
lightness = np.arctan(np.log(np.sqrt(magnitude) + 1)) * (4 / np.pi) - 1
if lightness < 0:
bot = 0
top = 1 + lightness
else:
bot = lightness
top = 1
# define hue
hue = 3 * arg / np.pi
if hue < 0:
hue += 6
# set ihue and use it to define rgb values based on cases
ihue = int(hue)
# case 1
if ihue == 0:
r = top
g = bot + hue * (top - bot)
b = bot
# case 2
elif ihue == 1:
r = bot + (2 - hue) * (top - bot)
g = top
b = bot
# case 3
elif ihue == 2:
r = bot
g = top
b = bot + (hue - 2) * (top - bot)
# case 4
elif ihue == 3:
r = bot
g = bot + (4 - hue) * (top - bot)
b = top
# case 5
elif ihue == 4:
r = bot + (hue - 4) * (top - bot)
g = bot
b = top
# case 6
else:
r = top
g = bot
b = bot + (6 - hue) * (top - bot)
# set rgb array values
rgb[i, j, 0] = r
rgb[i, j, 1] = g
rgb[i, j, 2] = b
return rgb