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initialDetection.py
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initialDetection.py
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# Load an image
from PIL import Image
import io
import numpy as np
import glob
import os
import numpy as np
import trimesh
import pyrender
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.pyplot as plt
from PIL import Image
import json
import sys
csum = lambda z: np.cumsum(z)[:-1]
dsum = lambda z: np.cumsum(z[::-1])[-2::-1]
argmax = lambda x, f: np.mean(x[:-1][f == np.max(f)]) # Use the mean for ties.
clip = lambda z: np.maximum(1e-30, z)
def preliminaries(n, x):
"""Some math that is shared across multiple algorithms."""
assert np.all(n >= 0)
x = np.arange(len(n), dtype=n.dtype) if x is None else x
assert np.all(x[1:] >= x[:-1])
w0 = clip(csum(n))
w1 = clip(dsum(n))
p0 = w0 / (w0 + w1)
p1 = w1 / (w0 + w1)
mu0 = csum(n * x) / w0
mu1 = dsum(n * x) / w1
d0 = csum(n * x**2) - w0 * mu0**2
d1 = dsum(n * x**2) - w1 * mu1**2
return x, w0, w1, p0, p1, mu0, mu1, d0, d1
def GHT(n, x=None, nu=0, tau=0, kappa=0, omega=0.5, prelim=None):
assert nu >= 0
assert tau >= 0
assert kappa >= 0
assert omega >= 0 and omega <= 1
x, w0, w1, p0, p1, _, _, d0, d1 = prelim or preliminaries(n, x)
v0 = clip((p0 * nu * tau**2 + d0) / (p0 * nu + w0))
v1 = clip((p1 * nu * tau**2 + d1) / (p1 * nu + w1))
f0 = -d0 / v0 - w0 * np.log(v0) + 2 * (w0 + kappa * omega) * np.log(w0)
f1 = -d1 / v1 - w1 * np.log(v1) + 2 * (w1 + kappa * (1 - omega)) * np.log(w1)
return argmax(x, f0 + f1), f0 + f1
def im2hist(im, zero_extents=False):
# Convert an image to grayscale, bin it, and optionally zero out the first and last bins.
max_val = np.iinfo(im.dtype).max
x = np.arange(max_val+1)
e = np.arange(-0.5, max_val+1.5)
assert len(im.shape) in [2, 3]
im_bw = np.amax(im[...,:3], -1) if len(im.shape) == 3 else im
n = np.histogram(im_bw, e)[0]
if zero_extents:
n[0] = 0
n[-1] = 0
return n, x, im_bw
def render(i):
global _nu, _tau, _kappa, _omega
#t, score = GHT(n, x, 1e30, 0.20, 1e-30, 0.50, prelim)
#t, score = GHT(n, x, _nu, _tau, _kappa, _omega, prelim)
t, score = GHT(n, x, 1.00e+9,1.58e+7,1.58e+8, 0.71, prelim)
plt.figure(0, figsize=(16,5))
plt.subplot(1,3,1)
plt.imshow(im, cmap='gray')
plt.axis('off')
plt.subplot(1,3,2)
plt.imshow(im_bw > t, cmap='gray', vmin=0, vmax=1)
plt.gca().set_xticks([])
plt.gca().set_yticks([])
plt.subplot(1,3,3)
normalize = lambda x : (x - np.min(score)) * np.max(n) / (np.max(score) - np.min(score))
plt.plot((x[:-1] + x[1:])/2, normalize(score))
plt.scatter(t, normalize(score[int(t)]))
plt.bar(x, n, width=1)
plt.gca().set_yticks([]);
def update(nu=None, tau=None, kappa=None, omega=None):
global _nu, _tau, _kappa, _omega
_nu = nu or _nu
_tau = tau or _tau
_kappa = kappa or _kappa
_omega = omega or _omega
def reset(nu=None, tau=None, kappa=None, omega=None):
global nu_slider, tau_slider, kappa_slider, omega_slider
if nu:
nu_slider.value = nu
if tau:
tau_slider.value = tau
if kappa:
kappa_slider.value = kappa
if omega:
omega_slider.value = omega
def update_and_render(nu=None, tau=None, kappa=None, omega=None):
update(nu, tau, kappa, omega)
#render()
def point_cloud(depth, pathS):
"""Transform a depth image into a point cloud with one point for each
pixel in the image, using the camera transform for a camera
centred at cx, cy with field of view fx, fy.
depth is a 2-D ndarray with shape (rows, cols) containing
depths from 1 to 254 inclusive. The result is a 3-D array with
shape (rows, cols, 3). Pixels with invalid depth in the input have
NaN for the z-coordinate in the result.
"""
os.chdir(pathS + '/sequence')
myFiles = glob.glob('*.txt')
camera_pose = open(pathS + '/sequence/' + '_info.txt','r')
lines = camera_pose.readlines()
string = lines[7]
t = string.find('=')
string1 = string[t+2:]
fx = np.float(string1[:string1.find(' ')])
string2 = string1[string1.find(' ')+1:]
string3 = string2[string2.find(' ')+1:]
cx = np.float(string3[:string3.find(' ')])
string4 = string3[string3.find(' ')+1:]
string5 = string4[4:]
fy = np.float(string5[:string5.find(' ')])
string6 = string5[string5.find(' ')+1:]
cy = np.float(string6[:string6.find(' ')])
cx = 200
cy = 100
fx = 200
fy = 200
rows, cols = depth.shape
c, r = np.meshgrid(np.arange(cols), np.arange(rows), sparse=True)
valid = (depth > 0) & (depth < 255)
z = np.where(valid, depth , np.nan)
x = np.where(valid, z * (c - cx) / fx, 0)
y = np.where(valid, z * (r - cy) / fy, 0)
return np.dstack((x, y, z))
def compute(pathS,pathR):
os.chdir(pathS + '/sequence')
myFiles = glob.glob('*.txt')
camera_pose = open(pathS + '/sequence/' + '_info.txt','r')
lines = camera_pose.readlines()
string = lines[7]
t = string.find('=')
string1 = string[t+2:]
#877.5 0 479.75 0 0 877.5 269.75
fx = np.float(string1[:string1.find(' ')])
string2 = string1[string1.find(' ')+1:]
string3 = string2[string2.find(' ')+1:]
cx = np.float(string3[:string3.find(' ')])
string4 = string3[string3.find(' ')+1:]
string5 = string4[4:]
fy = np.float(string5[:string5.find(' ')])
string6 = string5[string5.find(' ')+1:]
cy = np.float(string6[:string6.find(' ')])
#camera to world
cv2gl = np.array([
[1, 0, 0, 0],
[0, -1, 0, 0],
[0, 0, -1, 0],
[0, 0, 0, 1],
])
fuze_trimesh = trimesh.load(pathS + '/mesh.refined.obj')
fuze_trimesh1 = trimesh.load(pathR + '/mesh.refined.align.trimesh.obj')
finalPoints_all = np.empty((0,3))
#for i in range(0,10):
for i in range (0,len(myFiles)-1):
if myFiles[i]!='_info.txt':
#rint (myFiles[i])
transform_mat = np.loadtxt(pathS+ '/sequence/'+ str(myFiles[i]))
#print (transform_mat)
#world to camera
transform_mat = np.linalg.inv(transform_mat)
tranform_mat = np.matmul(cv2gl,transform_mat)
transformation_mat = tranform_mat.transpose()
fuze_trimesh.apply_transform(tranform_mat)
mesh = pyrender.Mesh.from_trimesh(fuze_trimesh)
scene = pyrender.Scene()
scene.add(mesh, pose=np.eye(4))
cx = 200
cy = 100
fx = 200
fy = 200
camera = pyrender.IntrinsicsCamera(fx, fy, cx, cy, 0.01, 1000)
camera_pose = np.eye(4)
#camera_pose[2, 3] = 0.1
scene.add(camera, pose=camera_pose)
#pyrender.Viewer(scene, use_raymond_lighting=True)
imgDimX = 400
imgDimY = 200
r = pyrender.OffscreenRenderer(imgDimX, imgDimY)
color, depth = r.render(scene)
fuze_trimesh1.apply_transform(tranform_mat)
mesh1 = pyrender.Mesh.from_trimesh(fuze_trimesh1)
scene1 = pyrender.Scene()
scene1.add(mesh1, pose=np.eye(4))
scene1.add(camera, pose=camera_pose)
color1, depth1 = r.render(scene1)
#change here and select closer depth
newDepth = depth-depth1
newDepthN = newDepth
newDepthN[newDepth<0.10] = 0
newDepthN[newDepth == depth] = 0
newDepthN[newDepth == -depth1] = 0
newDepthF = np.absolute(newDepthN)
path = pathR + '/'
my_dpi = 10
#fig = plt.figure(frameon=False)
#fig = plt.figure(figsize=(imgDimX/my_dpi, imgDimY/my_dpi), dpi=my_dpi)
plt.imsave(path + str(i) + 'tt_nn11.png',newDepthF, cmap=plt.cm.gray_r)
byteImgIO = io.BytesIO()
byteImg = Image.open(path + str(i) + 'tt_nn11.png')
byteImg.save(byteImgIO, "PNG")
byteImgIO.seek(0)
byteImg = byteImgIO.read()
im = np.array(Image.open(io.BytesIO(byteImg)))
# Precompute a histogram and some integrals.
n, x, im_bw = im2hist(im)
prelim = preliminaries(n, x)
default_nu = np.sum(n)
default_tau = np.sqrt(1/12)
default_kappa = np.sum(n)
# default_omega = 0.50
default_omega = 0
_nu = default_nu
_tau = default_tau
_kappa = default_kappa
_omega = default_omega
#nu=1.00e+9
#tau=1.58e+7
omega = 0
tau = 500e+2
nu = 500e+2
kappa = 0
#kappa=1.58e+8
#t, score = GHT(n, x, _nu, _tau, _kappa, _omega, prelim)
t, score = GHT(n, x, 1000, 300, 0.00, 0.00, prelim)
#new3 400/200/700/700/0/0
#new 4 400/200/1000/1000/0/0
F = np.where(im_bw[:,:] < t)
mask = (im_bw < t)
depthC = np.minimum(depth, depth1)
new_array = a = np.empty((imgDimY,imgDimX))
new_array[:] = 0
new_array[mask] = depthC[mask]
#render(i)
#fig = plt.figure(frameon=False)
#fig = plt.figure(figsize=(imgDimX/my_dpi, imgDimY/my_dpi), dpi=my_dpi)
plt.imsave(path + str(i) + '.png',im_bw < t, cmap=plt.cm.gray_r)
point_cloudN = point_cloud(new_array,pathS)
pointsA = point_cloudN.reshape(imgDimY*imgDimX,3)
#print (pointsA.shape)
finalPointsFF = pointsA[~np.isnan(pointsA).any(axis=1)]
b = np.ones((imgDimY*imgDimX,1))
finalPoints = np.hstack((pointsA,b))
transform_mat = np.loadtxt(pathS + '/sequence/'+ str(myFiles[i]))
finalPoints = trimesh.transformations.transform_points(pointsA,transform_mat)
finalPoints = finalPoints[~np.isnan(finalPoints).any(axis=1)]
if np.shape(finalPoints)[0] > 0:
finalPoints_all = np.vstack((finalPoints_all,finalPoints))
np.savetxt(path +'/reprojected_10.xyz', finalPoints_all, delimiter=' ')
if __name__ == '__main__':
file1 = open('validation-scan-rescan.txt')
Lines = file1.readlines()
with open('3RScan.json') as json_file:
data = json.load(json_file)
for item in data:
if (item['type'] == 'validation'):
for data_item in item['scans']:
removed = np.asarray(data_item['removed'])
ref = item['reference']
dd = data_item['reference']
pathS = sys.path[0] + '/' + ref
pathR = sys.path[0] + '/' + dd
print (dd)
compute(pathS,pathR)
print ("rescan ",pathR," is completed")