#!/usr/bin/python
"""
# ==============================
# flowlib.py
# library for optical flow processing
# Author: Ruoteng Li
# Date: 6th Aug 2016
# ==============================
"""
import png
import numpy as np
import matplotlib.colors as cl
import matplotlib.pyplot as plt
from PIL import Image
UNKNOWN_FLOW_THRESH = 1e7
SMALLFLOW = 0.0
LARGEFLOW = 1e8
"""
=============
Flow Section
=============
"""
def show_flow(filename):
"""
visualize optical flow map using matplotlib
:param filename: optical flow file
:return: None
"""
flow = read_flow(filename)
img = flow_to_image(flow)
plt.imshow(img)
plt.show()
def visualize_flow(flow, mode='Y'):
"""
this function visualize the input flow
:param flow: input flow in array
:param mode: choose which color mode to visualize the flow (Y: Ccbcr, RGB: RGB color)
:return: None
"""
if mode == 'Y':
# Ccbcr color wheel
img = flow_to_image(flow)
plt.imshow(img)
plt.show()
elif mode == 'RGB':
(h, w) = flow.shape[0:2]
du = flow[:, :, 0]
dv = flow[:, :, 1]
valid = flow[:, :, 2]
max_flow = max(np.max(du), np.max(dv))
img = np.zeros((h, w, 3), dtype=np.float64)
# angle layer
img[:, :, 0] = np.arctan2(dv, du) / (2 * np.pi)
# magnitude layer, normalized to 1
img[:, :, 1] = np.sqrt(du * du + dv * dv) * 8 / max_flow
# phase layer
img[:, :, 2] = 8 - img[:, :, 1]
# clip to [0,1]
small_idx = img[:, :, 0:3] < 0
large_idx = img[:, :, 0:3] > 1
img[small_idx] = 0
img[large_idx] = 1
# convert to rgb
img = cl.hsv_to_rgb(img)
# remove invalid point
img[:, :, 0] = img[:, :, 0] * valid
img[:, :, 1] = img[:, :, 1] * valid
img[:, :, 2] = img[:, :, 2] * valid
# show
plt.imshow(img)
plt.show()
return None
def read_flow(filename):
"""
read optical flow from Middlebury .flo file
:param filename: name of the flow file
:return: optical flow data in matrix
"""
f = open(filename, 'rb')
try:
magic = np.fromfile(f, np.float32, count=1)[0] # For Python3.x
except:
magic = np.fromfile(f, np.float32, count=1) # For Python2.x
data2d = None
if 202021.25 != magic:
print('Magic number incorrect. Invalid .flo file')
else:
w = np.fromfile(f, np.int32, count=1)
h = np.fromfile(f, np.int32, count=1)
#print("Reading %d x %d flo file" % (h, w))
data2d = np.fromfile(f, np.float32, count=2 * w[0] * h[0])
# reshape data into 3D array (columns, rows, channels)
data2d = np.resize(data2d, (h[0], w[0], 2))
f.close()
return data2d
def read_flow_png(flow_file):
"""
Read optical flow from KITTI .png file
:param flow_file: name of the flow file
:return: optical flow data in matrix
"""
flow_object = png.Reader(filename=flow_file)
flow_direct = flow_object.asDirect()
flow_data = list(flow_direct[2])
(w, h) = flow_direct[3]['size']
flow = np.zeros((h, w, 3), dtype=np.float64)
for i in range(len(flow_data)):
flow[i, :, 0] = flow_data[i][0::3]
flow[i, :, 1] = flow_data[i][1::3]
flow[i, :, 2] = flow_data[i][2::3]
invalid_idx = (flow[:, :, 2] == 0)
flow[:, :, 0:2] = (flow[:, :, 0:2] - 2 ** 15) / 64.0
flow[invalid_idx, 0] = 0
flow[invalid_idx, 1] = 0
return flow
def write_flow(flow, filename):
"""
write optical flow in Middlebury .flo format
:param flow: optical flow map
:param filename: optical flow file path to be saved
:return: None
"""
f = open(filename, 'wb')
magic = np.array([202021.25], dtype=np.float32)
(height, width) = flow.shape[0:2]
w = np.array([width], dtype=np.int32)
h = np.array([height], dtype=np.int32)
magic.tofile(f)
w.tofile(f)
h.tofile(f)
flow.tofile(f)
f.close()
def segment_flow(flow):
h = flow.shape[0]
w = flow.shape[1]
u = flow[:, :, 0]
v = flow[:, :, 1]
idx = ((abs(u) > LARGEFLOW) | (abs(v) > LARGEFLOW))
idx2 = (abs(u) == SMALLFLOW)
class0 = (v == 0) & (u == 0)
u[idx2] = 0.00001
tan_value = v / u
class1 = (tan_value < 1) & (tan_value >= 0) & (u > 0) & (v >= 0)
class2 = (tan_value >= 1) & (u >= 0) & (v >= 0)
class3 = (tan_value < -1) & (u <= 0) & (v >= 0)
class4 = (tan_value < 0) & (tan_value >= -1) & (u < 0) & (v >= 0)
class8 = (tan_value >= -1) & (tan_value < 0) & (u > 0) & (v <= 0)
class7 = (tan_value < -1) & (u >= 0) & (v <= 0)
class6 = (tan_value >= 1) & (u <= 0) & (v <= 0)
class5 = (tan_value >= 0) & (tan_value < 1) & (u < 0) & (v <= 0)
seg = np.zeros((h, w))
seg[class1] = 1
seg[class2] = 2
seg[class3] = 3
seg[class4] = 4
seg[class5] = 5
seg[class6] = 6
seg[class7] = 7
seg[class8] = 8
seg[class0] = 0
seg[idx] = 0
return seg
def flow_error(tu, tv, u, v):
"""
Calculate average end point error
:param tu: ground-truth horizontal flow map
:param tv: ground-truth vertical flow map
:param u: estimated horizontal flow map
:param v: estimated vertical flow map
:return: End point error of the estimated flow
"""
smallflow = 0.0
'''
stu = tu[bord+1:end-bord,bord+1:end-bord]
stv = tv[bord+1:end-bord,bord+1:end-bord]
su = u[bord+1:end-bord,bord+1:end-bord]
sv = v[bord+1:end-bord,bord+1:end-bord]
'''
stu = tu[:]
stv = tv[:]
su = u[:]
sv = v[:]
idxUnknow = (abs(stu) > UNKNOWN_FLOW_THRESH) | (abs(stv) > UNKNOWN_FLOW_THRESH)
stu[idxUnknow] = 0
stv[idxUnknow] = 0
su[idxUnknow] = 0
sv[idxUnknow] = 0
ind2 = [(np.absolute(stu) > smallflow) | (np.absolute(stv) > smallflow)]
index_su = su[ind2]
index_sv = sv[ind2]
an = 1.0 / np.sqrt(index_su ** 2 + index_sv ** 2 + 1)
un = index_su * an
vn = index_sv * an
index_stu = stu[ind2]
index_stv = stv[ind2]
tn = 1.0 / np.sqrt(index_stu ** 2 + index_stv ** 2 + 1)
tun = index_stu * tn
tvn = index_stv * tn
'''
angle = un * tun + vn * tvn + (an * tn)
index = [angle == 1.0]
angle[index] = 0.999
ang = np.arccos(angle)
mang = np.mean(ang)
mang = mang * 180 / np.pi
'''
epe = np.sqrt((stu - su) ** 2 + (stv - sv) ** 2)
epe = epe[ind2]
mepe = np.mean(epe)
return mepe
def flow_to_image(flow, display=False):
"""
Convert flow into middlebury color code image
:param flow: optical flow map
:return: optical flow image in middlebury color
"""
u = flow[:, :, 0]
v = flow[:, :, 1]
maxu = -999.
maxv = -999.
minu = 999.
minv = 999.
idxUnknow = (abs(u) > UNKNOWN_FLOW_THRESH) | (abs(v) > UNKNOWN_FLOW_THRESH)
u[idxUnknow] = 0
v[idxUnknow] = 0
maxu = max(maxu, np.max(u))
minu = min(minu, np.min(u))
maxv = max(maxv, np.max(v))
minv = min(minv, np.min(v))
rad = np.sqrt(u ** 2 + v ** 2)
maxrad = max(-1, np.max(rad))
if display:
print("max flow: %.4f\nflow range:\nu = %.3f .. %.3f\nv = %.3f .. %.3f" % (maxrad, minu,maxu, minv, maxv))
u = u/(maxrad + np.finfo(float).eps)
v = v/(maxrad + np.finfo(float).eps)
img = compute_color(u, v)
idx = np.repeat(idxUnknow[:, :, np.newaxis], 3, axis=2)
img[idx] = 0
return np.uint8(img)
def evaluate_flow_file(gt, pred):
"""
evaluate the estimated optical flow end point error according to ground truth provided
:param gt: ground truth file path
:param pred: estimated optical flow file path
:return: end point error, float32
"""
# Read flow files and calculate the errors
gt_flow = read_flow(gt) # ground truth flow
eva_flow = read_flow(pred) # predicted flow
# Calculate errors
average_pe = flow_error(gt_flow[:, :, 0], gt_flow[:, :, 1], eva_flow[:, :, 0], eva_flow[:, :, 1])
return average_pe
def evaluate_flow(gt_flow, pred_flow):
"""
gt: ground-truth flow
pred: estimated flow
"""
average_pe = flow_error(gt_flow[:, :, 0], gt_flow[:, :, 1], pred_flow[:, :, 0], pred_flow[:, :, 1])
return average_pe
"""
==============
Disparity Section
==============
"""
def read_disp_png(file_name):
"""
Read optical flow from KITTI .png file
:param file_name: name of the flow file
:return: optical flow data in matrix
"""
image_object = png.Reader(filename=file_name)
image_direct = image_object.asDirect()
image_data = list(image_direct[2])
(w, h) = image_direct[3]['size']
channel = len(image_data[0]) / w
flow = np.zeros((h, w, channel), dtype=np.uint16)
for i in range(len(image_data)):
for j in range(channel):
flow[i, :, j] = image_data[i][j::channel]
return flow[:, :, 0] / 256
def disp_to_flowfile(disp, filename):
"""
Read KITTI disparity file in png format
:param disp: disparity matrix
:param filename: the flow file name to save
:return: None
"""
f = open(filename, 'wb')
magic = np.array([202021.25], dtype=np.float32)
(height, width) = disp.shape[0:2]
w = np.array([width], dtype=np.int32)
h = np.array([height], dtype=np.int32)
empty_map = np.zeros((height, width), dtype=np.float32)
data = np.dstack((disp, empty_map))
magic.tofile(f)
w.tofile(f)
h.tofile(f)
data.tofile(f)
f.close()
"""
==============
Image Section
==============
"""
def read_image(filename):
"""
Read normal image of any format
:param filename: name of the image file
:return: image data in matrix uint8 type
"""
img = Image.open(filename)
im = np.array(img)
return im
def warp_image(im, flow):
"""
Use optical flow to warp image to the next
:param im: image to warp
:param flow: optical flow
:return: warped image
"""
from scipy import interpolate
image_height = im.shape[0]
image_width = im.shape[1]
flow_height = flow.shape[0]
flow_width = flow.shape[1]
n = image_height * image_width
(iy, ix) = np.mgrid[0:image_height, 0:image_width]
(fy, fx) = np.mgrid[0:flow_height, 0:flow_width]
fx += flow[:,:,0]
fy += flow[:,:,1]
mask = np.logical_or(fx <0 , fx > flow_width)
mask = np.logical_or(mask, fy < 0)
mask = np.logical_or(mask, fy > flow_height)
fx = np.minimum(np.maximum(fx, 0), flow_width)
fy = np.minimum(np.maximum(fy, 0), flow_height)
points = np.concatenate((ix.reshape(n,1), iy.reshape(n,1)), axis=1)
xi = np.concatenate((fx.reshape(n, 1), fy.reshape(n,1)), axis=1)
warp = np.zeros((image_height, image_width, im.shape[2]))
for i in range(im.shape[2]):
channel = im[:, :, i]
plt.imshow(channel, cmap='gray')
values = channel.reshape(n, 1)
new_channel = interpolate.griddata(points, values, xi, method='cubic')
new_channel = np.reshape(new_channel, [flow_height, flow_width])
new_channel[mask] = 1
warp[:, :, i] = new_channel.astype(np.uint8)
return warp.astype(np.uint8)
"""
==============
Others
==============
"""
def scale_image(image, new_range):
"""
Linearly scale the image into desired range
:param image: input image
:param new_range: the new range to be aligned
:return: image normalized in new range
"""
min_val = np.min(image).astype(np.float32)
max_val = np.max(image).astype(np.float32)
min_val_new = np.array(min(new_range), dtype=np.float32)
max_val_new = np.array(max(new_range), dtype=np.float32)
scaled_image = (image - min_val) / (max_val - min_val) * (max_val_new - min_val_new) + min_val_new
return scaled_image.astype(np.uint8)
def compute_color(u, v):
"""
compute optical flow color map
:param u: optical flow horizontal map
:param v: optical flow vertical map
:return: optical flow in color code
"""
[h, w] = u.shape
img = np.zeros([h, w, 3])
nanIdx = np.isnan(u) | np.isnan(v)
u[nanIdx] = 0
v[nanIdx] = 0
colorwheel = make_color_wheel()
ncols = np.size(colorwheel, 0)
rad = np.sqrt(u**2+v**2)
a = np.arctan2(-v, -u) / np.pi
fk = (a+1) / 2 * (ncols - 1) + 1
k0 = np.floor(fk).astype(int)
k1 = k0 + 1
k1[k1 == ncols+1] = 1
f = fk - k0
for i in range(0, np.size(colorwheel,1)):
tmp = colorwheel[:, i]
col0 = tmp[k0-1] / 255
col1 = tmp[k1-1] / 255
col = (1-f) * col0 + f * col1
idx = rad <= 1
col[idx] = 1-rad[idx]*(1-col[idx])
notidx = np.logical_not(idx)
col[notidx] *= 0.75
img[:, :, i] = np.uint8(np.floor(255 * col*(1-nanIdx)))
return img
def make_color_wheel():
"""
Generate color wheel according Middlebury color code
:return: Color wheel
"""
RY = 15
YG = 6
GC = 4
CB = 11
BM = 13
MR = 6
ncols = RY + YG + GC + CB + BM + MR
colorwheel = np.zeros([ncols, 3])
col = 0
# RY
colorwheel[0:RY, 0] = 255
colorwheel[0:RY, 1] = np.transpose(np.floor(255*np.arange(0, RY) / RY))
col += RY
# YG
colorwheel[col:col+YG, 0] = 255 - np.transpose(np.floor(255*np.arange(0, YG) / YG))
colorwheel[col:col+YG, 1] = 255
col += YG
# GC
colorwheel[col:col+GC, 1] = 255
colorwheel[col:col+GC, 2] = np.transpose(np.floor(255*np.arange(0, GC) / GC))
col += GC
# CB
colorwheel[col:col+CB, 1] = 255 - np.transpose(np.floor(255*np.arange(0, CB) / CB))
colorwheel[col:col+CB, 2] = 255
col += CB
# BM
colorwheel[col:col+BM, 2] = 255
colorwheel[col:col+BM, 0] = np.transpose(np.floor(255*np.arange(0, BM) / BM))
col += + BM
# MR
colorwheel[col:col+MR, 2] = 255 - np.transpose(np.floor(255 * np.arange(0, MR) / MR))
colorwheel[col:col+MR, 0] = 255
return colorwheel
