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CV_algs.py
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CV_algs.py
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import cv2
import numpy as np
import matplotlib.pyplot as plt
import os
import collections
from scipy.optimize import fsolve
import math
from Configs.configs import PARAMS,IMG_SHAPE,WINDOW_HEITH,WINDOW_PARAMS,CLASSES,ROADBED_PARAMS,SIDES, HIST_PARAMS
def plot_images(data, layout='row', cols=2, figsize=(20, 12)):
'''
Utility function for plotting images
:param data [(ndarray, string)]: List of data to display, [(image, title)]
:param layout (string): Layout, row-wise or column-wise
:param cols (number): Number of columns per row
:param figsize (number, number): Tuple indicating figure size
'''
rows = math.ceil(len(data) / cols)
f, ax = plt.subplots(figsize=figsize)
if layout == 'row':
for idx, d in enumerate(data):
img, title = d
plt.subplot(rows, cols, idx+1)
plt.title(title, fontsize=20)
#plt.axis('off')
if len(img.shape) == 2:
plt.imshow(img, cmap='gray')
#print(img.max())
elif len(img.shape) == 3:
plt.imshow(img)
elif layout == 'col':
counter = 0
for r in range(rows):
for c in range(cols):
img, title = data[r + rows*c]
nb_channels = len(img.shape)
plt.subplot(rows, cols, counter+1)
plt.title(title, fontsize=20)
#plt.axis('off')
if len(img.shape) == 2:
plt.imshow(img, cmap='gray')
elif len(img.shape) == 3:
plt.imshow(img)
counter += 1
plt.show()
return ax
def warp_image(img, warp_shape, src, dst):
'''
Performs perspective transformation (PT)
:param img (ndarray): Image
:param warp_shape: Shape of the warped image
:param src (ndarray): Source points
:param dst (ndarray): Destination points
:return : Tuple (Transformed image, PT matrix, PT inverse matrix)
'''
# Get the perspective transformation matrix and its inverse
M = cv2.getPerspectiveTransform(src, dst)
invM = cv2.getPerspectiveTransform(dst, src)
# Warp the image
warped = cv2.warpPerspective(img, M, warp_shape, flags=cv2.INTER_LINEAR)
return warped, M, invM
def preprocess_image(img, visualise=False):
'''
Pre-processes an image. Steps include:
1. Distortion correction FIX
2. Perspective Transformation
3. ROI crop FIX
:param img (ndarray): Original Image
:param visualise (boolean): Boolean flag for visualisation
:return : Pre-processed image, (PT matrix, PT inverse matrix)
'''
k = 209/251
b = 37958/251
f = lambda x:k*x + b
int(f(400))
#k2 = -207/251
k2 = -k
b2 = 162409/251
f2 = lambda x:k2*x + b2
f2(400)
ysize = img.shape[0]
xsize = img.shape[1]
# 1. Distortion correction
undist = img
# 2. Perspective transformation
range_vision = PARAMS["RANGE_OF_VISION"]
src = np.float32([
(f(range_vision),range_vision), #(436,342),
(f2(range_vision),range_vision),#(365,342),
(f2(600),600), #(158,593),
(f(600),600), #(645,593),
])
dx = 300
dst = np.float32([
(xsize - dx, 0),
(dx, 0),
(dx, ysize),
(xsize - dx, ysize)
])
warped, M, invM = warp_image(undist, (xsize, ysize), src, dst)
# 4. Visualise the transformation
if visualise:
img_copy = np.copy(img)
cv2.polylines(img_copy, [np.int32(src)], True, 1, 3)
plot_images([
(img_copy, 'Original Image'),
(warped, "warped")
])
return warped, (M, invM)
def get_poly_points(fit):
'''
Get the points for the left lane/ right lane defined by the polynomial coeff's 'left_fit'
and 'right_fit'
:param fit (ndarray): Coefficients for the polynomial that defines the lane line
: return (Tuple(ndarray, ndarray)): x-y coordinates for the lane line
'''
ysize, xsize = IMG_SHAPE
# Get the points for the entire height of the image
plot_y = np.linspace(0, ysize-1, ysize)
#plot_x = fit[0] * plot_y**2 + fit[1] * plot_y + fit[2]
polynom = np.poly1d(fit)
plot_x = polynom(plot_y)
# But keep only those points that lie within the image
plot_x[plot_x < 0] = 0
plot_x[plot_x > xsize] = xsize
plot_y = np.linspace(ysize - len(plot_x), ysize - 1, len(plot_x))
return plot_x.astype(np.int64), plot_y.astype(np.int64)
def check_validity(left_fit, right_fit, diagnostics=False):
'''
Determine the validity of lane lines represented by a set of second order polynomial coefficients
:param left_fit (ndarray): Coefficients for the 2nd order polynomial that defines the left lane line
:param right_fit (ndarray): Coefficients for the 2nd order polynomial that defines the right lane line
:param diagnostics (boolean): Boolean flag for logging
: return (boolean)
'''
if left_fit is None or right_fit is None:
if(diagnostics):
print(f"Exception: left_fit is {left_fit}, right_fit is {right_fit}")
return False
plot_xleft, plot_yleft= get_poly_points(left_fit)
plot_xright, plot_yright =get_poly_points(right_fit)
# Check whether the two lines lie within a plausible distance from one another for three distinct y-values
y1 = IMG_SHAPE[0] - 1 # Bottom
y2 = IMG_SHAPE[0] - int(min(len(plot_yleft), len(plot_yright)) * PARAMS['VALIDATE_Y2']) # For the 2nd and 3rd, take values between y1 and the top-most available value.
y3 = IMG_SHAPE[0] - int(min(len(plot_yleft), len(plot_yright)) * PARAMS['VALIDATE_Y3'])
# Compute the respective x-values for both lines
x1l = left_fit[0] * (y1**2) + left_fit[1] * y1 + left_fit[2]
x2l = left_fit[0] * (y2**2) + left_fit[1] * y2 + left_fit[2]
x3l = left_fit[0] * (y3**2) + left_fit[1] * y3 + left_fit[2]
x1r = right_fit[0] * (y1**2) + right_fit[1] * y1 + right_fit[2]
x2r = right_fit[0] * (y2**2) + right_fit[1] * y2 + right_fit[2]
x3r = right_fit[0] * (y3**2) + right_fit[1] * y3 + right_fit[2]
# Compute the L1 norms
x1_diff = abs(x1l - x1r)
x2_diff = abs(x2l - x2r)
x3_diff = abs(x3l - x3r)
# Define the threshold values for each of the three points #FIT
min_dist_y1 = PARAMS["min_dist_y1"] # 510 # 530
max_dist_y1 = PARAMS["max_dist_y1"] # 750 # 660
min_dist_y2 = PARAMS["min_dist_y2"]
max_dist_y2 = PARAMS["max_dist_y2"] # 660
min_dist_y3 = PARAMS["min_dist_y3"]
max_dist_y3 = PARAMS["max_dist_y3"] # 660
if (x1_diff < min_dist_y1) | (x1_diff > max_dist_y1) | \
(x2_diff < min_dist_y2) | (x2_diff > max_dist_y2) | \
(x3_diff < min_dist_y3) | (x3_diff > max_dist_y3):
if diagnostics:
print("Violated distance criterion: " +
"x1_diff == {:.2f}, x2_diff == {:.2f}, x3_diff == {:.2f}".format(x1_diff, x2_diff, x3_diff))
return False
# Check whether the line slopes are similar for two distinct y-values
# x = Ay**2 + By + C
# dx/dy = 2Ay + B
y1left_dx = 2 * left_fit[0] * y1 + left_fit[1]
y3left_dx = 2 * left_fit[0] * y3 + left_fit[1]
y1right_dx = 2 * right_fit[0] * y1 + right_fit[1]
y3right_dx = 2 * right_fit[0] * y3 + right_fit[1]
# Compute the L1-norm
norm1 = abs(y1left_dx - y1right_dx)
norm2 = abs(y3left_dx - y3right_dx)
# if diagnostics: print( norm1, norm2)
# Define the L1 norm threshold
thresh = PARAMS['TANGENT']
if (norm1 >= thresh) | (norm2 >= thresh):
if diagnostics:
print("Violated tangent criterion: " +
"norm1 == {:.3f}, norm2 == {:.3f} (thresh == {}).".format(norm1, norm2, thresh))
return False
return True
def polyfit_sliding_window(binary, cache, roadbed_fit, out = None,visualise=False, diagnostics=False):
'''
Detect lane lines in a thresholded binary image using the sliding window technique
:param binary (ndarray): Thresholded binary image
:cahe = [deque(maxlen = ..) *2] contains the last several linefits
:param visualise (boolean): Boolean flag for visualisation
:param diagnositics (boolean): Boolean flag for logging
'''
# Step 1: Compute the histogram along all the columns in the lower half of the image.
# The two most prominent peaks in this histogram will be good indicators of the
# x-position of the base of the lane lines
if visualise and out is None:
out = np.dstack((binary, binary, binary)) * 255
histogram = None
cutoffs = [int(binary.shape[0] //3)]
for cutoff in cutoffs:
histogram = np.sum(binary[cutoff:,:], axis=0)
if histogram.max() > 0:
break
if histogram.max() == 0:
print('Unable to detect lane lines in this frame. Trying another frame!')
return False,out, np.array([None,None]),(CLASSES.NONE,CLASSES.NONE)
# Find the peak of the left and right halves of the histogram
# They will be the starting points for the left and right lines
hist_roi = np.zeros_like(histogram)
hist_roi[HIST_PARAMS['roi_x_start']:HIST_PARAMS['roi_x_end']] = histogram[HIST_PARAMS['roi_x_start']:HIST_PARAMS['roi_x_end']]
midpoint = hist_roi.shape[0] // 2
first_max = np.argmax(hist_roi)
leftx_base = None
rightx_base = None
if(first_max < midpoint):
leftx_base = first_max
#new_midpoint = max(leftx_base + PARAMS['min_dist_y1'],midpoint)
start = leftx_base + PARAMS['min_dist_y1']
end = leftx_base + PARAMS['max_dist_y1']
rightx_base = np.argmax(hist_roi[start:end]) + start
else:
rightx_base = first_max
#new_midpoint = min(rightx_base - PARAMS['min_dist_y1'],midpoint)
start = rightx_base - PARAMS['max_dist_y1']
end = rightx_base - PARAMS['min_dist_y1']
leftx_base = np.argmax(hist_roi[start:end]) + start
if diagnostics:
plt.imshow(binary, cmap="gray")
plt.plot(histogram, 'm', linewidth=4.0)
plt.plot((midpoint, midpoint), (0, IMG_SHAPE[0]), 'c')
plt.plot((0, IMG_SHAPE[1]), (cutoff, cutoff), 'c')
plt.plot((HIST_PARAMS['roi_x_start'],HIST_PARAMS['roi_x_start']),(0, IMG_SHAPE[0]),'b')
plt.plot((HIST_PARAMS['roi_x_end'],HIST_PARAMS['roi_x_end']),(0, IMG_SHAPE[0]),'b')
plt.plot((leftx_base,leftx_base),(0, IMG_SHAPE[0]),'g')
plt.plot((rightx_base,rightx_base),(0, IMG_SHAPE[0]),'g')
#####
# Perform line search and fit through the base points
left_fit, left_class, right_fit, right_class = None,None,None,None
if leftx_base is not None:
left_fit, left_class,left_pCounts = get_Class_LaneInds(binary,leftx_base,out)
if rightx_base is not None:
right_fit, right_class,right_pCounts = get_Class_LaneInds(binary,rightx_base,out)
# close scope with roadbed if needs
if left_fit is None:
left_fit = roadbed_fit[0]
left_class = CLASSES.SOLID
if right_fit is None:
right_fit = roadbed_fit[1]
right_class = CLASSES.SOLID
### check line collision
if (left_fit is not None) and (right_fit is not None):
diff = np.poly1d(left_fit) - np.poly1d(right_fit)
solution,_,collision,_ = fsolve(diff,IMG_SHAPE[0]//2,full_output=True)
if collision == 1:
if 0 < solution and solution < IMG_SHAPE[1]:
if(left_pCounts < right_pCounts):
left_fit = None
left_class = CLASSES.NONE
else:
right_fit = None
right_class = CLASSES.NONE
#####
# Validate detected lane lines
valid = check_validity(left_fit, right_fit, diagnostics=diagnostics)
ret = False
if not valid:
# If the detected lane lines are NOT valid:
# 1. Compute the lane lines as an average of the previously detected lines
# from the cache and flag this detection cycle as a failure by setting ret=False
# 2. Else, if cache is empty, return
if len(cache[0]) == 0 or len(cache[1]) == 0:
if diagnostics: print('WARNING: Unable to detect lane lines in this frame.')
return ret, out, [left_fit, right_fit] , (left_class,right_class)
if diagnostics:
print(f"Compute the lane lines as an average of the previously detected lines, Cache len :{len(cache[0])}")
avg_params = np.mean(cache, axis = 1)
left_fit, right_fit = avg_params[0], avg_params[1]
cache[0].popleft()
cache[1].popleft()
else:
cache[0].append(left_fit)
cache[1].append(right_fit)
ret = True
# Plot the fitted polynomial
if visualise:
plot_xleft, plot_yleft = get_poly_points(left_fit)
plot_xright, plot_yright = get_poly_points(right_fit)
left_poly_pts = np.array([np.transpose(np.vstack([plot_xleft, plot_yleft]))])
right_poly_pts = np.array([np.transpose(np.vstack([plot_xright, plot_yright]))])
cv2.polylines(out, np.int32([left_poly_pts]), isClosed=False, color=(200,255,0), thickness=4)
cv2.polylines(out, np.int32([right_poly_pts]), isClosed=False, color=(200,255,0), thickness=4)
return ret, out, np.array([left_fit, right_fit]) , (left_class,right_class)
def isBaseValid(new_window_base):
return (WINDOW_PARAMS.margin.value<= new_window_base) and (new_window_base <= IMG_SHAPE[1] - WINDOW_PARAMS.margin.value)
def get_Class_LaneInds(binary,x_base, out = None):
nonzero = binary.nonzero()
nonzerox = nonzero[1]
nonzeroy = nonzero[0] # np.array is mondatory
# Current positions to be updated for each window
x_current = x_base
lane_inds = []
classes = []
dx = collections.deque([0, 0, 0], maxlen=3)
isWindow_movedOut = False
for window in range(WINDOW_PARAMS.nb_windows.value):
win_x_low = x_current - WINDOW_PARAMS.margin.value
win_x_high = x_current + WINDOW_PARAMS.margin.value
win_y_low = IMG_SHAPE[0] - (1 + window) * WINDOW_HEITH
win_y_high = IMG_SHAPE[0] - window * WINDOW_HEITH
# Identify the nonzero pixels in x and y within the window
good_inds = ((nonzeroy >= win_y_low) & (nonzeroy <= win_y_high)
& (nonzerox >= win_x_low) & (nonzerox <= win_x_high)).nonzero()[0]
classes.append(classify(binary,win_x_high, win_y_high, win_x_low, win_y_low))
# print(classes[window])
if(classes[window] != CLASSES.NONE) and (classes[window] != CLASSES.EMPY):
if len(good_inds) > WINDOW_PARAMS.minpix.value:
#Fit ax +b line in window
x = nonzerox[good_inds]
y = nonzeroy[good_inds]
fit, residual, *_ = np.polyfit(y, x, 1,full=True)
fit = np.poly1d(fit)
residual = max(residual,default=0)
#If residual too high then:
max_res = WINDOW_PARAMS.MAX_RESIDUAL_FITLINE_SOLID_SOLID.value if \
classes[window] == CLASSES.SOLID_SOLID or\
classes[window] == CLASSES.DASH_SOLID or \
classes[window] == CLASSES.SOLID_DASH\
else WINDOW_PARAMS.MAX_RESIDUAL_FITLINE.value
# print(residual)
if(residual > max_res):
classes[window] = CLASSES.NONE
# FIX FIX FIX add diagnostics
#print(f"resid warning{residual}")
x_new = x_current + int(np.mean(dx))
if isBaseValid(x_new):
x_current = x_new
else:
isWindow_movedOut = True
else:
lane_inds.append(good_inds)
#next base is meadian of Y on the next window
x_new = int(fit(win_y_low - WINDOW_HEITH / 2))
if isBaseValid(x_new):
dx.append(x_new - x_current)
x_current = x_new
else:
isWindow_movedOut = True
else:
x_new = x_current + int(np.mean(dx))
if isBaseValid(x_new):
x_current = x_new
else:
isWindow_movedOut = True
if (out is not None):
# Draw windows for visualisation
cv2.rectangle(out, (win_x_low, win_y_low), (win_x_high, win_y_high),\
WINDOW_PARAMS.win_colors.value[classes[window]], 2)
if (isWindow_movedOut):
break
if (out is not None):
x = 20
y = 20
dy = 30
for CLASS in CLASSES:
out = cv2.putText(out,CLASS.name,(x,y),cv2.FONT_HERSHEY_SIMPLEX,1,WINDOW_PARAMS.win_colors.value[CLASS])
y +=dy
classes = np.array(classes)
count_of_nonNone = np.sum(np.logical_and((np.array(classes) !=CLASSES.EMPY), (np.array(classes) !=CLASSES.NONE)))
if count_of_nonNone < PARAMS["MIN_WINDOWS_TO_DETECT"]:
return None, CLASSES.NONE, 0
final_class = classify_by_seq(classes)
if (len(lane_inds) == 0):
return None, final_class, 0
lane_inds = np.concatenate(lane_inds)
# Extract pixel positions for the left and right lane lines
x = nonzerox[lane_inds]
y = nonzeroy[lane_inds]
if (out is not None):
# Color the detected pixels for each lane line
out[y, x] = [255, 0, 0]
if len(x) >= WINDOW_PARAMS.min_lane_pts.value:
return np.polyfit(y, x, 2), final_class,len(x)
else:
return None, final_class, len(x)
def classify(binary,x_high, y_high, x_low, y_low):
new_binary = np.array([[binary[i,j] ^ binary[i,j + 1] for j in range(x_low,x_high - 1)] for i in range(y_low,y_high)])
summs = new_binary.sum(axis=1)
MAX = max(summs,default=0)
MIN = min(summs,default=0)
#MEAN = np.mean(summs) if MAX != 0 else 0
MaxSums = summs.copy()
#FIX FIX FIX
while (np.sum(MaxSums == MAX) < len(summs) // WINDOW_PARAMS.min_share_toClassify.value) and MAX > 0:
MaxSums[MaxSums == MAX] = 0
MAX = max(MaxSums,default=0)
MinSums = summs.copy()
while (np.sum(MinSums == MIN) < len(summs) // WINDOW_PARAMS.min_share_toClassify.value) and MIN < MAX:
MinSums[MinSums == MIN] = MAX
MIN = min(MinSums,default = MAX)
MEAN = int((MAX + MIN) //2)
#print("MINMAX ",MIN,MAX,MEAN)
#TODO #######################################
#add sigma
carrent_class = CLASSES.EMPY
#if(SHIFT > 80):
# current_class = CLASSES["NONE"]
if(MAX == 0):
current_class = CLASSES.EMPY
elif(MAX == 2) and (MIN == 0):
current_class = CLASSES.DASH
elif(MEAN == 2):
current_class = CLASSES.SOLID
elif(MEAN == 4):
current_class = CLASSES.SOLID_SOLID
elif(MAX == 4) and (MIN == 2):
current_class = CLASSES.DASH_SOLID
else:
current_class = CLASSES.NONE
return current_class
def classify_by_seq(windows):
arr = np.insert(windows,[0,len(windows)],CLASSES.NONE)
Count_N = np.count_nonzero(arr == CLASSES.NONE)
Count_E = np.count_nonzero(arr == CLASSES.EMPY)
# DASH if prev EMPTY next SOLID and vice versa
length = len(arr)
for i in range(1,length):
prev_class = arr[i-1]
current_class = arr[i]
if (current_class == CLASSES.SOLID and prev_class == CLASSES.EMPY):
arr[i] = CLASSES.DASH
elif(current_class == CLASSES.EMPY and prev_class == CLASSES.SOLID):
arr[i - 1] = CLASSES.DASH
# for i in range(1,length-1):
# prev_class = arr[i-1]
# current_class = arr[i]
# if (current_class == CLASSES.DASH):
# Strong line if it is n times in a row
Count_of_SS_in_row = max(np.diff(np.where(arr != CLASSES.SOLID_SOLID)[0]) - 1,default=0)
if(Count_of_SS_in_row > WINDOW_PARAMS.MAX_SS_WIN_TO_DETECT_SS.value):
return CLASSES.SOLID_SOLID
Count_of_S_in_row = max(np.diff(np.where(arr != CLASSES.SOLID)[0]) - 1,default=0)
if(Count_of_S_in_row > WINDOW_PARAMS.MAX_SOLID_WIN_TO_DETECT_SOLID.value):
return CLASSES.SOLID
# dash if Empty > n but not more then k
Count_of_E_in_row = max(np.diff(np.where(arr != CLASSES.EMPY)[0]) - 1,default=0)
if(Count_E >= WINDOW_PARAMS.MIN_E_WIN_TO_DETECT_D.value) and \
len(arr) - Count_E -Count_N >= WINDOW_PARAMS.MAX_NO_E_WIN_TO_DETECT_D.value:
return CLASSES.DASH
#set MAX priority class if it is here at least n times
MAX = CLASSES.EMPY
for CLASS in CLASSES:
counts = np.count_nonzero(arr == CLASS)
#FIX
if counts >= 2:
if CLASS.value > MAX.value:
MAX = CLASS
# counts = set()
# MAX = CLASSES.EMPY
# for element in arr:
# if(element in counts):
# if (element.value > MAX.value):
# MAX = element
# else:
# counts.add(element)
# print(MAX)
# print("NONONONONE",Count_of_S_in_row)
return MAX
def roadbed_line_fit(binary,side):
binary = binary[ROADBED_PARAMS.slice_of_Y_min.value:ROADBED_PARAMS.slice_of_Y_max.value,:]
target = None
if(side == SIDES.left):
f = lambda y: 0.71 * y - 175
fun = lambda x,y: x > f(y)
target = 0
elif side == SIDES.right:
f = lambda y: -0.71 * y + 800 + 175
fun = lambda x,y: x < f(y)
target =-1
y_points = []
x_points = []
for i,row in enumerate(binary):
nonzero = np.nonzero(row)
if(len(nonzero[0]) > 0):
y = i + ROADBED_PARAMS.slice_of_Y_min.value
x = nonzero[0][target] #+ adjustment(y,side)
y_points.append(y)
x_points.append(x)
# if(side == SIDES.right):
# print(i,x,y,fun(x,y))
# if(fun(x,y)):
# y_points.append(y)
# x_points.append(x)
# for point in zip(x_points,y_points):
# print(side,point)
polyfit = None
if (len(y_points) >= ROADBED_PARAMS.min_lane_pts.value):
polyfit = np.polyfit(y_points, x_points, 4)
return polyfit
def adjustment(y,side):
y_max = 600
y_min = ROADBED_PARAMS.adjustment_y_min.value
if(y<=y_max and y>= y_min):
a = ROADBED_PARAMS.adjustment_width.value
k = a/(y_max - y_min)
b = a - k* y_max
res = k* y + b
if(side == SIDES.right):
return res
elif(side == SIDES.left):
return -res
return 0
def roadbed_processing(bin_roadbed, side,out = None, visualise = False):
#line fit
polyfit = roadbed_line_fit(bin_roadbed,side)
#plotting
if visualise:
if out is None:
out = np.dstack((bin_roadbed,bin_roadbed,bin_roadbed)) * 255
if polyfit is not None:
plot_x, plot_y = get_poly_points(polyfit)
poly_pts = np.array([np.transpose(np.vstack([plot_x, plot_y]))])
cv2.polylines(out, np.int32([poly_pts]), isClosed=False, color=(200,255,155), thickness=4)
success = True
if(polyfit is None):
success = False
return success, out, polyfit
def draw(img, warped, invM, poly_param, color= (0,220, 110),lineColor =(255, 255, 255)):
'''
Utility function to draw the lane boundaries and numerical estimation of lane curvature and vehicle position.
:param img (ndarray): Original image
:param warped (ndarray): Warped image
:param invM (ndarray): Inverse Perpsective Transformation matrix
:param poly_param (ndarray): Set of 2nd order polynomial coefficients that represent the detected lane lines
:return (ndarray): Image with visual display
'''
undist = img
warp_zero = np.zeros_like(warped[:,:,0]).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
left_fit = poly_param[0]
right_fit = poly_param[1]
plot_xleft, plot_yleft = get_poly_points(left_fit)
plot_xright, plot_yright = get_poly_points(right_fit)
pts_left = np.array([np.transpose(np.vstack([plot_xleft, plot_yleft]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([plot_xright, plot_yright])))])
pts = np.hstack((pts_left, pts_right))
# Color the road
cv2.fillPoly(color_warp, np.int_([pts]), color)
cv2.polylines(color_warp, np.int32([pts_left]), isClosed=False,
color=lineColor, thickness=10)
cv2.polylines(color_warp, np.int32([pts_right]), isClosed=False,
color=lineColor, thickness= 10)
# Unwarp and merge with undistorted original image
unwarped = cv2.warpPerspective(color_warp, invM, (img.shape[1], img.shape[0]), flags=cv2.INTER_LINEAR)
out = cv2.addWeighted(undist, 1, unwarped, 0.4, 0)
return out
def smalldeleteArreas(img,diagnostics = False):
(numLabels, labels, stats, centroids) = cv2.connectedComponentsWithStats(img)
for i in range(1, numLabels):
if stats[i, cv2.CC_STAT_AREA] < PARAMS["MIN_ROADBED_AREA"]:
labels[labels == i] = 0
result = (labels > 0).astype("uint8")
if(diagnostics):
print("after smalldeleteArreas:")
plt.imshow(labels)
plt.show()
return result
#TO DO FIX cache
def simple_test(mask_mark_bin, mask_roadbed_bin ,visualisation = False,diagnostics = False):
binary_roadbed = smalldeleteArreas(mask_roadbed_bin,diagnostics= diagnostics)
cache = [collections.deque(maxlen = PARAMS['MAX_CACHE_LEN']),
collections.deque(maxlen = PARAMS['MAX_CACHE_LEN'])]
l_suc,out,l_roadbed_fit = roadbed_processing(binary_roadbed,SIDES.left,visualise=visualisation)
r_suc,out,r_roadbed_fit = roadbed_processing(binary_roadbed,SIDES.right,out = out,visualise=visualisation)
roadbed_fit = (l_roadbed_fit, r_roadbed_fit)
suc = min([l_suc,r_suc])
ret, img_poly, poly_param, (lc,rc) = polyfit_sliding_window(mask_mark_bin,cache, roadbed_fit, visualise= visualisation, diagnostics=diagnostics)
if diagnostics:
plot_images([(img_poly, 'Polyfit'), (out, 'Out')])
return [
[ret, poly_param, (lc,rc)],
[suc,roadbed_fit],
[img_poly,out]
]
def addLine(Mat,init_size,Minv,poly_fit,order):
y_size = Mat.shape[0]
x_size = Mat.shape[1]
y_init_size, x_init_size = init_size
Y = np.arange(0,y_init_size)
polynom = np.poly1d(poly_fit)
X = polynom(Y)
points = np.vstack([X,Y,np.ones_like(X)])
newPoints = (Minv @ points)
newPoints = ((newPoints / newPoints[2])[0:2])
#point_x = np.clip(newPoints[0] * x_size/x_init_size,0,x_size -1).astype("int")
point_x = newPoints[0] * x_size/x_init_size
point_y = newPoints[1] * y_size/y_init_size
condition = (point_x > 0) & (point_x < x_size) & (point_y > 0) & (point_y < y_size)
point_x = point_x[condition].astype("int")
point_y = point_y[condition].astype("int")
points = np.array([point_x,point_y]).T
thikness = 5
Mat = cv2.polylines(Mat, [points],False, order, thikness)
Mat[:,:thikness] = 0
Mat[:,-thikness:] = 0
##Mat[point_y,point_x] = order
return Mat