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import torch
from lib.utils import is_parallel
import numpy as np
np.set_printoptions(threshold=np.inf)
import cv2
from sklearn.cluster import DBSCAN
def build_targets(cfg, predictions, targets, model):
'''
predictions
[16, 3, 32, 32, 85]
[16, 3, 16, 16, 85]
[16, 3, 8, 8, 85]
torch.tensor(predictions[i].shape)[[3, 2, 3, 2]]
[32,32,32,32]
[16,16,16,16]
[8,8,8,8]
targets[3,x,7]
t [index, class, x, y, w, h, head_index]
'''
# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
det = model.module.model[model.module.detector_index] if is_parallel(model) \
else model.model[model.detector_index] # Detect() module
# print(type(model))
# det = model.model[model.detector_index]
# print(type(det))
na, nt = det.na, targets.shape[0] # number of anchors, targets
tcls, tbox, indices, anch = [], [], [], []
gain = torch.ones(7, device=targets.device) # normalized to gridspace gain
ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices
g = 0.5 # bias
off = torch.tensor([[0, 0],
[1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m
# [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
], device=targets.device).float() * g # offsets
for i in range(det.nl):
anchors = det.anchors[i] #[3,2]
gain[2:6] = torch.tensor(predictions[i].shape)[[3, 2, 3, 2]] # xyxy gain
# Match targets to anchors
t = targets * gain
if nt:
# Matches
r = t[:, :, 4:6] / anchors[:, None] # wh ratio
j = torch.max(r, 1. / r).max(2)[0] < cfg.TRAIN.ANCHOR_THRESHOLD # compare
# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
t = t[j] # filter
# Offsets
gxy = t[:, 2:4] # grid xy
gxi = gain[[2, 3]] - gxy # inverse
j, k = ((gxy % 1. < g) & (gxy > 1.)).T
l, m = ((gxi % 1. < g) & (gxi > 1.)).T
j = torch.stack((torch.ones_like(j), j, k, l, m))
t = t.repeat((5, 1, 1))[j]
offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
else:
t = targets[0]
offsets = 0
# Define
b, c = t[:, :2].long().T # image, class
gxy = t[:, 2:4] # grid xy
gwh = t[:, 4:6] # grid wh
gij = (gxy - offsets).long()
gi, gj = gij.T # grid xy indices
# Append
a = t[:, 6].long() # anchor indices
indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
anch.append(anchors[a]) # anchors
tcls.append(c) # class
return tcls, tbox, indices, anch
def morphological_process(image, kernel_size=5, func_type=cv2.MORPH_CLOSE):
"""
morphological process to fill the hole in the binary segmentation result
:param image:
:param kernel_size:
:return:
"""
if len(image.shape) == 3:
raise ValueError('Binary segmentation result image should be a single channel image')
if image.dtype is not np.uint8:
image = np.array(image, np.uint8)
kernel = cv2.getStructuringElement(shape=cv2.MORPH_ELLIPSE, ksize=(kernel_size, kernel_size))
# close operation fille hole
closing = cv2.morphologyEx(image, func_type, kernel, iterations=1)
return closing
def connect_components_analysis(image):
"""
connect components analysis to remove the small components
:param image:
:return:
"""
if len(image.shape) == 3:
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
else:
gray_image = image
# print(gray_image.dtype)
return cv2.connectedComponentsWithStats(gray_image, connectivity=8, ltype=cv2.CV_32S)
# def if_y(samples_x):
# for sample_x in samples_x:
# if len(sample_x):
# if len(sample_x) != (sample_x[-1] - sample_x[0] + 1):
# return False
# return True
# def fitlane(mask, sel_labels, labels, stats):
# for label_group in sel_labels:
# states = [stats[k] for k in label_group]
# x_max, y_max, w_max, h_max, _ = np.amax(np.array(states), axis=0)
# x_min, y_min, w_min, h_min, _ = np.amin(np.array(states), axis=0)
# # print(np.array(states))
# x = x_min; y = y_min; w = w_max; h = h_max
# if len(label_group) > 1:
# # print(label_group)
# for m in range(len(label_group)-1):
# # print(label_group[m+1])
# # print(label_group[0])
# labels[labels == label_group[m+1]] = label_group[0]
# t = label_group[0]
# if (y_max + h - 1) > 720:
# samples_y = np.linspace(y, 720-1, 20)
# else:
# samples_y = np.linspace(y, y_max+h-1, 20)
# samples_x = [np.where(labels[int(sample_y)]==t)[0] for sample_y in samples_y]
# if if_y(samples_x):
# # print('in y')
# samples_x = [int(np.mean(sample_x)) if len(sample_x) else -1 for sample_x in samples_x]
# samples_x = np.array(samples_x)
# samples_y = np.array(samples_y)
# samples_y = samples_y[samples_x != -1]
# samples_x = samples_x[samples_x != -1]
# func = np.polyfit(samples_y, samples_x, 2)
# # x_limits = np.polyval(func, 0)
# # if x_limits < 0 or x_limits > 1280:
# # if (y_max + h - 1) > 720:
# draw_y = np.linspace(y, 720-1, 720-y)
# # else:
# # draw_y = np.linspace(y, y_max+h-1, y_max+h-y)
# # draw_y = np.linspace(y, 720-1, 720-y)
# draw_x = np.polyval(func, draw_y)
# draw_y = draw_y[draw_x < 1280]
# draw_x = draw_x[draw_x < 1280]
# draw_points = (np.asarray([draw_x, draw_y]).T).astype(np.int32)
# cv2.polylines(mask, [draw_points], False, 1, thickness=15)
# else:
# # print('in x')
# if (x_max + w - 1) > 1280:
# samples_x = np.linspace(x, 1280-1, 20)
# else:
# samples_x = np.linspace(x, x_max+w-1, 20)
# samples_y = [np.where(labels[:, int(sample_x)]==t)[0] for sample_x in samples_x]
# samples_y = [int(np.mean(sample_y)) if len(sample_y) else -1 for sample_y in samples_y]
# samples_x = np.array(samples_x)
# samples_y = np.array(samples_y)
# samples_x = samples_x[samples_y != -1]
# samples_y = samples_y[samples_y != -1]
# func = np.polyfit(samples_x, samples_y, 2)
# # y_limits = np.polyval(func, 0)
# # if y_limits > 720 or y_limits < 0:
# # if (x_max + w - 1) > 1280:
# draw_x = np.linspace(x, 1280-1, 1280-x)
# # else:
# # y_limits = np.polyval(func, 0)
# # if y_limits > 720 or y_limits < 0:
# # draw_x = np.linspace(x, x_max+w-1, w+x_max-x)
# # else:
# # if x_max+w-1 < 640:
# # draw_x = np.linspace(0, x_max+w-1, w+x_max-x)
# # else:
# # draw_x = np.linspace(x, 1280-1, 1280-x)
# draw_y = np.polyval(func, draw_x)
# draw_x = draw_x[draw_y < 720]
# draw_y = draw_y[draw_y < 720]
# draw_points = (np.asarray([draw_x, draw_y]).T).astype(np.int32)
# cv2.polylines(mask, [draw_points], False, 1, thickness=15)
# return mask
# def connect_lane(image):
# if len(image.shape) == 3:
# gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# else:
# gray_image = image
# mask = np.zeros((image.shape[0], image.shape[1]), np.uint8)
# # print(gray_image.dtype)
# num_labels, labels, stats, centers = cv2.connectedComponentsWithStats(gray_image, connectivity=8, ltype=cv2.CV_32S)
# ratios = []
# selected_label = []
# for t in range(1, num_labels, 1):
# x, y, w, h, area = stats[t]
# center = centers[t]
# if area > 400:
# samples_y = [y, y+h-1]
# selected_label.append(t)
# samples_x = [np.where(labels[int(m)]==t)[0] for m in samples_y]
# samples_x = [int(np.median(sample_x)) for sample_x in samples_x]
# delta_x = samples_x[1] - samples_x[0]
# if center[0]/1280 > 0.5:
# ratios.append([0.7 * h / delta_x , h / w, 1.])
# else:
# ratios.append([0.7 * h / delta_x , h / w, 0.])
# clustering = DBSCAN(eps=0.3, min_samples=1).fit(ratios)
# # print(clustering.labels_)
# split_labels = []
# selected_label = np.array(selected_label)
# for k in range(len(set(clustering.labels_))):
# index = np.where(clustering.labels_==k)[0]
# split_labels.append(selected_label[index])
# # for i in range(1, num_labels, 1):
# # if i not in set(selected_label):
# # labels[labels == i] = 0
# # print(split_labels)
# mask_post = fitlane(mask, split_labels, labels, stats)
# return mask_post
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