472 lines
19 KiB
Python
472 lines
19 KiB
Python
|
import argparse
|
|||
|
|
import os
|
|||
|
|
import platform
|
|||
|
|
import sys
|
|||
|
|
from pathlib import Path
|
|||
|
|
import cv2
|
|||
|
|
import numpy as np
|
|||
|
|
import time
|
|||
|
|
import torchvision
|
|||
|
|
import torch
|
|||
|
|
from read_data import LoadImages, LoadStreams
|
|||
|
|
|
|||
|
|
# from models.common import DetectMultiBackend
|
|||
|
|
|
|||
|
|
from PIL import Image, ImageDraw, ImageFont
|
|||
|
|
|
|||
|
|
|
|||
|
|
class LaneDetection():
|
|||
|
|
counter_frame = 0
|
|||
|
|
processed_fps = 0
|
|||
|
|
|
|||
|
|
def __init__(self, video_path=None):
|
|||
|
|
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
|||
|
|
self.model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/traffic/lane.pt', force_reload=True)
|
|||
|
|
# self.model = torch.load('weight/traffic/lane.pt', map_location=self.device)['model'].float().fuse()
|
|||
|
|
self.classes = self.model.names
|
|||
|
|
|
|||
|
|
self.frame = [None]
|
|||
|
|
self.imgsz = (640, 640)
|
|||
|
|
|
|||
|
|
if video_path is not None:
|
|||
|
|
self.video_name = video_path
|
|||
|
|
else:
|
|||
|
|
self.video_name = 'vid2.mp4' # A default video file
|
|||
|
|
|
|||
|
|
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
|||
|
|
|
|||
|
|
self.dataset = LoadImages(self.video_name, img_size=self.imgsz)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def use_webcam(self, source):
|
|||
|
|
# self.dataset.release() # Release any existing video capture
|
|||
|
|
# self.cap = cv2.VideoCapture(0) # Open default webcam
|
|||
|
|
# print('use_webcam')
|
|||
|
|
source = source
|
|||
|
|
|
|||
|
|
self.dataset = LoadStreams(source, img_size=self.imgsz)
|
|||
|
|
self.flag = 1
|
|||
|
|
|
|||
|
|
# return model
|
|||
|
|
|
|||
|
|
def class_to_label(self, x):
|
|||
|
|
return self.classes[int(x)]
|
|||
|
|
|
|||
|
|
|
|||
|
|
def get_frame(self):
|
|||
|
|
|
|||
|
|
red_thres = 120,
|
|||
|
|
green_thres = 160,
|
|||
|
|
blue_thres = 120,
|
|||
|
|
scale = 0.6
|
|||
|
|
|
|||
|
|
for im0s in self.dataset:
|
|||
|
|
# print(self.dataset.mode)
|
|||
|
|
# print(self.dataset)
|
|||
|
|
if self.dataset.mode == 'stream':
|
|||
|
|
image = im0s[0].copy()
|
|||
|
|
else:
|
|||
|
|
image = im0s.copy()
|
|||
|
|
|
|||
|
|
img = image[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
|
|||
|
|
|
|||
|
|
img0 = img.copy()
|
|||
|
|
|
|||
|
|
img = torch.tensor(img0)
|
|||
|
|
|
|||
|
|
img = img.float() # uint8 to fp16/32
|
|||
|
|
img /= 255.0 # 0 - 255 to 0.0 - 1.0
|
|||
|
|
if img.ndimension() == 3:
|
|||
|
|
img = img.unsqueeze(0)
|
|||
|
|
img = img.to(self.device)
|
|||
|
|
|
|||
|
|
pred = self.model(img)
|
|||
|
|
pred = non_max_suppression(pred, 0.25, 0.45, None, False, max_det=1000)
|
|||
|
|
# print(pred)
|
|||
|
|
|
|||
|
|
for i, det in enumerate(pred): # per image
|
|||
|
|
im0 = im0s.copy()
|
|||
|
|
annotator = Annotator(im0, line_width=3, example=str(self.classes))
|
|||
|
|
if len(det):
|
|||
|
|
# Rescale boxes from img_size to im0 size
|
|||
|
|
det[:, :4] = scale_boxes(img.shape[2:], det[:, :4], im0.shape).round()
|
|||
|
|
|
|||
|
|
im0 = annotator.result()
|
|||
|
|
|
|||
|
|
|
|||
|
|
color_im0 = color_select(im0, red_thres, green_thres, blue_thres)
|
|||
|
|
edg_im0 = canny_edg_(color_im0)
|
|||
|
|
im0 = Hough_transform(edg_im0, im0, scale)
|
|||
|
|
|
|||
|
|
|
|||
|
|
ret, jpeg = cv2.imencode(".jpg", im0)
|
|||
|
|
|
|||
|
|
accuracy = 0
|
|||
|
|
num_people = 0
|
|||
|
|
|
|||
|
|
return jpeg.tobytes(), ''
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
class Annotator:
|
|||
|
|
# YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
|
|||
|
|
def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
|
|||
|
|
assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
|
|||
|
|
non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic
|
|||
|
|
self.pil = pil or non_ascii
|
|||
|
|
if self.pil: # use PIL
|
|||
|
|
self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
|
|||
|
|
self.draw = ImageDraw.Draw(self.im)
|
|||
|
|
self.font = 'Arial.Unicode.ttf'
|
|||
|
|
else: # use cv2
|
|||
|
|
self.im = im
|
|||
|
|
self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
|
|||
|
|
|
|||
|
|
|
|||
|
|
def masks(self, masks, colors, im_gpu, alpha=0.5):
|
|||
|
|
"""Plot masks at once.
|
|||
|
|
Args:
|
|||
|
|
masks (tensor): predicted masks on cuda, shape: [n, h, w]
|
|||
|
|
colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n]
|
|||
|
|
im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1]
|
|||
|
|
alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque
|
|||
|
|
"""
|
|||
|
|
if self.pil:
|
|||
|
|
# convert to numpy first
|
|||
|
|
self.im = np.asarray(self.im).copy()
|
|||
|
|
if im_gpu is None:
|
|||
|
|
# Add multiple masks of shape(h,w,n) with colors list([r,g,b], [r,g,b], ...)
|
|||
|
|
if len(masks) == 0:
|
|||
|
|
return
|
|||
|
|
if isinstance(masks, torch.Tensor):
|
|||
|
|
masks = torch.as_tensor(masks, dtype=torch.uint8)
|
|||
|
|
masks = masks.permute(1, 2, 0).contiguous()
|
|||
|
|
masks = masks.cpu().numpy()
|
|||
|
|
# masks = np.ascontiguousarray(masks.transpose(1, 2, 0))
|
|||
|
|
masks = scale_image(masks.shape[:2], masks, self.im.shape)
|
|||
|
|
masks = np.asarray(masks, dtype=np.float32)
|
|||
|
|
colors = np.asarray(colors, dtype=np.float32) # shape(n,3)
|
|||
|
|
s = masks.sum(2, keepdims=True).clip(0, 1) # add all masks together
|
|||
|
|
masks = (masks @ colors).clip(0, 255) # (h,w,n) @ (n,3) = (h,w,3)
|
|||
|
|
self.im[:] = masks * alpha + self.im * (1 - s * alpha)
|
|||
|
|
else:
|
|||
|
|
if len(masks) == 0:
|
|||
|
|
self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
|
|||
|
|
colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0
|
|||
|
|
colors = colors[:, None, None] # shape(n,1,1,3)
|
|||
|
|
masks = masks.unsqueeze(3) # shape(n,h,w,1)
|
|||
|
|
masks_color = masks * (colors * alpha) # shape(n,h,w,3)
|
|||
|
|
|
|||
|
|
inv_alph_masks = (1 - masks * alpha).cumprod(0) # shape(n,h,w,1)
|
|||
|
|
mcs = (masks_color * inv_alph_masks).sum(0) * 2 # mask color summand shape(n,h,w,3)
|
|||
|
|
|
|||
|
|
im_gpu = im_gpu.flip(dims=[0]) # flip channel
|
|||
|
|
im_gpu = im_gpu.permute(1, 2, 0).contiguous() # shape(h,w,3)
|
|||
|
|
im_gpu = im_gpu * inv_alph_masks[-1] + mcs
|
|||
|
|
im_mask = (im_gpu * 255).byte().cpu().numpy()
|
|||
|
|
# print(type(im_gpu), type(im_mask), type(self.im.shape))
|
|||
|
|
self.im[:] = scale_image(im_gpu.shape, im_mask, self.im.shape)
|
|||
|
|
if self.pil:
|
|||
|
|
# convert im back to PIL and update draw
|
|||
|
|
self.fromarray(self.im)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def rectangle(self, xy, fill=None, outline=None, width=1):
|
|||
|
|
# Add rectangle to image (PIL-only)
|
|||
|
|
self.draw.rectangle(xy, fill, outline, width)
|
|||
|
|
|
|||
|
|
def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'):
|
|||
|
|
# Add text to image (PIL-only)
|
|||
|
|
if anchor == 'bottom': # start y from font bottom
|
|||
|
|
w, h = self.font.getsize(text) # text width, height
|
|||
|
|
xy[1] += 1 - h
|
|||
|
|
self.draw.text(xy, text, fill=txt_color, font=self.font)
|
|||
|
|
|
|||
|
|
def fromarray(self, im):
|
|||
|
|
# Update self.im from a numpy array
|
|||
|
|
self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
|
|||
|
|
self.draw = ImageDraw.Draw(self.im)
|
|||
|
|
|
|||
|
|
def result(self):
|
|||
|
|
# Return annotated image as array
|
|||
|
|
return np.asarray(self.im)
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
def canny_edg_(img):
|
|||
|
|
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 转为灰度图像
|
|||
|
|
kernel_size = 5
|
|||
|
|
blur_gray = cv2.GaussianBlur(gray, (kernel_size, kernel_size), 0) # 高斯滤波
|
|||
|
|
low_thres = 160
|
|||
|
|
high_thres = 240
|
|||
|
|
edg_img = cv2.Canny(blur_gray, low_thres, high_thres)
|
|||
|
|
return edg_img
|
|||
|
|
|
|||
|
|
|
|||
|
|
def color_select(img, red_thres=120, green_thres=160, blue_thres=120):
|
|||
|
|
# h, w = img.shape[:2]
|
|||
|
|
color_select = np.copy(img)
|
|||
|
|
bgr_thre = [blue_thres, green_thres, red_thres]
|
|||
|
|
thresholds = (img[:, :, 0] < bgr_thre[0]) | (img[:, :, 1] < bgr_thre[1]) | (img[:, :, 2] < bgr_thre[2])
|
|||
|
|
color_select[thresholds] = [0, 0, 0] # 小于阈值的像素设置为0
|
|||
|
|
return color_select
|
|||
|
|
|
|||
|
|
def Hough_transform(edg_img, img, mask_scale=0.6):
|
|||
|
|
# img是原始图像
|
|||
|
|
|
|||
|
|
mask_img = get_mask(edg_img, mask_scale) # 掩膜图像
|
|||
|
|
# -----------------霍夫曼变换-----------------------
|
|||
|
|
# 定义Hough 变换的参数
|
|||
|
|
rho = 1
|
|||
|
|
theta = np.pi/180
|
|||
|
|
threshold = 2
|
|||
|
|
min_line_length = 4 # 组成一条线的最小像素
|
|||
|
|
max_line_length = 5 # 可连接线段之间的最大像素距离
|
|||
|
|
|
|||
|
|
lines = cv2.HoughLinesP(mask_img, rho, theta, threshold, np.array([]),
|
|||
|
|
min_line_length, max_line_length)
|
|||
|
|
|
|||
|
|
left_line = []
|
|||
|
|
right_line = []
|
|||
|
|
for line in lines:
|
|||
|
|
for x1, y1, x2, y2 in line:
|
|||
|
|
if x1 == x2:
|
|||
|
|
pass
|
|||
|
|
else:
|
|||
|
|
# 求直线方程斜率判断左右车道
|
|||
|
|
m = (y2 - y1) / (x2 - x1)
|
|||
|
|
c = y1 - m * x1
|
|||
|
|
if m < 0: # 左车道
|
|||
|
|
left_line.append((m, c))
|
|||
|
|
elif m >= 0: # 右车道
|
|||
|
|
right_line.append((m, c))
|
|||
|
|
cv2.line(img, (x1, y1), (x2, y2), (255, 0, 0), 5)
|
|||
|
|
return img
|
|||
|
|
|
|||
|
|
def scale_image(im1_shape, masks, im0_shape, ratio_pad=None):
|
|||
|
|
"""
|
|||
|
|
img1_shape: model input shape, [h, w]
|
|||
|
|
img0_shape: origin pic shape, [h, w, 3]
|
|||
|
|
masks: [h, w, num]
|
|||
|
|
"""
|
|||
|
|
# Rescale coordinates (xyxy) from im1_shape to im0_shape
|
|||
|
|
if ratio_pad is None: # calculate from im0_shape
|
|||
|
|
gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new
|
|||
|
|
pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding
|
|||
|
|
else:
|
|||
|
|
pad = ratio_pad[1]
|
|||
|
|
top, left = int(pad[1]), int(pad[0]) # y, x
|
|||
|
|
bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0])
|
|||
|
|
|
|||
|
|
if len(masks.shape) < 2:
|
|||
|
|
raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}')
|
|||
|
|
masks = masks[top:bottom, left:right]
|
|||
|
|
# masks = masks.permute(2, 0, 1).contiguous()
|
|||
|
|
# masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0]
|
|||
|
|
# masks = masks.permute(1, 2, 0).contiguous()
|
|||
|
|
|
|||
|
|
masks = cv2.resize(masks, (im0_shape[1], im0_shape[0]))
|
|||
|
|
|
|||
|
|
if len(masks.shape) == 2:
|
|||
|
|
masks = masks[:, :, None]
|
|||
|
|
return masks
|
|||
|
|
|
|||
|
|
def xywh2xyxy(x):
|
|||
|
|
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
|
|||
|
|
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
|
|||
|
|
y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
|
|||
|
|
y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
|
|||
|
|
y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
|
|||
|
|
y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
|
|||
|
|
return y
|
|||
|
|
|
|||
|
|
|
|||
|
|
def non_max_suppression(
|
|||
|
|
prediction,
|
|||
|
|
conf_thres=0.25,
|
|||
|
|
iou_thres=0.45,
|
|||
|
|
classes=None,
|
|||
|
|
agnostic=False,
|
|||
|
|
multi_label=False,
|
|||
|
|
labels=(),
|
|||
|
|
max_det=300,
|
|||
|
|
nm=0, # number of masks
|
|||
|
|
):
|
|||
|
|
"""Non-Maximum Suppression (NMS) on inference results to reject overlapping detections
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
list of detections, on (n,6) tensor per image [xyxy, conf, cls]
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out)
|
|||
|
|
prediction = prediction[0] # select only inference output
|
|||
|
|
|
|||
|
|
device = prediction.device
|
|||
|
|
mps = 'mps' in device.type # Apple MPS
|
|||
|
|
if mps: # MPS not fully supported yet, convert tensors to CPU labelme_dataset NMS
|
|||
|
|
prediction = prediction.cpu()
|
|||
|
|
bs = prediction.shape[0] # batch size
|
|||
|
|
nc = prediction.shape[2] - nm - 5 # number of classes
|
|||
|
|
xc = prediction[..., 4] > conf_thres # candidates
|
|||
|
|
|
|||
|
|
# Checks
|
|||
|
|
assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
|
|||
|
|
assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
|
|||
|
|
|
|||
|
|
# Settings
|
|||
|
|
# min_wh = 2 # (pixels) minimum box width and height
|
|||
|
|
max_wh = 7680 # (pixels) maximum box width and height
|
|||
|
|
max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()
|
|||
|
|
time_limit = 0.5 + 0.05 * bs # seconds to quit after
|
|||
|
|
redundant = True # require redundant detections
|
|||
|
|
multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)
|
|||
|
|
merge = False # use merge-NMS
|
|||
|
|
|
|||
|
|
t = time.time()
|
|||
|
|
mi = 5 + nc # mask start index
|
|||
|
|
output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs
|
|||
|
|
for xi, x in enumerate(prediction): # image index, image inference
|
|||
|
|
# Apply constraints
|
|||
|
|
# x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
|
|||
|
|
x = x[xc[xi]] # confidence
|
|||
|
|
|
|||
|
|
# Cat apriori labels if autolabelling
|
|||
|
|
if labels and len(labels[xi]):
|
|||
|
|
lb = labels[xi]
|
|||
|
|
v = torch.zeros((len(lb), nc + nm + 5), device=x.device)
|
|||
|
|
v[:, :4] = lb[:, 1:5] # box
|
|||
|
|
v[:, 4] = 1.0 # conf
|
|||
|
|
v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls
|
|||
|
|
x = torch.cat((x, v), 0)
|
|||
|
|
|
|||
|
|
# If none remain process next image
|
|||
|
|
if not x.shape[0]:
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
# Compute conf
|
|||
|
|
x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf
|
|||
|
|
|
|||
|
|
# Box/Mask
|
|||
|
|
box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2)
|
|||
|
|
mask = x[:, mi:] # zero columns if no masks
|
|||
|
|
|
|||
|
|
# Detections matrix nx6 (xyxy, conf, cls)
|
|||
|
|
if multi_label:
|
|||
|
|
i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T
|
|||
|
|
x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1)
|
|||
|
|
else: # best class only
|
|||
|
|
conf, j = x[:, 5:mi].max(1, keepdim=True)
|
|||
|
|
x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres]
|
|||
|
|
|
|||
|
|
# Filter by class
|
|||
|
|
if classes is not None:
|
|||
|
|
x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
|
|||
|
|
|
|||
|
|
# Apply finite constraint
|
|||
|
|
# if not torch.isfinite(x).all():
|
|||
|
|
# x = x[torch.isfinite(x).all(1)]
|
|||
|
|
|
|||
|
|
# Check shape
|
|||
|
|
n = x.shape[0] # number of boxes
|
|||
|
|
if not n: # no boxes
|
|||
|
|
continue
|
|||
|
|
elif n > max_nms: # excess boxes
|
|||
|
|
x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence
|
|||
|
|
else:
|
|||
|
|
x = x[x[:, 4].argsort(descending=True)] # sort by confidence
|
|||
|
|
|
|||
|
|
# Batched NMS
|
|||
|
|
c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
|
|||
|
|
boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
|
|||
|
|
i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
|
|||
|
|
if i.shape[0] > max_det: # limit detections
|
|||
|
|
i = i[:max_det]
|
|||
|
|
if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
|
|||
|
|
# update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
|
|||
|
|
iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
|
|||
|
|
weights = iou * scores[None] # box weights
|
|||
|
|
x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
|
|||
|
|
if redundant:
|
|||
|
|
i = i[iou.sum(1) > 1] # require redundancy
|
|||
|
|
|
|||
|
|
output[xi] = x[i]
|
|||
|
|
if mps:
|
|||
|
|
output[xi] = output[xi].to(device)
|
|||
|
|
|
|||
|
|
|
|||
|
|
return output
|
|||
|
|
|
|||
|
|
|
|||
|
|
def box_iou(box1, box2, eps=1e-7):
|
|||
|
|
# https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
|
|||
|
|
"""
|
|||
|
|
Return intersection-over-union (Jaccard index) of boxes.
|
|||
|
|
Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
|
|||
|
|
Arguments:
|
|||
|
|
box1 (Tensor[N, 4])
|
|||
|
|
box2 (Tensor[M, 4])
|
|||
|
|
Returns:
|
|||
|
|
iou (Tensor[N, M]): the NxM matrix containing the pairwise
|
|||
|
|
IoU values for every element in boxes1 and boxes2
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
|
|||
|
|
(a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)
|
|||
|
|
inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)
|
|||
|
|
|
|||
|
|
# IoU = inter / (area1 + area2 - inter)
|
|||
|
|
return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)
|
|||
|
|
|
|||
|
|
def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
|
|||
|
|
# Rescale boxes (xyxy) from img1_shape to img0_shape
|
|||
|
|
if ratio_pad is None: # calculate from img0_shape
|
|||
|
|
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
|
|||
|
|
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
|
|||
|
|
else:
|
|||
|
|
gain = ratio_pad[0][0]
|
|||
|
|
pad = ratio_pad[1]
|
|||
|
|
|
|||
|
|
boxes[..., [0, 2]] -= pad[0] # x padding
|
|||
|
|
boxes[..., [1, 3]] -= pad[1] # y padding
|
|||
|
|
boxes[..., :4] /= gain
|
|||
|
|
clip_boxes(boxes, img0_shape)
|
|||
|
|
return boxes
|
|||
|
|
|
|||
|
|
def is_ascii(s=''):
|
|||
|
|
# Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7)
|
|||
|
|
s = str(s) # convert list, tuple, None, etc. to str
|
|||
|
|
return len(s.encode().decode('ascii', 'ignore')) == len(s)
|
|||
|
|
|
|||
|
|
def clip_boxes(boxes, shape):
|
|||
|
|
# Clip boxes (xyxy) to image shape (height, width)
|
|||
|
|
if isinstance(boxes, torch.Tensor): # faster individually
|
|||
|
|
boxes[..., 0].clamp_(0, shape[1]) # x1
|
|||
|
|
boxes[..., 1].clamp_(0, shape[0]) # y1
|
|||
|
|
boxes[..., 2].clamp_(0, shape[1]) # x2
|
|||
|
|
boxes[..., 3].clamp_(0, shape[0]) # y2
|
|||
|
|
else: # np.array (faster grouped)
|
|||
|
|
boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2
|
|||
|
|
boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2
|
|||
|
|
|
|||
|
|
def get_mask(edg_img, mask_scale=0.6):
|
|||
|
|
# ----------------检测区域的选择---------------------
|
|||
|
|
mask = np.zeros_like(edg_img) # 全黑的图像
|
|||
|
|
ignore_mask_color = 255
|
|||
|
|
# get image size
|
|||
|
|
imgshape = edg_img.shape
|
|||
|
|
# 设置mask shape [1,4,2] 一般车道位置大概占据画面的1/3的位置
|
|||
|
|
ret = np.array([[(1, imgshape[0]), (1, int(imgshape[0] * mask_scale)), (imgshape[1] - 1, int(imgshape[0] * mask_scale)),
|
|||
|
|
(imgshape[1] - 1, imgshape[0] - 1)]], dtype=np.int32)
|
|||
|
|
# 多边形填充，mask是需要填充的图像，ret是多边形顶点, 将需要保留的区域填充为白色矩形
|
|||
|
|
cv2.fillPoly(mask, ret, ignore_mask_color) # mask下面部分变成白色
|
|||
|
|
# 图像与运算，保留掩膜图像
|
|||
|
|
mask_img = cv2.bitwise_and(edg_img, mask)
|
|||
|
|
# ------------------------------------------------
|
|||
|
|
return mask_img
|
|||
|
|
|
|||
|
|
|