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test.py
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test.py
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import argparse
import time
from pathlib import Path
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, \
strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized
from tkinter import *
import numpy as np
import imutils
from imutils import contours
import serial as ser
import struct
import pyrealsense2 as rs
def nothing(x):
pass
pc = rs.pointcloud()
points = rs.points()
pipeline = rs.pipeline() # 创建一个管道
config = rs.config() # Create a config并配置要流式传输的管道。
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 15)
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 15)
# 使用选定的流参数显式启用设备流
# Start streaming 开启流
pipe_profile = pipeline.start(config)
# Create an align object 创建对其流对象
# rs.align allows us to perform alignment of depth frames to others frames
# The "align_to" is the stream type to which we plan to align depth frames.
# (对其流)
align_to = rs.stream.color
align = rs.align(align_to) # 设置为其他类型的流,意思是我们允许深度流与其他流对齐
#print(type(align))
cap = cv2.VideoCapture(0)
def led_practice(x,y):
frames = pipeline.wait_for_frames() # 等待开启通道
aligned_frames = align.process(frames) # 将深度框和颜色框对齐
depth_frame = aligned_frames.get_depth_frame() # ?获得对齐后的帧数深度数据(图)
color_frame = aligned_frames.get_color_frame() # ?获得对齐后的帧数颜色数据(图)
img_color = np.asanyarray(color_frame.get_data()) # 把图像像素转化为数组
img_depth = np.asanyarray(depth_frame.get_data()) # 把图像像素转化为数组
#cv2.putText(img_color, "Distance/cm:"+str(img_depth[300, 250]), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1.2, [255, 0, 255])
#cv2.putText(img_color, "Distance/cm:"+str(img_depth[x_axis, y_axis]), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1.2, [255, 0, 255])
#cv2.putText(img_color, "X:"+str(np.float(vtx[i][0])), (80, 80), cv2.FONT_HERSHEY_SIMPLEX, 1, [255, 0, 255])
#cv2.putText(img_color, "Y:"+str(np.float(vtx[i][1])), (80, 120), cv2.FONT_HERSHEY_SIMPLEX, 1, [255, 0, 255])
#cv2.putText(img_color, "Z:"+str(np.float(vtx[i][2])), (80, 160), cv2.FONT_HERSHEY_SIMPLEX, 1, [255, 0, 255])
print('Distance: ',img_depth[x,y]/10)
#cv2.imshow('depth_frame', img_color)
#cv2.imshow("dasdsadsa", img_depth)
def ceshijian():
# se=ser.Serial("/dev/ttyTHS1",9600,timeout=1)
torch.cuda.current_device()
torch.cuda._initialized = True
def detect(save_img=False):
source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
('rtsp://', 'rtmp://', 'http://'))
# Directories
save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Initialize
set_logging()
device = select_device(opt.device)
half = device.type != 'cpu' # half precision only supported on CUDA
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
if half:
model.half() # to FP16
# Second-stage classifier
classify = False
if classify:
modelc = load_classifier(name='resnet101', n=2) # initialize
modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
# Set Dataloader
#vid_path, vid_writer = None, None
if webcam:
view_img = True
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz)
else:
save_img = True
dataset = LoadImages(source, img_size=imgsz)
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
# Run inference
t0 = time.time()
img = torch.zeros((1, 3, 640, 480), device=device) # init img
#_ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
for path, img, im0s, im0sdepth in dataset:
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = time_synchronized()
pred = model(img, augment=opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
t2 = time_synchronized()
# Apply Classifier
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
# Process detections
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, s, im0, im0depth = Path(path[i]), '%g: ' % i, im0s[i].copy(),im0sdepth[i].copy()
#p, s = Path(path[i]), '%g: ' % i
#im0= np.asanyarray(color_frame.get_data()) # 把图像像素转化为数组
else:
p, s,im0,im0depth = Path(path), '',im0s,im0sdepth
#p, s = Path(path), ''
#im0= np.asanyarray(color_frame.get_data()) # 把图像像素转化为数组
#save_path = str(save_dir / p.name)
#txt_path = str(save_dir / 'labels' / p.stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += '%g %ss, ' % (n, names[int(c)]) # add to string
# Write results
for *xyxy, conf, cls in reversed(det):
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format
#with open(txt_path + '.txt', 'a') as f:
#f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or view_img: # Add bbox to image
label = '%s %.2f' % (names[int(cls)], conf)
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
print((torch.tensor(xyxy)[3]-torch.tensor(xyxy)[1])/(torch.tensor(xyxy)[2]-torch.tensor(xyxy)[0]))#打印箭的斜率
cord=torch.tensor(xyxy).numpy().tolist()
#print(cord[0])
xcord=int((cord[2]+cord[0])/2)
ycord=int((cord[3]+cord[1])/2)
cv2.circle(im0, (xcord, ycord), 8, [255, 0, 255], thickness=-1)
if (names[int(cls)]=='arrow'):
#if xcord>640:
#xcord=640
#if ycord>480:
#ycord=480
print(im0depth[ycord,xcord]/10)
#se.write(str((torch.tensor(xyxy)[3]-torch.tensor(xyxy)[1])/(torch.tensor(xyxy)[2]-torch.tensor(xyxy)[0])).encode("GB2312"))
# Print time (inference + NMS)
print('%sDone. (%.3fs)' % (s, t2 - t1))
# Stream results
if view_img:
cv2.imshow(str(p), im0)
if cv2.waitKey(1) == ord('q'): # q to quit
cv2.destroyAllWindows()
raise StopIteration
# Save results (image with detections)
#if save_img:
#if dataset.mode == 'images':
#cv2.imwrite(save_path, im0)
#else:
#if vid_path != save_path: # new video
#vid_path = save_path
#if isinstance(vid_writer, cv2.VideoWriter):
# vid_writer.release() # release previous video writer
#fourcc = 'mp4v' # output video codec
#fps = vid_cap.get(cv2.CAP_PROP_FPS)
#w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
#h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
#vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
#vid_writer.write(im0)
if save_txt or save_img:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
print(f"Results saved to {save_dir}{s}")
print('Done. (%.3fs)' % (time.time() - t0))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='F:/Program Files (x86)/yolo v5/runs/train/exp2/weights/best.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='0', help='source') # file/folder, 0 for webcam
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.45, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.25, help='IOU threshold for NMS')
parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', action='store_true', help='display results')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--update', action='store_true', help='update all models')
parser.add_argument('--project', default='runs/detect', help='save results to project/name')
parser.add_argument('--name', default='exp', help='save results to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
opt = parser.parse_args()
print(opt)
with torch.no_grad():
if opt.update: # update all models (to fix SourceChangeWarning)
for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
detect()
strip_optimizer(opt.weights)
else:
detect()
def ceshitong():
lower = np.array([0, 96, 126])
upper = np.array([97, 225, 255])
# 内核
kernel = np.ones((5, 5), np.uint8)
# 打开摄像头
vc = cv2.VideoCapture(0)
if vc.isOpened():
flag, frame = vc.read()
# 翻转图像
cv2.imshow("frame", frame)
else:
flag = False
while flag:
flag, frame = vc.read()
# 翻转图像
draw_frame = frame.copy()
if frame is None:
break
if flag is True:
'''下面对摄像头读取到的图像进行处理,这个步骤是比较重要的'''
# 转换颜色空间HSV
frame_hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# 颜色识别
img = cv2.inRange(frame_hsv, lower, upper)
# 膨胀操作
dilation = cv2.dilate(img, kernel, iterations=1)
# 闭操作
closing = cv2.morphologyEx(dilation, cv2.MORPH_CLOSE, kernel)
# 高斯滤波
closing = cv2.GaussianBlur(closing, (5, 5), 0)
# 边缘检测
edges = cv2.Canny(closing, 10, 20)
'''上面进行那么多操作就是为了得到更好的目标图形,具体效果因环境而异'''
# 寻找轮廓
cnts, _ = cv2.findContours(
edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# 判断轮廓数量也就是判断是否寻找到轮廓,如果没有找到轮廓就不继续进行操作
if len(cnts) > 0:
# 存放轮廓面积的列表
s = []
# 存放最大轮廓的索引
max_index = 0
# 获得排序后的轮廓列表以及每个轮廓对应的外接矩形
(cnts, boundingRects) = contours.sort_contours(cnts)
# 寻找面积最大的轮廓的索引
for cnt in cnts:
s.append(cv2.contourArea(cnt))
max_index = s.index(max(s))
# 根据面积最大轮廓的索引找到它的外接矩形的信息
(x, y, w, h) = boundingRects[max_index]
# 画矩形
frame_out = cv2.rectangle(
draw_frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.imshow("frame", draw_frame)
if cv2.waitKey(10) == ord('q'):
break
vc.release()
cv2.destroyAllWindows()
GUI=Tk()
GUI.title('机器人控制台')
GUI.geometry('600x400')
Button1=Button(GUI,text='开始识别箭',command=ceshijian)
Button2=Button(GUI,text='开始识别桶',command=ceshitong)
Button1.pack()
Button2.pack()
GUI.mainloop()