2021-10-01
作者：互联网
from __future__ import division
import time
# se=ser.Serial("/dev/ttyUSB0",115200,timeout=1)
from models import *
from utils.utils import *
from utils.datasets import *
from utils.augmentations import *
from utils.transforms import *
import cv2
import os
import sys
import time
import datetime
import argparse
from PIL import Image
import torch
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.ticker import NullLocator
i = 1
cap = cv2.VideoCapture(0)
array_of_img = []
start = time.time()
directory_name = r'output'
import socket, sys
dest = ('<broadcast>', 7870)
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1)
washtime = []
thex = []
they = []
area = 0
count = 0
str1 = ""
filename = 'test_text.txt'
flag = 0
# to = '{"target":"step_z","num":"100"}'
# y = '{"target":"step_x","num":"100"}'
centerx = 300
centery = 300
def abs(x):
if x>=0:
return x
else:
return -x
# 2000步移六秒
def computetime(x):
total = int(abs(x)/200)
total = total*6
total = total+2
return total
if __name__ == "__main__":
time.sleep(5)
start = time.time()
# s.sendto(to.encode(),dest)
# while(cap.isOpened()):
# count=count+1
# ret, frame = cap.read()
# cv2.imshow('frame',frame)
# if cv2.waitKey(30) == ord('q'):
# cv2.imwrite('data/custom/dd/'+str(i)+".jpg",frame)
# if cv2.waitKey(30) == ord('q'):
# ret, frame = cap.read()
# frame=cv2.imread('data/custom/dd/'+str(i)+".jpg")
# break
# When everything done, release the capture
# cap.release()
# cv2.destroyAllWindows()
parser = argparse.ArgumentParser()
parser.add_argument("--image_folder", type=str, default="data/custom/dd", help="path to dataset")
parser.add_argument("--model_def", type=str, default="config/yolov3-custom.cfg",
help="path to model definition file")
parser.add_argument("--weights_path", type=str, default="checkpoints/ckpt_86.pth", help="path to weights file")
parser.add_argument("--class_path", type=str, default="data/custom/classes.names", help="path to class label file")
parser.add_argument("--conf_thres", type=float, default=0.8, help="object confidence threshold")
parser.add_argument("--nms_thres", type=float, default=0.4, help="iou thresshold for non-maximum suppression")
parser.add_argument("--batch_size", type=int, default=1, help="size of the batches")
parser.add_argument("--n_cpu", type=int, default=0, help="number of cpu threads to use during batch generation")
parser.add_argument("--img_size", type=int, default=416, help="size of each image dimension")
parser.add_argument("--checkpoint_model", type=str, default="checkpoints/ckpt_86.pth",
help="path to checkpoint model")
opt = parser.parse_args()
print(opt)
#device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = torch.device('cpu')
os.makedirs("output", exist_ok=True)
# Set up modella
model = Darknet(opt.model_def, img_size=opt.img_size).to(device)
if opt.weights_path.endswith(".weights"):
# Load darknet weights
model.load_darknet_weights(opt.weights_path)
else:
# Load checkpoint weights
model.load_state_dict(torch.load(opt.weights_path,map_location = 'cpu')) #cpu环境
model.eval() # Set in evaluation mode
dataloader = DataLoader(
ImageFolder(opt.image_folder, transform= \
transforms.Compose([DEFAULT_TRANSFORMS, Resize(opt.img_size)])),
batch_size=opt.batch_size,
shuffle=False,
num_workers=opt.n_cpu,
)
classes = load_classes(opt.class_path) # Extracts class labels from file
Tensor = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor
imgs = [] # Stores image paths
img_detections = [] # Stores detections for each image index
print("\nPerforming object detection:")
prev_time = time.time()
for batch_i, (img_paths, input_imgs) in enumerate(dataloader):
# Configure input
input_imgs = Variable(input_imgs.type(Tensor))
# Get detections
with torch.no_grad():
detections = model(input_imgs)
detections = non_max_suppression(detections, opt.conf_thres, opt.nms_thres)
# Log progress
current_time = time.time()
inference_time = datetime.timedelta(seconds=current_time - prev_time)
prev_time = current_time
print("\t+ Batch %d, Inference Time: %s" % (batch_i, inference_time))
# Save image and detections
imgs.extend(img_paths)
img_detections.extend(detections)
# Bounding-box colors
cmap = plt.get_cmap("tab20b")
colors = [cmap(i) for i in np.linspace(0, 1, 20)]
print("\nSaving images:")
# Iterate through images and save plot of detections
for img_i, (path, detections) in enumerate(zip(imgs, img_detections)):
print("(%d) Image: '%s'" % (img_i, path))
# Create plot
img = np.array(Image.open(path))
plt.figure()
fig, ax = plt.subplots(1)
ax.imshow(img)
# Draw bounding boxes and labels of detections
if detections is not None:
flag = 1
# Rescale boxes to original image
detections = rescale_boxes(detections, opt.img_size, img.shape[:2])
unique_labels = detections[:, -1].cpu().unique()
n_cls_preds = len(unique_labels)
bbox_colors = random.sample(colors, n_cls_preds)
alltime = 0
for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
str1=""
str2 = ""
print("\t+ Label: %s, Conf: %.5f" % (classes[int(cls_pred)], cls_conf.item()))
box_w = x2 - x1
box_h = y2 - y1
centx = int((x1 + x2) * 0.1 / 2)
centy = int((y1 + y2) * 0.1 / 2)
thex.append(centx)
they.append(centy)
str1 += '{"target":"step_z","num":'
#==========================================================================================
# ==========================================================================================
# ==========================================================================================
str1_time = computetime(centy-centery)
print("1 time:"+str(str1_time))
str1 += str(10*(centy-centery))
str1 += "}"
s.sendto(str1.encode(), dest)
time.sleep(abs(str1_time))
str2 += '{"target":"step_x","num":'
str2_time = computetime(centy-centery)
print("2 time:"+str(str1_time))
str2 +=str(10*(centx-centerx))
str2+= "}"
s.sendto(str2.encode(),dest)
time.sleep(abs(str2_time))
area = box_h*box_w
if area > 0:
print("begin wash")
alltime = 5
if classes[int(cls_pred)] == "solid":
alltime += 7
str3 = ""
str3+='{"target":"pump","num":1000}' #打开水泵 程序sleep的时间即进行清洗的时间
s.sendto(str3.encode(),dest)
time.sleep(alltime)
str3 = ""
str3 +='{"target":"pump","num":0}'
s.sendto(str3.encode(), dest)
print("x和y:" + str((x2 + x1) / 2) + "," + str((y1 + y2) / 2))
print("x:")
print(int((x1 + x2) / 2))
print("y:")
print(int((y1 + y2) / 2))
print(int(box_w * 0.1))
color = bbox_colors[int(np.where(unique_labels == int(cls_pred))[0])]
# Create a Rectangle patch
bbox = patches.Rectangle((x1, y1), box_w, box_h, linewidth=2, edgecolor=color, facecolor="none")
# print(int(box_w) * int(box_h) * 0.01)
str1 = '{"target":"step_z","num":' #上下移动
str1+=str(10*(centery-centy))
str1 += "}"
s.sendto(str1.encode(),dest)
time.sleep(abs(str1_time))
str2 = '{"target":"step_x","num":'
str2 +=str(10*(centerx-centx))
str2+= "}"
s.sendto(str2.encode(),dest)
time.sleep(abs(str2_time))
print(str1)
print(str2)
ax.add_patch(bbox)
# Add label
plt.text(
x1,
y1,
s=classes[int(cls_pred)],
color="white",
verticalalignment="top",
bbox={"color": color, "pad": 0},
)
# Save generated image with detections
plt.axis("off")
plt.gca().xaxis.set_major_locator(NullLocator())
plt.gca().yaxis.set_major_locator(NullLocator())
filename = os.path.basename(path).split(".")[0]
# output_path = os.path.join("output", f"{filename}.png")
# output_path = os.path.join("/data/custom/dd", f"{filename}.png")
plt.savefig(filename, bbox_inches="tight", pad_inches=0.0)
plt.close()
end = time.time()
print(end - start)
total = int(end - start)
with open("time.txt", 'w+') as file_object: # 如果不存在将自动创建
file_object.write(str(total) + "\n")
# pictureSocket.socket_client()
标签：10,01,int,time,detections,2021,path,print,import 来源： https://blog.csdn.net/qq_41358574/article/details/120576879