from __future__ import division
import serial as ser
import time
import pictureSocket
#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>',7860)
s = socket.socket(socket.AF_INET,socket.SOCK_DGRAM)
s.setsockopt(socket.SOL_SOCKET,socket.SO_BROADCAST,1)
washtime = []
thex = []
they = []
area = 0
str1=""
if __name__ == "__main__":
s.sendto("fd".encode(),dest)
while(cap.isOpened()):
ret, frame = cap.read()
cv2.imshow('frame',frame)
if cv2.waitKey(30) == ord('q'):
#ret, frame = cap.read()
cv2.imwrite('data/custom/dd/'+str(i)+".jpg",frame)
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")
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))
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:
# 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)
for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
alltime = 0
area = 0
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+='{"x":'
str1+=str(centx)
str1+=',"y":'
str1+=str(centy)
if area > 600:
alltime = 10
str1 += ',"alltime":'
str1 += str(alltime)
str1 += ',"area":'
area = int(box_w*box_h*0.1)
str1 += str(area)
str1+="}"
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))
print(int(box_h*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)
print(str1)
if(box_w*box_h>50):
#se.write("1".encode())
#time.sleep(3)
#se.write("0".encode())
#se.write(str1.encode())
s.sendto(str1.encode(),dest)
#data_read = se.read(1000)
#print(data_read.decode())
# Add the bbox to the plot
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()
print("beforeagain")
#time.sleep(10)
str2=''
print("again")
i = 2
cap = cv2.VideoCapture(0)
while(cap.isOpened()):
ret, frame = cap.read()
cv2.imshow('frame',frame)
if cv2.waitKey(30) == ord('q'):
#ret, frame = cap.read()
cv2.imwrite('data/custom/dd2/'+str(i)+".jpg",frame)
frame=cv2.imread('data/custom/dd2/'+str(i)+".jpg")
break
cap.release()
cv2.destroyAllWindows()
os.makedirs("output", exist_ok=True)
model.eval（) # Set in evaluation mode
dataloader = DataLoader(
ImageFolder("data/custom/dd2", 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)
if detections is None:
s.sendto("0".encode(),dest)
# Draw bounding boxes and labels of detections
if detections is not None:
# 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)
for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
print("第二次：")
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)
area = int(box_w*box_h*0.1)
str2+='{"x":'
str2+=str(centx)
str2+=',"y":'
str2+=str(centy)
str2 += ',"alltime":5'
str2 += ',"area":'
str2 += str(area)
str2+="}"
if area > 600:
alltime = 10
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))
print(int(box_h*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)
print(str2)
if box_w*box_h>50:
#se.write("1".encode())
#time.sleep(3)
#se.write("0".encode())
#se.write(str1.encode())
s.sendto(str2.encode(),dest)
#data_read = se.read(1000)
#print(data_read.decode())
# Add the bbox to the plot
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()
pictureSocket.socket_client()
自动拍照：
cam = cv2.VideoCapture(0)
while (True):
ret, img = cam.read()
cv2.imwrite("C:\\Users\\14172\\Pictures\\other\\" + str(count) + ".jpg", img)
count += 1
time.sleep(3)
if count == 10: # 此处设置采集数量，count-1
break
cv2.imshow('image', img)
cv2.waitKey(10)
cam.release()
cv2.destroyAllWindows()
