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TrafficRuleDetectorImg.py
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TrafficRuleDetectorImg.py
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import argparse
import time
from pathlib import Path
from prometheus_client import Counter
import requests
from pprint import pprint
import random
import os
import glob
from sqlalchemy import false
regions = ['mx', 'in'] # Change to your country
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
import os
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized
def detect(opt):
source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
# 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
stride = int(model.stride.max()) # model stride
imgsz = check_img_size(imgsz, s=stride) # 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()
cudnn.benchmark = True
existingOutputs = []
imgCounter = 0
try:
dataset = LoadImages(source, img_size=imgsz, stride=stride)
# 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]
print(names)
# Run inference
if device.type != 'cpu':
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
t0 = time.time()
for path, img, im0s, vid_cap 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
p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)
p = Path(p) # to Path
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if len(det):
print(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 += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
# Write results
for *xyxy, conf, cls in det:
c = int(cls) # integer class
label = f'{names[c]} {conf:.2f}'
plot_one_box(xyxy, im0, label=label, color=colors[c], line_thickness=3)
x1,y1,x2,y2 = int(xyxy[0])-10, int(xyxy[1])-10, int(xyxy[2])+10, int(xyxy[3])+10
# print(names[c])
if names[c] == 'Rider':
print('\n\nProcessing for rider # ',xyxy)
rider_helmet_status = None
rider_lp_number = None
rider_lp_status = None
no_of_passengers = 0
try:
roi = im0s[y1:y2, x1:x2]
cv2.imwrite('rider.png',roi)
except Exception as e:
print("Error",str(e))
try:
x1,y1,x2,y2 = int(xyxy[0]), int(xyxy[1]), int(xyxy[2]), int(xyxy[3])
roi = im0s[y1:y2, x1:x2]
cv2.imwrite('rider.png',roi)
except Exception as e:
print("Error 1",str(e))
rid_dataset = LoadImages('rider.png', img_size=imgsz, stride=stride)
rid_t0 = time.time()
for rid_path, rid_img, rid_im0s, rid_vid_cap in rid_dataset:
rid_img = torch.from_numpy(rid_img).to(device)
rid_img = rid_img.half() if half else rid_img.float() # uint8 to fp16/32
rid_img /= 255.0 # 0 - 255 to 0.0 - 1.0
if rid_img.ndimension() == 3:
rid_img = rid_img.unsqueeze(0)
rid_pred = model(rid_img, augment=opt.augment)[0]
# Apply NMS
rid_pred = non_max_suppression(rid_pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
# Apply Classifier
if classify:
rid_pred = apply_classifier(rid_pred, modelc, rid_img, rid_im0s)
# Process detections
for rid_i, rid_det in enumerate(rid_pred): # detections per image
rid_p, rid_s, rid_im0, rid_frame = rid_path, '', rid_im0s.copy(), getattr(rid_dataset, 'frame', 0)
rid_p = Path(rid_p) # to Path
rid_s += '%gx%g ' % rid_img.shape[2:] # print string
if len(rid_det):
# print(rid_det)
# Rescale boxes from img_size to im0 size
rid_det[:, :4] = scale_coords(rid_img.shape[2:], rid_det[:, :4], rid_im0.shape).round()
# Print results
for rid_c in rid_det[:, -1].unique():
rid_n = (rid_det[:, -1] == rid_c).sum() # detections per class
rid_s += f"{rid_n} {names[int(rid_c)]}{'s' * (rid_n > 1)}, " # add to string
# Write results
for *xyxy, rid_conf, cls in rid_det:
rid_c = int(cls) # integer class
rid_label = f'{names[rid_c]} {rid_conf:.2f}'
plot_one_box(xyxy, rid_im0, label=rid_label, color=colors[rid_c], line_thickness=3)
if names[rid_c] =="Helmet":
rider_helmet_status = True
no_of_passengers = no_of_passengers + 1
if names[rid_c] =="No Helmet":
rider_helmet_status = False
no_of_passengers = no_of_passengers + 1
if names[rid_c] =="LP":
try:
x1,y1,x2,y2 = int(xyxy[0])-50, int(xyxy[1])-50, int(xyxy[2])+50, int(xyxy[3])+50
lp_roi = roi[y1:y2, x1:x2]
cv2.imwrite('rider_lp.png',lp_roi)
except Exception as e:
x1,y1,x2,y2 = int(xyxy[0]), int(xyxy[1]), int(xyxy[2]), int(xyxy[3])
lp_roi = roi[y1:y2, x1:x2]
cv2.imwrite('rider_lp.png',lp_roi)
regions = ['mx', 'in'] # Change to your country
with open("rider_lp.png", 'rb') as fp:
response = requests.post(
'https://api.platerecognizer.com/v1/plate-reader/',
data=dict(regions=regions), # Optional
files=dict(upload=fp),
headers={'Authorization': 'Token 5cb2b9e847d8f063dc54b2fc7eac9c769c3ac4c5'})
try:
rider_lp_number = response.json()['results'][0]['plate']
except Exception as e:
pass
# print('\nALPR not able to detect',str(e))
fp.close()
os.remove('rider_lp.png')
rider_lp_status = True
# print(names[rid_c])q
# print(xyxy)
if rider_helmet_status:
print('\n\nRider wearing Helmet')
else:
print('\n\nRider not wearing Helmet')
if rider_lp_status:
print('\nRider having LP')
else:
print('\nRider not having LP\n\n')
print('\nPlate Number : ',rider_lp_number )
print('\nNo. of passengers : ',no_of_passengers )
# if rider_helmet_status == False or no_of_passengers>=3:
print('Voilence found')
if str(rider_helmet_status)+'\n'+str(rider_lp_status)+'\n'+str(rider_lp_number)+'\n'+str(no_of_passengers) not in existingOutputs:
cv2.imwrite('output/Det_'+str(imgCounter)+'.png',rid_im0)
existingOutputs.append(str(rider_helmet_status)+'\n'+str(rider_lp_status)+'\n'+str(rider_lp_number)+'\n'+str(no_of_passengers))
lines = 'output/Det_'+str(imgCounter)+'.png\n'+str(rider_helmet_status)+'\n'+str(rider_lp_status)+'\n'+str(rider_lp_number)+'\n'+str(no_of_passengers)+"\nNot"
with open('output/Det_'+str(imgCounter)+'.txt', 'w') as f:
f.writelines(lines)
imgCounter = imgCounter+1
cv2.imshow('Output', im0)
cv2.waitKey(0) # 1 millisecond
except Exception as e:
print(e)
def start_detecttion(file=None):
files = glob.glob('output/*')
for f in files:
os.remove(f)
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='./runs/train/finalModel/weights/best.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='dataset/t_image/', help='source')
parser.add_argument('--img-size', type=int, default=448, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
parser.add_argument('--device', default='cpu', 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('--save-crop', action='store_true', help='save cropped prediction boxes')
parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
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()
if file != None:
opt.source = file
detect(opt)
start_detecttion(r"F:\Helmet_Number Plate Detection-GUI\final\test_images\498490_1_En_37_Fig2_HTML.png")