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detect_video.py
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detect_video.py
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from __future__ import division
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import cv2
from util import *
from DNModel import net as Darknet
from img_process import inp_to_image, custom_resize
import pandas as pd
import random
import pickle as pkl
import argparse
import test
import serial
#from serial import Serial
def prepare_input(img, inp_dim):
"""
Prepare image for inputting to the neural network.
Perform tranpose and return Tensor
"""
orig_im = img
dim = orig_im.shape[1], orig_im.shape[0]
img = (custom_resize(orig_im, (inp_dim, inp_dim)))
img_ = img[:,:,::-1].transpose((2,0,1)).copy()
img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
return img_, orig_im, dim
def write(x,img):
h,w,_ = img.shape
c1 = tuple(x[1:3].int())
c2 = tuple(x[3:5].int())
cls = int(x[-1])
label = "{0}".format(classes[cls])
color = random.choice(colors)
th_y = 550
cv2.line(img, (0,th_y), (w,th_y), (255,0,0), 5)
'''
to detect the elephant
'''
if label == 'elephant':
cv2.rectangle(img, c1, c2, (0,255,255),2)
cv2.putText(img, label, (c1[0], c1[1]-10), 1,1, (255,255,0),2)
if c2[1] > th_y:
data = '1'
#print('label')
ser.write(data.encode())
# '''uncomment the below code to detect the person from the camara'''
# if label == 'person':
# cv2.rectangle(img, c1, c2, (0,255,255),2)
# cv2.putText(img, label, (c1[0], c1[1]-10), 1,1, (255,255,0),2)
# if c2[1] < th_y:
# data = '0'
# #print('label')
# ser.write(data.encode())
def arg_parse():
"""
Parse arguements to the detect module
"""
parser = argparse.ArgumentParser(description='YOLO v3 Video Detection Module')
parser.add_argument("--video", dest = 'video', help =
"Video to run detection upon",
default = "shreetrim24.avi", type = str)
parser.add_argument("--dataset", dest = "dataset", help = "Dataset on which the network has been trained", default = "pascal")
parser.add_argument("--confidence", dest = "confidence", help = "Object Confidence to filter predictions", default = 0.5)
parser.add_argument("--nms_thresh", dest = "nms_thresh", help = "NMS Threshhold", default = 0.4)
parser.add_argument("--cfg", dest = 'cfgfile', help =
"Config file",
default = "cfg/yolov3.cfg", type = str)
parser.add_argument("--weights", dest = 'weightsfile', help =
"weightsfile",
default = "yolov3.weights", type = str)
parser.add_argument("--reso", dest = 'reso', help =
"Input resolution of the network. Increase to increase accuracy. Decrease to increase speed",
default = "320", type = str)
return parser.parse_args()
if __name__ == '__main__':
args = arg_parse()
ser = serial.Serial('/dev/ttyUSB0', 9600)
confidence = float(args.confidence)
nms_thesh = float(args.nms_thresh)
start = 0
CUDA = torch.cuda.is_available()
num_classes = 80
bbox_attrs = 5 + num_classes
print("Loading network")
model = Darknet(args.cfgfile)
model.load_weights(args.weightsfile)
print("Network loaded")
classes = load_classes('data/coco.names')
colors = pkl.load(open("pallete", "rb"))
model.DNInfo["height"] = args.reso
inp_dim = int(model.DNInfo["height"])
if CUDA:
model.cuda()
model.eval()
videofile = args.video
'''
put 0 to capture the image from the camara
cap = cv2.VideoCapture(0)
'''
cap = cv2.VideoCapture(videofile)
assert cap.isOpened(), 'Cannot capture source'
while cap.isOpened():
ret, frame = cap.read()
frame = cv2.resize(frame, (1240,950))
if ret:
img, orig_im, dim = prepare_input(frame, inp_dim)
im_dim = torch.FloatTensor(dim).repeat(1,2)
if CUDA:
im_dim = im_dim.cuda()
img = img.cuda()
with torch.no_grad():
output = model(Variable(img), CUDA)
output = write_results(output, confidence, num_classes, nms = True, nms_conf = nms_thesh)
if type(output) == int:
cv2.imshow("frame", orig_im)
key = cv2.waitKey(1)
if key & 0xFF == ord('x'):
break
continue
im_dim = im_dim.repeat(output.size(0), 1)
scaling_factor = torch.min(inp_dim/im_dim,1)[0].view(-1,1)
output[:,[1,3]] -= (inp_dim - scaling_factor*im_dim[:,0].view(-1,1))/2
output[:,[2,4]] -= (inp_dim - scaling_factor*im_dim[:,1].view(-1,1))/2
output[:,1:5] /= scaling_factor
for i in range(output.shape[0]):
output[i, [1,3]] = torch.clamp(output[i, [1,3]], 0.0, im_dim[i,0])
output[i, [2,4]] = torch.clamp(output[i, [2,4]], 0.0, im_dim[i,1])
list(map(lambda x: write(x, orig_im), output))
cv2.imshow("Object Detect", orig_im)
key = cv2.waitKey(1)
if key & 0xFF == ord('x'):
break
else:
break