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hand_detection.py
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import cv2
import argparse
import orien_lines
import datetime
from imutils.video import VideoStream
from utils import detector_utils as detector_utils
import pandas as pd
from datetime import date
import xlrd
from xlwt import Workbook
from xlutils.copy import copy
import numpy as np
lst1=[]
lst2=[]
ap = argparse.ArgumentParser()
ap.add_argument('-d', '--display', dest='display', type=int,
default=1, help='Display the detected images using OpenCV. This reduces FPS')
args = vars(ap.parse_args())
detection_graph, sess = detector_utils.load_inference_graph()
def save_data(no_of_time_hand_detected, no_of_time_hand_crossed):
try:
today = date.today()
today=str(today)
#loc = (r'C:\Users\rahul.tripathi\Desktop\result.xls')
rb = xlrd.open_workbook('result.xls')
sheet = rb.sheet_by_index(0)
sheet.cell_value(0, 0)
#print(sheet.nrows)
q=sheet.cell_value(sheet.nrows-1,1)
rb = xlrd.open_workbook('result.xls')
#rb = xlrd.open_workbook(loc)
wb=copy(rb)
w_sheet=wb.get_sheet(0)
if q==today:
w=sheet.cell_value(sheet.nrows-1,2)
e=sheet.cell_value(sheet.nrows-1,3)
w_sheet.write(sheet.nrows-1,2,w+no_of_time_hand_detected)
w_sheet.write(sheet.nrows-1,3,e+no_of_time_hand_crossed)
wb.save('result.xls')
else:
w_sheet.write(sheet.nrows,0,sheet.nrows)
w_sheet.write(sheet.nrows,1,today)
w_sheet.write(sheet.nrows,2,no_of_time_hand_detected)
w_sheet.write(sheet.nrows,3,no_of_time_hand_crossed)
wb.save('result.xls')
except FileNotFoundError:
today = date.today()
today=str(today)
# Workbook is created
wb = Workbook()
# add_sheet is used to create sheet.
sheet = wb.add_sheet('Sheet 1')
sheet.write(0, 0, 'Sl.No')
sheet.write(0, 1, 'Date')
sheet.write(0, 2, 'Number of times hand detected')
sheet.write(0, 3, 'Number of times hand crossed')
m=1
sheet.write(1, 0, m)
sheet.write(1, 1, today)
sheet.write(1, 2, no_of_time_hand_detected)
sheet.write(1, 3, no_of_time_hand_crossed)
wb.save('result.xls')
if __name__ == '__main__':
# Detection confidence threshold to draw bounding box
score_thresh = 0.80
#vs = cv2.VideoCapture('rtsp://192.168.1.64')
vs = VideoStream(0).start()
#Oriendtation of machine
Orientation= 'bt'
#input("Enter the orientation of hand progression ~ lr,rl,bt,tb :")
#For Machine
#Line_Perc1=float(input("Enter the percent of screen the line of machine :"))
Line_Perc1=float(15)
#For Safety
#Line_Perc2=float(input("Enter the percent of screen for the line of safety :"))
Line_Perc2=float(30)
# max number of hands we want to detect/track
num_hands_detect = 2
# Used to calculate fps
start_time = datetime.datetime.now()
num_frames = 0
im_height, im_width = (None, None)
cv2.namedWindow('Detection', cv2.WINDOW_NORMAL)
def count_no_of_times(lst):
x=y=cnt=0
for i in lst:
x=y
y=i
if x==0 and y==1:
cnt=cnt+1
return cnt
try:
while True:
frame = vs.read()
frame = np.array(frame)
#frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
if im_height == None:
im_height, im_width = frame.shape[:2]
# Convert image to rgb since opencv loads images in bgr, if not accuracy will decrease
try:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
except:
print("Error converting to RGB")
#cv2.line(img=frame, pt1=(0, Line_Position1), pt2=(frame.shape[1], Line_Position1), color=(255, 0, 0), thickness=2, lineType=8, shift=0)
#cv2.line(img=frame, pt1=(0, Line_Position2), pt2=(frame.shape[1], Line_Position2), color=(255, 0, 0), thickness=2, lineType=8, shift=0)
# Run image through tensorflow graph
boxes, scores, classes = detector_utils.detect_objects(
frame, detection_graph, sess)
Line_Position2=orien_lines.drawsafelines(frame,Orientation,Line_Perc1,Line_Perc2)
# Draw bounding boxeses and text
a,b=detector_utils.draw_box_on_image(
num_hands_detect, score_thresh, scores, boxes, classes, im_width, im_height, frame,Line_Position2,Orientation)
lst1.append(a)
lst2.append(b)
no_of_time_hand_detected=no_of_time_hand_crossed=0
# Calculate Frames per second (FPS)
num_frames += 1
elapsed_time = (datetime.datetime.now() -
start_time).total_seconds()
fps = num_frames / elapsed_time
if args['display']:
# Display FPS on frame
detector_utils.draw_text_on_image("FPS : " + str("{0:.2f}".format(fps)), frame)
cv2.imshow('Detection', cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destroyAllWindows()
vs.stop()
break
no_of_time_hand_detected=count_no_of_times(lst2)
#no_of_time_hand_detected=b
no_of_time_hand_crossed=count_no_of_times(lst1)
#print(no_of_time_hand_detected)
#print(no_of_time_hand_crossed)
save_data(no_of_time_hand_detected, no_of_time_hand_crossed)
print("Average FPS: ", str("{0:.2f}".format(fps)))
except KeyboardInterrupt:
no_of_time_hand_detected=count_no_of_times(lst2)
no_of_time_hand_crossed=count_no_of_times(lst1)
today = date.today()
save_data(no_of_time_hand_detected, no_of_time_hand_crossed)
print("Average FPS: ", str("{0:.2f}".format(fps)))