pedestrian_detection.py

# PREP DEPENDENCIES
from PyQt5.QtCore import Qt, pyqtSignal, QThread  # To send out video frames to our PyQt5 GUI page
from PyQt5.QtGui import QImage  # To convert the OpenCV frames into PyQt5 image
import cv2 as cv  # For enabling computer vision
import numpy as np  # For SCi-Calculations
from imutils.object_detection import non_max_suppression  # To detect multiple humans in a single frame

# Variable to record total detections
global person

# Histogram of Oriented Gradients Detector
HOGCV = cv.HOGDescriptor()
HOGCV.setSVMDetector(cv.HOGDescriptor_getDefaultPeopleDetector())


# Detect Humans from the frames
def detect_persons(frame):
	global person
	# USing Sliding window search to detect humans in the frame
	rects, weights = HOGCV.detectMultiScale(frame, winStride=(4, 4), padding=(8, 8), scale=2)
	rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])
	pick = non_max_suppression(rects, probs=None, overlapThresh=0.65)
	person = 1
	return rects, weights, pick, person


# Draw bounding boxes around detected persons
def draw_bounding_boxes(pick, frame):
	global person
	# Draw bounding boxes for each detection
	for x, y, w, h in pick:
		cv.rectangle(frame, (x, y), (w, h), (255, 255, 255), 1)
		cv.rectangle(frame, (x, y - 20), (w, y), (33, 33, 33), -1)
		cv.putText(frame, f'P{person}', (x, y), cv.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
		person += 1


# Pedestrian Detection Thread class (The PDS will run in a separate thread when called by the start button from GUI pge)
class StartPDS(QThread):
	# Variable that will be sent to GUI page
	ImageUpdate = pyqtSignal(QImage)
	TotalPeople = pyqtSignal(str)
	
	def __init__(self):
		super().__init__()
		self.ThreadActive = None
	
	# {Pedestrian} Detection begins here (The method will run when the thread starts)
	def run(self):
		# Activating thread
		global person
		self.ThreadActive = True
		# Capture video
		video_stream = cv.VideoCapture("dependencies/video/pds2.mp4")
		# While the PDS thread is active, Do detections
		while self.ThreadActive:
			# Set the FPS cap on video
			cv.waitKey(5)
			# Split video into frames
			ret, frame = video_stream.read()
			if ret:
				try:
					# Resize the frame
					frame = cv.resize(frame, (480, 315))
					# detect persons
					rects, weights, pick, person = detect_persons(frame)
					# Draw bounding boxes
					draw_bounding_boxes(pick, frame)
					# Set the total detections value string that wil be sent to the GUI frame
					total_people = f"People: {person - 1}"
					# Convert the frame from OpenCV format to PyQt5 format
					frame = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
					convert_to_qt_format = QImage(
						frame.data, frame.shape[1],
						frame.shape[0],
						QImage.Format_RGB888)
					frame = convert_to_qt_format.scaled(720, 405, Qt.KeepAspectRatio)
					
					# Send the frames and Stats to the GUI window
					self.ImageUpdate.emit(frame)
					self.TotalPeople.emit(total_people)
				
				except TypeError or ValueError or AttributeError or Exception:
					print("Trying to Read the footage!")
					pass
		# Close all opened windows and stop video Capture
		video_stream.release()
		cv.destroyAllWindows()
	
	# Thread stop method (De-Activate the thread and Quit the operation when method is called from GUI window)
	def stop(self):
		self.ThreadActive = False
		self.wait()