Final versions of camera-tools
This commit is contained in:
Lennart Heimbs
parent
949649c9a9
commit
7a99fdc45c
@ -17,6 +17,7 @@ python peopleCounter.py -c # Attempts to detect people using webcam
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HOGCV = cv2.HOGDescriptor()
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HOGCV.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
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#HOGCV.set
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VERBOSITY = False
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def detector(image):
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@ -26,7 +27,9 @@ def detector(image):
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clone = image.copy()
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(rects, _) = HOGCV.detectMultiScale(image, winStride=(4, 4), padding=(8, 8), scale=1.05)
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(rects, _) = HOGCV.detectMultiScale(image, winStride=(2, 2), padding=(8, 8), scale=1.05)
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# draw the original bounding boxes
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for (x, y, w, h) in rects:
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@ -129,10 +132,31 @@ def videoDetect(cap):
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def remoteDetect(image_hub):
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while True:
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rpi_name, image = image_hub.recv_image()
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cv2.imshow(rpi_name, image) # 1 window for each RPi
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cv2.waitKey(1)
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rpi_name, frame = image_hub.recv_image()
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image_hub.send_reply(b'OK')
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frame = imutils.resize(frame, width=min(400, frame.shape[1]))
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result = detector(frame.copy())
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# shows the result
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for (xA, yA, xB, yB) in result:
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cv2.rectangle(frame, (xA, yA), (xB, yB), (0, 255, 0), 2)
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#if VERBOSITY:
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cv2.imshow('frame', frame)
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#cv2.waitKey(0)
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#if time.time() - init >= sample_time:
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if len(result):
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print("{} people detected.".format(len(result)))
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#init = time.time()
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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#cv2.imshow(rpi_name, frame) # 1 window for each RPi
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#cv2.waitKey(1)
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@ -1,155 +1,120 @@
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#!/usr/bin/env python
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import argparse
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#from datetime import datetime, time
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import time
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from statistics import median
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import imutils
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from imutils.video import VideoStream
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#from imutils.video import FPS
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import cv2
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import numpy as np
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frame_timer = None
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contour_timer = None
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detection_timer = None
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frame_time = []
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contour_time = []
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detection_time = []
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import paho.mqtt.client as mqtt
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from video_stream import imagezmq
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VISUAL_DEBUG = True
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BROKER = "141.75.33.126"
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PORT = 1883
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def getArgs():
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""" Arguments """
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ap = argparse.ArgumentParser()
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ap.add_argument("-v", "--video", help="path to the video file")
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ap.add_argument("-a", "--min-area", type=int, default=500, help="minimum area size")
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ap.add_argument("-t", "--tracker", type=str, default="csrt", help="OpenCV object tracker type")
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return vars(ap.parse_args())
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def main():
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args = getArgs()
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timer = Timer()
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try:
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mqtt_client = mqtt.Client("pi-camera")
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mqtt_client.connect(BROKER, PORT)
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except:
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print("Connection to MQTT-Broker failed.")
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return 1
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# if the video argument is None, then the code will read from webcam (work in progress)
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if args.get("video", None) is None:
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vs = VideoStream(src=0).start()
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time.sleep(2.0)
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# otherwise, we are reading from a video file
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else:
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vs = cv2.VideoCapture(args["video"])
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cv2.namedWindow('Video stream', cv2.WINDOW_NORMAL)
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detector = DetectionFromFrame(args["min_area"], 0.5)
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while True:
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timer.start_frame_timer()
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detector.currentFrame = vs.read()
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detector.currentFrame = detector.currentFrame if args.get("video", None) is None else detector.currentFrame[1]
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# if the frame can not be grabbed, then we have reached the end of the video
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if detector.currentFrame is None:
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break
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# resize the frame to 500
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detector.currentFrame = imutils.resize(detector.currentFrame, width=500)
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detector.framecounter += 1
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if detector.framecounter > 1:
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cnts = detector.prepareFrame()
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for c in cnts:
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timer.start_contour_timer()
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bound_rect = cv2.boundingRect(c)
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#(x, y, w, h) = cv2.boundingRect(c)
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#initBB2 =(x,y,w,h)
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prott1 = r'ML-Models/MobileNetSSD_deploy.prototxt'
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prott2 = r'ML-Models/MobileNetSSD_deploy.caffemodel'
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net = cv2.dnn.readNetFromCaffe(prott1, prott2)
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#trackbox = detector.currentFrame[y:y+h, x:x+w]boundRect[1]
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trackbox = detector.currentFrame[bound_rect[1]:bound_rect[1]+bound_rect[3],
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bound_rect[0]:bound_rect[0]+bound_rect[2]]
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trackbox = cv2.resize(trackbox, (224, 224))
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#cv2.imshow('image',trackbox)
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timer.start_detection_timer()
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blob = cv2.dnn.blobFromImage(cv2.resize(trackbox, (300, 300)),0.007843, (300, 300), 127.5)
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net.setInput(blob)
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detections = net.forward()
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for i in np.arange(0, detections.shape[2]):
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detector.detectConfidentiallyPeople(i, detections, bound_rect)
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timer.stop_detection_timer()
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cv2.rectangle(detector.currentFrame, (bound_rect[0], bound_rect[1]),
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(bound_rect[0] + bound_rect[2], bound_rect[1] + bound_rect[3]), (255, 255, 0), 1)
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timer.stop_contour_timer()
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try:
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args = getArgs()
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timer = Timer()
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# if the video argument is None, then the code will read from webcam (work in progress)
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if args.get("video", None) is None:
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#vs = VideoStream(src=0).start()
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image_hub = imagezmq.ImageHub()
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# show the frame and record if the user presses a key
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cv2.imshow("Video stream", detector.currentFrame)
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key = cv2.waitKey(1) & 0xFF
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time.sleep(2.0)
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# otherwise, we are reading from a video file
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else:
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vs = cv2.VideoCapture(args["video"])
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# if the `q` key is pressed, break from the lop
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if key == ord("q"):
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break
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if key == ord("d"):
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detector.firstFrame = None
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#detector.lastFrame = detector.currentFrame
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cv2.namedWindow('Video stream', cv2.WINDOW_NORMAL)
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detector = DetectionFromFrame(args["min_area"], 0.8)
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while True:
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people_count = 0
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timer.start_frame_timer()
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if args.get("video", None) is None:
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rpi_name, detector.currentFrame = image_hub.recv_image()
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image_hub.send_reply(b'OK')
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else:
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detector.currentFrame = vs.read()
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detector.currentFrame = detector.currentFrame if args.get("video", None) is None else detector.currentFrame[1]
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# if the frame can not be grabbed, then we have reached the end of the video
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if detector.currentFrame is None:
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break
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timer.print_time()
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# resize the frame to 500
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detector.currentFrame = imutils.resize(detector.currentFrame, width=500)
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detector.framecounter += 1
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if detector.framecounter > 1:
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cnts = detector.prepareFrame()
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# finally, stop the camera/stream and close any open windows
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vs.stop() if args.get("video", None) is None else vs.release()
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cv2.destroyAllWindows()
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for c in cnts:
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bound_rect = cv2.boundingRect(c)
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#(x, y, w, h) = cv2.boundingRect(c)
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#initBB2 =(x,y,w,h)
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class Timer:
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def __init__(self):
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self.frame_timer = None
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self.contour_timer = None
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self.detection_timer = None
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prott1 = r'ML-Models/MobileNetSSD_deploy.prototxt'
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prott2 = r'ML-Models/MobileNetSSD_deploy.caffemodel'
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net = cv2.dnn.readNetFromCaffe(prott1, prott2)
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self.contour_time = []
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self.detection_time = []
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#trackbox = detector.currentFrame[y:y+h, x:x+w]boundRect[1]
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trackbox = detector.currentFrame[bound_rect[1]:bound_rect[1]+bound_rect[3],
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bound_rect[0]:bound_rect[0]+bound_rect[2]]
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trackbox = cv2.resize(trackbox, (224, 224))
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#cv2.imshow('image',trackbox)
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blob = cv2.dnn.blobFromImage(cv2.resize(trackbox, (300, 300)),0.007843, (300, 300), 127.5)
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net.setInput(blob)
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detections = net.forward()
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for i in np.arange(0, detections.shape[2]):
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people_count += detector.detectConfidentiallyPeople(i, detections, bound_rect)
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def start_frame_timer(self):
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self.frame_timer = time.time()
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cv2.rectangle(detector.currentFrame, (bound_rect[0], bound_rect[1]),
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(bound_rect[0] + bound_rect[2], bound_rect[1] + bound_rect[3]), (255, 255, 0), 1)
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# show the frame and record if the user presses a key
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cv2.imshow("Video stream", detector.currentFrame)
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key = cv2.waitKey(1) & 0xFF
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def get_frame_time(self):
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return time.time() - self.frame_timer
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# send number of people detected via mqtt
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mqtt_client.publish("/gso/bb/104/Camera", str(people_count))
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def start_contour_timer(self):
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self.contour_timer = time.time()
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# if the `q` key is pressed, break from the lop
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if key == ord("q"):
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break
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if key == ord("d"):
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detector.firstFrame = None
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#detector.lastFrame = detector.currentFrame
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def stop_contour_timer(self):
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self.contour_time.append(time.time() - self.contour_timer)
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timer.print_frame_time()
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def start_detection_timer(self):
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self.detection_timer = time.time()
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def stop_detection_timer(self):
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self.detection_time.append(time.time() - self.detection_timer)
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# finally, stop the camera/stream and close any open windows
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if args.get("video", None) is not None:
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vs.stop() if args.get("video", None) is None else vs.release()
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def print_time(self):
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average_contour = 0 if not self.contour_time else sum(self.contour_time)/float(len(self.contour_time))
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average_detection = 0 if not self.detection_time else sum(self.detection_time)/float(len(self.detection_time))
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median_contour = 0 if not self.contour_time else median(self.contour_time)
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median_detection = 0 if not self.detection_time else median(self.detection_time)
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total_contour = sum(self.contour_time)
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total_detection = sum(self.detection_time)
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print("Time for Frame: {:.2f}. Contour Total: {:.2f}. Contour Median: {:.2f}. Contour Average: {:.2f}. Detection Total: {:.2f}. Detection Median: {:.2f}. Detection Average: {:.2f}. ".format(
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self.get_frame_time(), total_contour, median_contour, average_contour, total_detection, median_detection, average_detection))
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#print("Contour Times:" + str(timer.contour_time))
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#print("Detection Times:" + str(timer.detection_time))
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self.contour_time = []
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self.detection_time = []
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cv2.destroyAllWindows()
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finally:
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if args.get("video", None) is None:
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image_hub.send_reply(b'OK')
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class DetectionFromFrame:
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def __init__(self, min_size, confidence):
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@ -205,27 +170,65 @@ class DetectionFromFrame:
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confidence = detections[0, 0, i, 2]
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if confidence > self.confidence_level:
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# extract the index of the class label from the `detections`, then compute the (x, y)-coordinates of
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# the bounding box for the object
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#idx = int(detections[0, 0, i, 1])
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#box = detections[0, 0, i, 3:7] * np.array([bound_rect[2], bound_rect[3], bound_rect[2], bound_rect[3]])
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#(startX, startY, endX, endY) = box.astype("int")
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# draw the prediction on the frame
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#label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
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label = "{:.2f}%".format(confidence * 100)
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#cv2.rectangle(frame, (startX, startY), (endX, endY), COLORS[idx], 2)
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# draw a rectangle in green over the detected area
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cv2.rectangle(self.currentFrame, (bound_rect[0], bound_rect[1]),
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(bound_rect[0] + bound_rect[2], bound_rect[1] + bound_rect[3]), detected_color, 3)
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y = bound_rect[1] - 15 if bound_rect[1] - 15 > 15 else bound_rect[1] + 15
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#cv2.putText(frame, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
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label = "{:.2f}%".format(confidence * 100)
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cv2.putText(self.currentFrame, label, (bound_rect[0], bound_rect[1]-5), cv2.FONT_HERSHEY_SIMPLEX, 0.3, detected_color, 1)
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#cv2.imshow("Video stream", self.currentFrame)
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#print("Person found")
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return 1
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else:
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return 0
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class Timer:
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def __init__(self):
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self.frame_timer = None
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self.contour_timer = None
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self.detection_timer = None
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self.contour_time = []
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self.detection_time = []
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def start_frame_timer(self):
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self.frame_timer = time.time()
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def get_frame_time(self):
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return time.time() - self.frame_timer
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def start_contour_timer(self):
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self.contour_timer = time.time()
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def stop_contour_timer(self):
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self.contour_time.append(time.time() - self.contour_timer)
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def start_detection_timer(self):
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self.detection_timer = time.time()
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def stop_detection_timer(self):
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self.detection_time.append(time.time() - self.detection_timer)
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def print_frame_time(self):
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print("Time for Frame: {:.2f}.".format(self.get_frame_time()))
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def print_other_times(self):
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average_contour = 0 if not self.contour_time else sum(self.contour_time)/float(len(self.contour_time))
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average_detection = 0 if not self.detection_time else sum(self.detection_time)/float(len(self.detection_time))
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median_contour = 0 if not self.contour_time else median(self.contour_time)
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median_detection = 0 if not self.detection_time else median(self.detection_time)
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total_contour = sum(self.contour_time)
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total_detection = sum(self.detection_time)
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print("Contour Total: {:.2f}. Contour Median: {:.2f}. Contour Average: {:.2f}.".format(
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total_contour, median_contour, average_contour))
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print("Detection Total: {:.2f}. Detection Median: {:.2f}. Detection Average: {:.2f}. ".format(
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total_detection, median_detection, average_detection))
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self.contour_time = []
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self.detection_time = []
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if __name__ == "__main__":
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