From 13549bb5947ad7303b51dedf3633034414015648 Mon Sep 17 00:00:00 2001 From: heimbsle69869 Date: Mon, 5 Aug 2019 10:15:09 +0200 Subject: [PATCH] add timing to person detection --- camera/counter_people.py | 11 ++-- camera/person_detection.py | 124 ++++++++++++++++++++++++++++--------- 2 files changed, 100 insertions(+), 35 deletions(-) diff --git a/camera/counter_people.py b/camera/counter_people.py index f63868b..d245fd6 100755 --- a/camera/counter_people.py +++ b/camera/counter_people.py @@ -1,11 +1,12 @@ #!/usr/bin/env python -import numpy as np -import imutils -import cv2 import argparse -from video_stream import imagezmq +import numpy as np +import cv2 +import imutils from imutils.object_detection import non_max_suppression +from video_stream import imagezmq + ''' Usage: @@ -25,7 +26,7 @@ def detector(image): clone = image.copy() - (rects, weights) = HOGCV.detectMultiScale(image, winStride=(4, 4), padding=(8, 8), scale=1.05) + (rects, _) = HOGCV.detectMultiScale(image, winStride=(4, 4), padding=(8, 8), scale=1.05) # draw the original bounding boxes for (x, y, w, h) in rects: diff --git a/camera/person_detection.py b/camera/person_detection.py index 6543c75..2d8ebf4 100755 --- a/camera/person_detection.py +++ b/camera/person_detection.py @@ -1,15 +1,26 @@ #!/usr/bin/env python -from imutils.video import VideoStream -from imutils.video import FPS import argparse -import imutils -import cv2 -from datetime import datetime, time -import numpy as np -import time as time2 +#from datetime import datetime, time +import time +from statistics import median -VISUAL_DEBUG=True +import imutils +from imutils.video import VideoStream +#from imutils.video import FPS + +import cv2 +import numpy as np + +frame_timer = None +contour_timer = None +detection_timer = None + +frame_time = [] +contour_time = [] +detection_time = [] + +VISUAL_DEBUG = True def getArgs(): """ Arguments """ @@ -22,18 +33,20 @@ def getArgs(): def main(): args = getArgs() + timer = Timer() # if the video argument is None, then the code will read from webcam (work in progress) if args.get("video", None) is None: vs = VideoStream(src=0).start() - time2.sleep(2.0) + time.sleep(2.0) # otherwise, we are reading from a video file else: vs = cv2.VideoCapture(args["video"]) cv2.namedWindow('Video stream', cv2.WINDOW_NORMAL) - detector = DetectionFromFrame(args["min_area"], 0.8) + detector = DetectionFromFrame(args["min_area"], 0.5) while True: + timer.start_frame_timer() detector.currentFrame = vs.read() detector.currentFrame = detector.currentFrame if args.get("video", None) is None else detector.currentFrame[1] # if the frame can not be grabbed, then we have reached the end of the video @@ -42,12 +55,13 @@ def main(): # resize the frame to 500 detector.currentFrame = imutils.resize(detector.currentFrame, width=500) - detector.framecounter+=1 + detector.framecounter += 1 if detector.framecounter > 1: cnts = detector.prepareFrame() - + for c in cnts: - boundRect = cv2.boundingRect(c) + timer.start_contour_timer() + bound_rect = cv2.boundingRect(c) #(x, y, w, h) = cv2.boundingRect(c) #initBB2 =(x,y,w,h) @@ -56,19 +70,24 @@ def main(): net = cv2.dnn.readNetFromCaffe(prott1, prott2) #trackbox = detector.currentFrame[y:y+h, x:x+w]boundRect[1] - trackbox = detector.currentFrame[boundRect[1]:boundRect[1]+boundRect[3], - boundRect[0]:boundRect[0]+boundRect[2]] + trackbox = detector.currentFrame[bound_rect[1]:bound_rect[1]+bound_rect[3], + bound_rect[0]:bound_rect[0]+bound_rect[2]] trackbox = cv2.resize(trackbox, (224, 224)) #cv2.imshow('image',trackbox) - + timer.start_detection_timer() blob = cv2.dnn.blobFromImage(cv2.resize(trackbox, (300, 300)),0.007843, (300, 300), 127.5) net.setInput(blob) detections = net.forward() - + for i in np.arange(0, detections.shape[2]): - detector.detectConfidentiallyPeople(i, detections, boundRect) - cv2.rectangle(detector.currentFrame, (boundRect[0], boundRect[1]), - (boundRect[0] + boundRect[2], boundRect[1] + boundRect[3]), (255, 255, 0), 1) + + detector.detectConfidentiallyPeople(i, detections, bound_rect) + timer.stop_detection_timer() + + cv2.rectangle(detector.currentFrame, (bound_rect[0], bound_rect[1]), + (bound_rect[0] + bound_rect[2], bound_rect[1] + bound_rect[3]), (255, 255, 0), 1) + + timer.stop_contour_timer() # show the frame and record if the user presses a key @@ -82,10 +101,55 @@ def main(): detector.firstFrame = None #detector.lastFrame = detector.currentFrame + timer.print_time() + # finally, stop the camera/stream and close any open windows vs.stop() if args.get("video", None) is None else vs.release() cv2.destroyAllWindows() +class Timer: + def __init__(self): + self.frame_timer = None + self.contour_timer = None + self.detection_timer = None + + self.contour_time = [] + self.detection_time = [] + + def start_frame_timer(self): + self.frame_timer = time.time() + + def get_frame_time(self): + return time.time() - self.frame_timer + + def start_contour_timer(self): + self.contour_timer = time.time() + + def stop_contour_timer(self): + self.contour_time.append(time.time() - self.contour_timer) + + def start_detection_timer(self): + self.detection_timer = time.time() + + def stop_detection_timer(self): + self.detection_time.append(time.time() - self.detection_timer) + + def print_time(self): + average_contour = 0 if not self.contour_time else sum(self.contour_time)/float(len(self.contour_time)) + average_detection = 0 if not self.detection_time else sum(self.detection_time)/float(len(self.detection_time)) + + median_contour = 0 if not self.contour_time else median(self.contour_time) + median_detection = 0 if not self.detection_time else median(self.detection_time) + + total_contour = sum(self.contour_time) + total_detection = sum(self.detection_time) + + print("Time for Frame: {:.2f}. Contour Total: {:.2f}. Contour Median: {:.2f}. Contour Average: {:.2f}. Detection Total: {:.2f}. Detection Median: {:.2f}. Detection Average: {:.2f}. ".format( + self.get_frame_time(), total_contour, median_contour, average_contour, total_detection, median_detection, average_detection)) + #print("Contour Times:" + str(timer.contour_time)) + #print("Detection Times:" + str(timer.detection_time)) + self.contour_time = [] + self.detection_time = [] class DetectionFromFrame: def __init__(self, min_size, confidence): @@ -132,10 +196,10 @@ class DetectionFromFrame: return cnts - def detectConfidentiallyPeople(self, i, detections, boundRect): - CLASSES = ["person"] + def detectConfidentiallyPeople(self, i, detections, bound_rect): + #CLASSES = ["person"] - COLORS = [0,255,0] + detected_color = (0, 255, 0) #COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3)) confidence = detections[0, 0, i, 2] @@ -144,21 +208,21 @@ class DetectionFromFrame: # extract the index of the class label from the `detections`, then compute the (x, y)-coordinates of # the bounding box for the object #idx = int(detections[0, 0, i, 1]) - box = detections[0, 0, i, 3:7] * np.array([boundRect[2], boundRect[3], boundRect[2], boundRect[3]]) - (startX, startY, endX, endY) = box.astype("int") + #box = detections[0, 0, i, 3:7] * np.array([bound_rect[2], bound_rect[3], bound_rect[2], bound_rect[3]]) + #(startX, startY, endX, endY) = box.astype("int") # draw the prediction on the frame #label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100) - label = "{}: {:.2f}%".format(CLASSES[0], confidence * 100) + label = "{:.2f}%".format(confidence * 100) #cv2.rectangle(frame, (startX, startY), (endX, endY), COLORS[idx], 2) - cv2.rectangle(self.currentFrame, (boundRect[0], boundRect[1]), - (boundRect[0] + boundRect[2], boundRect[1] + boundRect[3]), (0,255, 0), 3) + cv2.rectangle(self.currentFrame, (bound_rect[0], bound_rect[1]), + (bound_rect[0] + bound_rect[2], bound_rect[1] + bound_rect[3]), detected_color, 3) - y = boundRect[1] - 15 if boundRect[1] - 15 > 15 else boundRect[1] + 15 + y = bound_rect[1] - 15 if bound_rect[1] - 15 > 15 else bound_rect[1] + 15 #cv2.putText(frame, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2) - cv2.putText(self.currentFrame, label, (0, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1) + cv2.putText(self.currentFrame, label, (bound_rect[0], bound_rect[1]-5), cv2.FONT_HERSHEY_SIMPLEX, 0.3, detected_color, 1) #cv2.imshow("Video stream", self.currentFrame) #print("Person found")