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Lennart Heimbs 2019-08-01 20:06:41 +00:00
parent 57f8e55d2a
commit 36bba050e7
2 changed files with 84 additions and 15 deletions
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54
camera/image_presence.py Normal file
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@ -0,0 +1,54 @@
#!/usr/bin/env python
# import the necessary packages
from __future__ import print_function
from imutils.object_detection import non_max_suppression
from imutils import paths
import numpy as np
import argparse
import imutils
import cv2
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--images", required=True, help="path to images directory")
args = vars(ap.parse_args())
# initialize the HOG descriptor/person detector
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
# loop over the image paths
for imagePath in paths.list_images(args["images"]):
# load the image and resize it to (1) reduce detection time
# and (2) improve detection accuracy
image = cv2.imread(imagePath)
image = imutils.resize(image, width=min(400, image.shape[1]))
orig = image.copy()
# detect people in the image
(rects, weights) = hog.detectMultiScale(image, winStride=(4, 4),
padding=(8, 8), scale=1.05)
# draw the original bounding boxes
for (x, y, w, h) in rects:
cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2)
# apply non-maxima suppression to the bounding boxes using a
# fairly large overlap threshold to try to maintain overlapping
# boxes that are still people
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)
# draw the final bounding boxes
for (xA, yA, xB, yB) in pick:
cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)
# show some information on the number of bounding boxes
filename = imagePath[imagePath.rfind("/") + 1:]
print("[INFO] {}: {} original boxes, {} after suppression".format(filename, len(rects), len(pick)))
# show the output images
#cv2.imshow("Before NMS", orig)
#cv2.imshow("After NMS", image)
#cv2.waitKey(0)

45
camera/video_presence.py
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@ -1,3 +1,5 @@
#!/usr/bin/env python
from imutils.video import VideoStream
from imutils.video import FPS
import argparse
@ -58,8 +60,10 @@ differ = None
now = ''
framecounter = 0
trackeron = 0
people_count_total = 0
while True:
people_count_per_frame = 0
frame = vs.read()
frame = frame if args.get("video", None) is None else frame[1]
# if the frame can not be grabbed, then we have reached the end of the video
@ -87,15 +91,19 @@ while True:
# dilate the thresholded image to fill in holes, then find contours on thresholded image
thresh = cv2.dilate(thresh, None, iterations=2)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
thresh = np.uint8(thresh)
cnts, im2 = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
#cnts = cnts if imutils.is_cv2() else im2
#print(len(cnts))
#if len(cnts) > 1:
#cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# loop over the contours identified
contourcount = 0
for c in cnts:
contourcount = contourcount + 1
# if the contour is too small, ignore it
# if the contour is too small, ignore it
if cv2.contourArea(c) < args["min_area"]:
continue
@ -103,8 +111,8 @@ while True:
(x, y, w, h) = cv2.boundingRect(c)
initBB2 =(x,y,w,h)
prott1 = r'ML-Models\MobileNetSSD_deploy.prototxt'
prott2 = r'ML-Models\MobileNetSSD_deploy.caffemodel'
prott1 = r'ML-Models/MobileNetSSD_deploy.prototxt'
prott2 = r'ML-Models/MobileNetSSD_deploy.caffemodel'
net = cv2.dnn.readNetFromCaffe(prott1, prott2)
CLASSES = ["person"]
@ -120,22 +128,28 @@ while True:
for i in np.arange(0, detections.shape[2]):
confidence = detections[0, 0, i, 2]
confidence_level = 0.7
confidence_level = 0.8
if confidence > confidence_level:
people_count_per_frame+=1
people_count_total+=1
# 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([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# draw the prediction on the frame
label = "{}: {:.2f}%".format(CLASSES[idx],
confidence * 100)
cv2.rectangle(frame, (startX, startY), (endX, endY),
COLORS[idx], 2)
#label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
label = "{}: {:.2f}%".format(CLASSES[0], confidence * 100)
#cv2.rectangle(frame, (startX, startY), (endX, endY), COLORS[idx], 2)
cv2.rectangle(frame, (startX, startY), (endX, endY), COLORS[0], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(frame, label, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
#cv2.putText(frame, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
cv2.putText(frame, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[0], 2)
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 255, 0), 2)
# Start tracker
@ -146,7 +160,7 @@ while True:
# check to see if we are currently tracking an object, if so, ignore other boxes
# this code is relevant if we want to identify particular persons (section 2 of this tutorial)
# this code is relevant if we want to identify particular persons
if initBB2 is not None:
# grab the new bounding box coordinates of the object
@ -173,6 +187,8 @@ while True:
info = [
("Success", "Yes" if success else "No"),
("FPS", "{:.2f}".format(fps.fps())),
("People Frame", "{}".format(people_count_per_frame)),
("People Total", "{}".format(people_count_total))
]
# loop over the info tuples and draw them on our frame
@ -183,8 +199,7 @@ while True:
# draw the text and timestamp on the frame
now2 = datetime.now()
time_passed_seconds = str((now2-now).seconds)
cv2.putText(frame, 'Detecting persons',(10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.putText(frame, 'Detecting persons',(10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
# show the frame and record if the user presses a key
cv2.imshow("Video stream", frame)