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run_LSTM_test.py
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import os
import matplotlib
import matplotlib.pyplot as plt
import tensorflow as tf # Version 1.0.0 (some previous versions are used in past commits)
from sklearn import metrics
import random
from random import randint
import argparse
import logging
import time
import operator
import imutils
import cv2
import numpy as np
import math
from tf_pose.estimator import TfPoseEstimator
from tf_pose.networks import get_graph_path, model_wh
from itertools import chain, count
from sklearn.neighbors import NearestNeighbors
from collections import defaultdict
import winsound
import darknet.json as dk
import facerec.recognize as fr
# import deepface.deepface as df
import security
## Input management
CAMERA = [] # Default value, if no camera is given, switch to video mode
VIDEO = "utilities/test_vid.mp4"
REAL_FPS = 6
PROC_FPS = 3 # Proc is surely < Real
SKIP_FRAME = round(REAL_FPS/PROC_FPS) - 1
# 5th is face camera. Remove to use cailing cams cropped by FREG.
# CAMERA = [0]
# CAMERA = [0, 1]
# CAMERA = [cv2.CAP_DSHOW + 0] # Using directshow to fix black bar
# CAMERA = ["rtsp://167.205.66.187:554/onvif1"]
# CAMERA = [ "rtsp://167.205.66.147:554/onvif1",
# "rtsp://167.205.66.148:554/onvif1",
# "rtsp://167.205.66.149:554/onvif1",
# "rtsp://167.205.66.150:554/onvif1",
# cv2.CAP_DSHOW + 0 ]
# CAMERA = [ "rtsp://192.168.0.108:554/onvif1", 88:83:5D:AA:B5:52
# "rtsp://192.168.0.107:554/onvif1", 00:7E:56:B0:78:8A
# "rtsp://192.168.0.104:554/onvif1", 00:7E:56:B0:63:8A
# "rtsp://192.168.0.110:554/onvif1", 00:7E:56:B0:78:18
# cv2.CAP_DSHOW + 0 ]
# CAMERA = [ "rtsp://192.168.0.108:554/onvif1",
# "rtsp://192.168.0.107:554/onvif1",
# "rtsp://192.168.0.104:554/onvif1",
# "rtsp://192.168.0.110:554/onvif1",
# cv2.CAP_DSHOW + 0 ]
# CAMERA = [ "rtsp://192.168.0.100:554/onvif1",
# "rtsp://192.168.0.101:554/onvif1",
# "rtsp://192.168.0.103:554/onvif1",
# "rtsp://192.168.0.102:554/onvif1",
# cv2.CAP_DSHOW + 0 ]
FPSLIM = 0 # Set to 0 for unlimited
# Size of the images, act as a boundary
IMAGE = [1024,576]
SUBIM = [512,288]
ROTATE = [0, 0, 0, 0, 270]
# ROTATE = [180, 180, 180, 180, 90]
# Face camera, the fifth camera on the list
FCAMDS = 1 # Face camera downscale
# FCAMCP = [0.2, 1-0.5, 0.2, 1-0.2] # Crop fraction from top, bottom, left, right
FCAMCP = [0.35, 1-0.25, 0.2, 1-0.2] # Crop fraction from top, bottom, left, right
FCOFF = SUBIM # Center location of face camera
## System-wide parameters
# Disable/Enable the actual systems and not just visual change
SYS_OPOSE = True
SYS_ACT = SYS_OPOSE and True
# SYS_ACT = True
SYS_DARK = False
SYS_FACEREC = True
# OpenPose model parameters
# OPSIZE = "256x144"
# OPSIZE = "512x288"
# OPSIZE = "717x403"
# OPSIZE = "768x432"
OPSIZE = "1024x576"
# OPSIZE = "1280x720"
# OPSIZE = "1536x864"
# OPSIZE = "2048x1152"
OPGPU = 0./6.0 # GPU fraction limit
# LSGPU = 0./6.0
# OPGPU = 1/6.0 # GPU fraction limit
LSGPU = 0/6.0
## LSTM Parameters
# N_STEPS = 8
N_STEPS = 5
# DATASET_PATH = "data/"
# DATASET_PATH = "data/Overlap_fixed/"
# DATASET_PATH = "data/Overlap_fixed4/"
# DATASET_PATH = "data/Overlap_fixed4_separated/"
# DATASET_PATH = "data/2a_Amplify/"
# DATASET_PATH = "data/Direct2a/"
# DATASET_PATH = "data/Direct2a/Normalize/"
# DATASET_PATH = "data/Direct2a/NormalizePoint/"
DATASET_PATH = "data/Direct2a/NormalizeOnce/"
# DATASET_PATH = "data/Test/5/"
LAYER = 2 # 1: Default [36,36] # 2: Simpler [36]
## Preprocessing schemes, only applies right before the poses loaded to LSTM.
# No effect to the original pose data.
# Group A, main preprocessing:
# 1: Amplify - Poses emulated as if there's a big border between sub-images
# 2: Normalize - Individual pose returned to origin
# 3: NormalizeOnce - Every pose in a gesture will be relative to the first in the gesture
# 4: NormalizePoint - Every point in a gesture will be relative to the first point in the gesture
# 5: Reverse - Poses in 4 sub-images emulated as if happening in a single image
# Other: No preprocessing
POSEAMP = 1000 # [Amplify] Value added if a pose is over the sub-image boundary
# Group B, idle management:
# 1: Null - Unmoving gestures (average) are forced to be all null
# 2: Null - Unmoving gestures (key point [neck, or nose]) are forced to be all null
# Other: No preprocessing
IDLETH = int(IMAGE[0]/100) # Max distance (in coord) a gesture forced to be idling
PREPROC = [3,1]
## Label id selection schemes
# No effect to the original pose data. Based on the index:
# 0: Weighted - Positive poses receive boosted confidence (lowering false "suspicious").
# 1: Grouped - Big gesture (DR, UR, DL, UL, ND) will be groups, averaged, max obtained.
# Labels in losing groups will be totally ignored (zero)
# After: Max confidence
LABSEL = [True,False]
# Label weight for weighted label scheme, multiplied to the base confidence
LABWEI = np.array([1,1,1,1, 0,0,0,0, 0,0,0,0, 0,0,0,0, 0]) * 0.2 + 1
# LABWEI = np.array([1,1,1,1, 0,0,0,0, 0,0,0,0, 0,0,0,0]) * 0.2 + 1
LABGRO = [ [0,4,8,12],
[1,5,9,13],
[2,6,10,14],
[3,7,11,15],
[16]]
LABELS = [
"jalan_DR", "jalan_UR", "jalan_DL", "jalan_UL",
"barang2_DR", "barang2_UR", "barang2_DL", "barang2_UL",
"barang1l_DR", "barang1l_UR", "barang1l_DL", "barang1l_UL",
"barang1r_DR", "barang1r_UR", "barang1r_DL", "barang1r_UL",
"diam_ND"
]
# LABELS = [
# "jalan_NE", "jalan_NW", "jalan_SE", "jalan_SW",
# "menyapu_NE", "menyapu_NW", "menyapu_SE", "menyapu_SW",
# "barang_NE", "barang_NW", "barang_SE", "barang_SW",
# "diam_NE", "diam_NW", "diam_SE", "diam_SW"
# ]
# LABELS = ["normal", "anomaly"]
## Security Parameters
N_HIST = 10
FRPARAM = 0.3 # Individual frame parameter, depending on the post processing used.
HISTH = 0.5 # Historical threshold for final trigger.
## Postprocessing schemes, historical level calculation
# Before: N_HIST frames collected, each having percentage of positive detections vs. all detections
# 0: Count threshold - Percentage of frames above PARAM threshold vs. all frames.
# 1: Average - Average all frames (no PARAM required)
# 2: Percentile - Calculate the PARAM percentile from all frames
# After: Check against historical threshold
POSTPROC = 2
# Alarms & indicators
ALDUR = 2 # Alarm duration in seconds (using the file duration if it's shorter)
ALAUTH = 4 # Authorized state duration, if there's any known face
ALSND = "utilities/alarm.wav" # Alarm sound directory
## Utilities
# Prevent face blinking, hold prev result if new result is empty
HFACE = 0
# Prescale & Pratical face_reg region
FPSCALE = 1 # The face image prescale divisor
FUP = 2 # Facerec model upsample
# Cropping ceiling cams for face recog region
# FREG = [0, 200, 250, 800] # Face region, for single SW camera [y1, y2, x1, x2], 1024x576 single image
# FREG = [288+0, 288+100, 512+125, 512+340] # Face region, for SW camera in 2x2 [y1, y2, x1, x2], 1024x576 four images
# FREG = [0, 576, 0, 1024]
# FREG = [350, 510, 400, 600]
FREG = [210, 360, 425, 590]
# Exit zone [y1, y2, x1, x2]
EX = [288,375,701,800]
EXR = 3 # Radius (square) from pose point to be used as color reference
EXTH = 0.2 # Threshold in distance fraction
# Masking areas to NOT be detected by openpose.
# Used to hide noisy area unpassable by human. (Masks are not shown during preview)
# The mask is a polygon, specify the vertices location.
DOMASK = 1
DRAWMASK = 0 # Preview the masking or keep it hidden
# PMASK = [ np.array([[610,520],[770,430],[960,576],[660,576]], np.int32), # SW
# np.array([[185,430],[255,470],[70,570],[0,575],[0,530]], np.int32), # SE
# np.array([[760,200],[880,288],[1024,134],[985,44]], np.int32), # NW
# np.array([[260,190],[50,50],[136,53],[327,157]], np.int32) # NE
# ]
# PMASK = [ np.array([[290,200],[0,0],[512,0],[350,180]], np.int32), # NE
# np.array([[650,200],[800,288],[1024,288],[1024,0],[985,44]], np.int32), # NW
# np.array([[185,430],[255,470],[70,570],[0,575],[0,300]], np.int32), # SE
# np.array([[610,520],[700,420],[770,380],[960,576],[660,576]], np.int32), # SW
# np.array([[950,400],[1024,400],[1024,500]], np.int32)] # SW
# PMASK = [ np.array([[290,200],[0,0],[512,0],[350,180]], np.int32), # NE
# np.array([[650,200],[800,288],[1024,288],[1024,0],[985,44]], np.int32), # NW
# np.array([[275,400],[190,400],[200,480],[270,460]], np.int32), # SE
# np.array([[185,430],[255,470],[70,570],[0,575],[0,300]], np.int32), # SE
# np.array([[900,576],[700,420],[640,400],[512,576]], np.int32), # SW
# np.array([[950,400],[1024,400],[1024,500]], np.int32)] # SW
# PMASK = [ np.array([[0,0],[1024,0],[1024,576],[0,576]], np.int32) ]
PMASK = [ np.array([[0,0],[1,0],[1,1]], np.int32) ]
# PMASK = [ np.array([[579,500],[580,575],[760,574],[756,473],[724,443]], np.int32),
# np.array([[384,339],[329,401],[154,343],[225,288],[386,287]], np.int32),
# np.array([[960,478],[905,573],[1023,574],[1024,466]], np.int32),
# np.array([[360,285],[393,229],[509,190],[511,365],[475,315]], np.int32),
# np.array([[635,338],[706,374],[514,449],[516,364]], np.int32)]
DUMMY = False
SKX = [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34]
SKY = [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35]
class mainhuman_activity:
# Pre-processing for every image
def preprocess(raws, rots):
imgs = []
for img, rot in zip(raws, rots):
img = cv2.resize(img, dsize=(SUBIM[0], SUBIM[1]), interpolation=cv2.INTER_CUBIC) # 16:9
# img = cv2.resize(img, dsize=(1024, 576), interpolation=cv2.INTER_CUBIC) # 16:9
# img = cv2.resize(img, dsize=(512, 288), interpolation=cv2.INTER_CUBIC) # 16:9
# img = cv2.resize(img, dsize=(256, 144), interpolation=cv2.INTER_CUBIC) # 16:9
# img = cv2.resize(img, dsize=(464, 288), interpolation=cv2.INTER_CUBIC) # 16:10
# img = cv2.resize(img, dsize=(640, 480), interpolation=cv2.INTER_CUBIC) # 4:3
# img = cv2.resize(img, dsize=(320, 240), interpolation=cv2.INTER_CUBIC) # 4:3
# img = cv2.resize(img, dsize=(160, 120), interpolation=cv2.INTER_CUBIC) # 4:3
img = imutils.rotate_bound(img, rot)
imgs.append(img)
if len(imgs) == 1:
image = imgs[0]
if len(imgs) >= 2: # Two images side-by-side
image = np.hstack((imgs[0], imgs[1]))
if len(imgs) >= 4: # Four images boxed
image2 = np.hstack((imgs[2], imgs[3]))
image = np.vstack((image, image2))
return imgs, image
def __init__(self, camera=CAMERA):
self.fps = 1
frame_time = 0
hisfps = [] # Historical FPS data
self.alprev = 0 # Prev alarm time
self.altrig = 0 # Alarm triggered, -1 authorized, 0 neutral, 1 triggered
freg = []
if len(camera) > 0:
from webcamvideostream import WebcamVideoStream
cams = [WebcamVideoStream(src=cam, resolution=(1280,720)).start() for cam in camera]
imgs = []
for i, cam in enumerate(cams):
# cam.set(cv2.CAP_PROP_BUFFERSIZE, 1) # Internal buffer will now store only x frames
img = cam.read()
# If no image is acquired
if (img is None):
# Black image
imgs.append(np.zeros((100,100,3), np.uint8))
elif (img.size == 0):
imgs.append(np.zeros((100,100,3), np.uint8))
else:
imgs.append(img)
# TEST, 4 camera simulation
# for i in range(3):
# imgs.append(img)
imgs, image = mainhuman_activity.preprocess(imgs, ROTATE)
# Face camera, not rendered on main image
if len(imgs) == 5:
im_h, im_w = imgs[4].shape[:2]
imf = imgs[4][round(im_h*FCAMCP[0]): round(im_h*FCAMCP[1]), round(im_w*FCAMCP[2]): round(im_w*FCAMCP[3])] # Crop
im_h, im_w = imf.shape[:2]
imf = cv2.resize(imf, dsize=(round(im_w/FCAMDS), round(im_h/FCAMDS)), interpolation=cv2.INTER_CUBIC) # Downsample
im_h, im_w = imf.shape[:2]
ky = 0 if im_h % 2 == 0 else 1
kx = 0 if im_w % 2 == 0 else 1
freg = [round(FCOFF[1]-im_h/2), round(FCOFF[1]+im_h/2)+ky, round(FCOFF[0]-im_w/2), round(FCOFF[0]+im_w/2)+kx]
else:
freg = FREG # Use cropped ceiling cams for face
else:
cams = []
print("No camera given, trying to use video instead.")
cap = cv2.VideoCapture(VIDEO, cv2.CAP_FFMPEG)
time.sleep(1)
if cap.isOpened() is False:
print("Error opening video stream or file")
return None
frame = 0
frame_skipped = 0
ret_val, image = cap.read()
freg = FREG # Use ceiling cams for face
self.im_h, self.im_w = image.shape[:2]
# print(h, w, c, h2, w2, c2)
###print("\n######################## Facerec")
if SYS_FACEREC:
facer = fr.face_recog(face_dir="./facerec/face/")
###print("\n######################## Darknet")
if SYS_DARK:
dark = dk.darknet_recog()
###print(dark.performDetect(image))
###print("\n######################## LSTM")
if SYS_ACT:
act = activity_human()
act.test()
###print("\n######################## Openpose")
if SYS_OPOSE:
opose = openpose_human(image)
# print("\n######################## Deepface")
# dface = df.face_recog()
# print(dface.run(image))
hold_face = 0
act_labs = []
act_confs = []
act_locs = []
sec_hist = []
sec_auths = {}
if DUMMY:
# Dummy pose
dimg = cv2.imread("images/TestPose.jpg")
doff_x = 0
doff_y = 30
rimg = cv2.imread("images/Background.png")
# For FPS calculation
ptime = time.time()
# Main loop
while True:
imgs = []
if len(camera) > 0:
for i, cam in enumerate(cams):
# Grab the frames AND do the heavy preprocessing for each camera
# ret_val, img = cam.read()
# For better synchronization on multi-cam setup:
# Grab the frames first without doing the heavy stuffs (decode, demosaic, etc)
# ret_val = cam.grab()
# The FIFO nature of the buffer means we can't get the latest frame
# Thus skip the earlier frames. Delay stats: 7s 8fps +artifact >>> 2s 3fps
# for i in range(5):
# ret_val = cam.grab()
# Multi-threading using WebcamVideoStream
img = cam.read()
###print(cam.grabbed)
# If no image is acquired
if (img is None):
# Black image
imgs.append(np.zeros((100,100,3), np.uint8))
elif (img.size == 0):
imgs.append(np.zeros((100,100,3), np.uint8))
else:
imgs.append(img)
# for i, cam in enumerate(cams):
# # Decode the captured frames
# ret_val, img = cam.retrieve()
# imgs.append(img)
# Skip frame if there's nothing
if (imgs is [None]):
continue
# # TEST, 4 camera simulation
# for i in range(3):
# imgs.append(img)
imgs, image = mainhuman_activity.preprocess(imgs, ROTATE)
# Face camera, not seen on main image
if len(imgs) == 5:
im_h, im_w = imgs[4].shape[:2]
imf = imgs[4][round(im_h*FCAMCP[0]): round(im_h*FCAMCP[1]), round(im_w*FCAMCP[2]): round(im_w*FCAMCP[3])] # Crop
im_h, im_w = imf.shape[:2]
imf = cv2.resize(imf, dsize=(round(im_w/FCAMDS), round(im_h/FCAMDS)), interpolation=cv2.INTER_CUBIC) # Downsample
else:
# Video mode
ret_val, image = cap.read()
# Skip frames to get realtime data representation
if frame_skipped < SKIP_FRAME:
frame += 1
frame_skipped += 1
continue
frame += 1
frame_skipped = 0
# Special smaller image for face recognition, reduces memory
if len(imgs) == 5:
imface = imf # Use face camera
else:
# Use cropped ceiling cams
imface = image[freg[0]:freg[1], freg[2]:freg[3]]
# Special masked image for openpose, reduce environment noise.
# Draw a polygon mask around unwanted area, for 4 cam mode
impose = image.copy()
if DOMASK:
for pmask in PMASK:
cv2.fillPoly(impose, [pmask], color=(200,200,288))
# cv2.fillPoly(impose, [pmask], color=(0,0,0))
# Dummy image
if DUMMY:
impose[0:IMAGE[1], 0:IMAGE[0]] = rimg
if (doff_x >= 0) and (doff_y >= 0) and (doff_x+dimg.shape[1] < IMAGE[0]) and (doff_y+dimg.shape[0] < IMAGE[1]):
impose[doff_y:doff_y+dimg.shape[0], doff_x:doff_x+dimg.shape[1]] = dimg
impose[doff_y+288:doff_y+dimg.shape[0]+288, 1024-(doff_x+dimg.shape[1]):1024-doff_x] = cv2.flip(dimg.copy(), 1)
else:
doff_x = 0
doff_y = 30
doff_x += int(round((1024-dimg.shape[1])/(3*4)))
# doff_y += int(round((576-dimg.shape[0])/(3*4)))
###print("\n######################## Openpose")
if SYS_OPOSE:
human_keypoints, human_ids, humans = opose.runopenpose(impose)
# print(humans, human_keypoints)
else:
human_keypoints = {0: [np.zeros(36)]}
human_ids = {0: 0}
humans = []
###print("\n######################## Darknet")
if SYS_DARK:
dobj = dark.performDetect(image)
###print(dobj)
else:
dobj = []
###print("\n######################## Facerec")
if SYS_FACEREC:
face_locs_tp, face_names_tp = facer.runinference(imface, tolerance=0.4, prescale=1/FPSCALE, upsample=FUP)
###print(face_locs_tp, face_names_tp)
else:
face_locs_tp = []
face_names_tp = []
# Prevent face blinking, apply the result if the new result is not empty.
if face_locs_tp or hold_face <= 0:
face_locs = face_locs_tp # Apply the results
face_names = face_names_tp
hold_face = HFACE # Reset counter
else:
hold_face -= 1
# print("\n######################## LSTM")
act_labs = []
act_confs = []
act_locs = []
if SYS_ACT:
for key, human_keypoint in human_keypoints.items():
###print(key, human_keypoint)
if(len(human_keypoint)==N_STEPS):
act.runinference(human_keypoint)
act_labs.append(act.action)
act_confs.append(act.conf)
loc = openpose_human.average([human_keypoint[N_STEPS-1]])
# loc here is produced with format [[x,y]], so must be passing [0]
act_locs.append(loc[0])
###print("\n######################## Maths")
sec_lv, sec_flv, sec_auths = self.sec_calc(sec_hist, image, act_labs, act_confs, human_keypoints, dobj, imface, face_names, face_locs, sec_auths)
###print(sec_lv)
self.alert(sec_lv, len(sec_auths))
###print("\n######################## Display")
# Main drawing procedure
if DRAWMASK:
# Draw openpose mask & face region
self.display_all(impose, imface, sec_lv, sec_auths, humans, human_ids, act_labs, act_confs, act_locs, dobj, face_locs, face_names, freg)
else:
self.display_all(image, imface, sec_lv, sec_auths, humans, human_ids, act_labs, act_confs, act_locs, dobj, face_locs, face_names, freg)
# Frame management stuffs, counted before frame limited
frame_time = time.time() - ptime
# FPS limiter
if FPSLIM > 0:
time.sleep(max(1./FPSLIM - (frame_time), 0))
# FPS display & log, counted after frame limited
self.fps = 1.0 / (time.time() - ptime)
hisfps.append(self.fps)
ptime = time.time()
if cv2.waitKey(1) == 27:
break
cv2.destroyAllWindows()
# Output FPS history
fh = open("fps.txt", "w")
for fps in hisfps:
fh.write("%.3f \n" % fps)
fh.close()
def alert(self, sec_lv, sec_nauth):
if self.altrig == 0: # From neutral
# Alert & indicator about level below threshold
if sec_lv < HISTH:
winsound.PlaySound(None, winsound.SND_ASYNC)
winsound.PlaySound(ALSND, winsound.SND_ASYNC | winsound.SND_ALIAS)
self.altrig = 1 # To alert
self.alprev = time.time()
elif self.altrig == 1: # From alert
if time.time() > self.alprev + ALDUR:
self.altrig = 0 # To neutral
winsound.PlaySound(None, winsound.SND_ASYNC)
elif self.altrig == -1: # From cooldown period
if time.time() > self.alprev + ALAUTH:
self.altrig = 0 # To neutral
elif self.altrig == -2: # From authorized
# If none authorized
if sec_nauth == 0:
self.altrig = -1 # To cooldown period
self.alprev = time.time()
# Check authorization, nullify any security result if there's any authorized personnel
if sec_nauth > 0:
winsound.PlaySound(None, winsound.SND_ASYNC)
self.altrig = -2
def sec_calc(self, hist, image, act_labs, act_confs, human_keypoints, dobj, imface, face_names, face_locs, sec_auths, exth=EXTH):
# Pass components used for security level calculations
# TODO: implement threshold, constants, etc as variables
sec = security.Frame(act_labs, act_confs, dobj, face_names)
sec.calc()
# Add to historical record
# Base calculations from N latest data
hist.append(sec)
if (len(hist) > N_HIST):
# Remove the last, only the view changed, no copy created
hist.pop(0)
all_hist = len(hist)
# Calculation
lvs = []
for s in hist:
lvs.append(s.level)
print("%.3f " % s.level, end="")
print("| | ", end ="")
lvs = np.array(lvs)
if all_hist >= N_HIST:
if POSTPROC == 0: # Count if
sec_lv = len(lvs[lvs >= FRPARAM])/N_HIST
elif POSTPROC == 1: # Average
sec_lv = sum(lvs)/N_HIST
elif POSTPROC == 2: # Percentile
sec_lv = np.percentile(lvs, FRPARAM*100)
else:
sec_lv = 1.0
# print("%d/%d %.2f | " % (all_neg, all_hist, sec_lv), end="")
print("%.2f | " % (sec_lv), end="")
# Print latest labels & confidence
for act, conf in zip(act_labs, act_confs):
print("%s[%.2f]," % (act, conf), end="")
print()
# Authorized exiting
# Only check if there's no new face
if len(sec_auths) > 0 and len(face_names) == 0:
##print(human_keypoints)
for id, keys in human_keypoints.items(): # loc = (x,y)
###print(keys[-1], len(keys))
# Get the last pose, only if the sequence is longer than 1 (has detected before)
if len(keys) > 1:
pose = keys[-1]
(x, y) = (int(pose[2]), int(pose[3]-5)) # pose[2],pose[3] = (x,y) of body center (chest)
if (EX[2] <= x <= EX[3]) and (EX[0] <= y <= EX[1]):
# Get surrounding colors, by radius EXR
###print(loc[1]-EXR, loc[1]+EXR, loc[0]-EXR, loc[0]+EXR)
color = np.mean(image[y-EXR:y+EXR, x-EXR:x+EXR], axis=(0,1))
frac = {}
# Check against every detected authorized
for auth in sec_auths:
(b1, g1, r1) = sec_auths[auth]
(b2, g2, r2) = color
dist = math.sqrt((b2-b1)**2+(g2-g1)**2+(r2-r1)**2)
frac[auth] = (dist/441.67) # frac = dist/sqrt(255^2*3)
# Get the one with smallest distance
minkey = min(frac, key=frac.get)
if frac[minkey] <= EXTH: # Check to threshold
sec_auths.pop(minkey)
# Authorization, just need one positive to trigger
sec_flv = 0
for name, (top, right, bottom, left) in zip(face_names, face_locs):
if name != "Unknown":
sec_flv += 1
# Get color from the bottom row of imface
color = np.mean(imface[-1,left:right].copy(), axis=0)
sec_auths[name] = color # Designate that color to the person
# Percentage
return sec_lv, sec_flv, sec_auths
# Authorization, just need one positive to trigger
sec_flv = 0
for name, (top, right, bottom, left) in zip(face_names, face_locs):
if name != "Unknown":
sec_flv += 1
# Get color from the bottom row of imface
color = np.mean(imface[-1,left:right].copy(), axis=0)
sec_auths[name] = color # Designate that color to the person
# Percentage
return sec_lv, sec_flv, sec_auths
def display_all(self, image, imface, sec_lv, sec_auths, humans, human_ids, act_labs, act_confs, act_locs, objs, face_locs, face_names, freg=[]):
# try:
# from skimage import io, draw
# import numpy as np
# print("*** "+str(len(detections))+" Results, color coded by confidence ***")
vt = 10 # Vertical positioning
# Face camera display
image[freg[0]:freg[1], freg[2]:freg[3]] = imface # Insert to the center
# Face region display
if freg != []:
cv2.rectangle(image, (freg[2], freg[0]), (freg[3], freg[1]), color=(64,64,64), thickness=1)
# Exit region display
cv2.rectangle(image, (EX[2], EX[0]), (EX[3], EX[1]), color=(64,64,64), thickness=1)
# Openpose display
image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False)
# Security level display
color = (0, int(255 * sec_lv), int(255 * (1 - sec_lv)))
cv2.rectangle(image, (10, vt), (self.im_w-10,vt+10), (255, 255, 255), thickness=1)
cv2.rectangle(image, (10, vt), (int(round((self.im_w-20)*sec_lv)+10), vt+10), color, cv2.FILLED)
cv2.rectangle(image, (int(round((self.im_w-20)*HISTH)+10-1), vt-5), (int(round((self.im_w-20)*HISTH)+10)+1,vt+10+5), (0, 0, 255), cv2.FILLED)
vt += 30
# Visual safety level indicator
if self.altrig == 1: # Alert
cv2.rectangle(image, (0, 0), (self.im_w, self.im_h), (0, 0, 255), thickness=8)
elif self.altrig <= -1: # Authorized or cooldown
cv2.rectangle(image, (0, 0), (self.im_w, self.im_h), (0, 255, 0), thickness=8)
# Authorized names inside
ht = 10 # For horizontal
nvt = IMAGE[1]-10 # vt from bottom
cv2.putText(image,
"Auth: %2d |" % len(sec_auths),
(ht, nvt), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 255, 0), 2)
ht += 85 # For horizontal
for name in sec_auths:
b, g, r = sec_auths[name]
cv2.rectangle(image, (ht-2, nvt-15), (ht+15-2, nvt+4), (b,g,r), thickness=-1)
cv2.rectangle(image, (ht-2, nvt-15), (ht+15-2, nvt+4), (255,255,255), thickness=1)
cv2.putText(image,
name[0], (ht, nvt),
cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(255-b, 255-g, 255-r), 1)
ht += 15
# Extra stats
cv2.putText(image,
"SECURITY: %.0f%%" % (sec_lv*100),
(10, vt), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 255, 0), 2)
vt += 20
cv2.putText(image,
"FPS: %.2f" % self.fps,
(10, vt), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 255, 0), 2)
vt += 20
# LSTM display
for (act_lab, act_conf, act_loc, id_val) in zip(act_labs, act_confs, act_locs, human_ids.values()):
###print(act_lab, act_conf, act_loc, id_val)
cv2.putText(image,
" %d: %s %.2f" % (id_val, act_lab, act_conf),
(int(round(act_loc[0])), int(round(act_loc[1]))), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 255, 0), 2)
# vt += 20
# Darknet display
for obj in objs:
###print(obj)
label = obj[0]
dconf = obj[1]
bounds = obj[2]
image, color = openpose_human.draw_box(image, 1, bounds, label, dconf)
# Facerec display
for (top, right, bottom, left), face in zip(face_locs, face_names):
###print(face)
label = face
# bounds = [4*left, 4*top, 4*(right-left), 4*(bottom-top)]
bounds = [freg[2]+FPSCALE*left, freg[0]+FPSCALE*top, FPSCALE*(right-left), FPSCALE*(bottom-top)]
image, color = openpose_human.draw_box(image, 0, bounds, label, loc=1)
cv2.imshow('Bedssys', image)
class openpose_human:
# def __init__(self, camera=0,resize='0x0',resize_out_ratio=4.0,model='mobilenet_thin',show_process=False):
def __init__(self, image, resize=OPSIZE, model='mobilenet_v2_small'):
self.logger = logging.getLogger('TfPoseEstimator-WebCam')
self.logger.setLevel(logging.DEBUG)
self.ch = logging.StreamHandler()
self.ch.setLevel(logging.DEBUG)
self.formatter = logging.Formatter('[%(asctime)s] [%(name)s] [%(levelname)s] %(message)s')
self.ch.setFormatter(self.formatter)
self.logger.addHandler(self.ch)
##self.logger.debug('initialization %s : %s' % (model, get_graph_path(model)))
self.w, self.h = model_wh(resize)
if self.w > 0 and self.h > 0:
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=OPGPU) # Allocate GPU fraction
self.e = TfPoseEstimator(get_graph_path(model), target_size=(self.w, self.h), tf_config=tf.ConfigProto(gpu_options=gpu_options))
else:
self.e = TfPoseEstimator(get_graph_path(model), target_size=(432, 368))
##self.logger.debug('cam read+')
# cam = cv2.VideoCapture(camera)
# ret_val, image = cam.read()
self.im_h, self.im_w = image.shape[:2]
# logger.info('cam image=%dx%d' % (image.shape[1], image.shape[0]))
self.videostep = 0
self.human_keypoint = {0: [np.zeros(36)]}
self.human_ids = {0: 0}
def runopenpose(self, image, resize_out_ratio=4.0):
# ret_val, image = cam.read()
##self.logger.debug('image process+')
humans = self.e.inference(image, resize_to_default=(self.w > 0 and self.h > 0), upsample_size=resize_out_ratio)
skeletoncount = 0
skels = np.array([np.zeros(36)])
for human in humans:
if skeletoncount == 0: # Initialize by adding N_STEPS of empty skeletons
skels = np.array([openpose_human.write_coco_json(human, self.im_w,self.im_h)])
else: # Append the rest
skels = np.vstack([skels, np.array(openpose_human.write_coco_json(human, self.im_w,self.im_h))])
skeletoncount = skeletoncount + 1
# if skeletoncount == 1: # Just assume it's the same prson if there's only one
# self.human_keypoint[0].append(skels)
if skeletoncount > 0:
self.human_keypoint, self.human_ids = openpose_human.push(self.human_keypoint, self.human_ids, skels)
else:
# No human actually detected (humans is empty, thus skcount = 0)
self.human_keypoint = {0: [np.zeros(36)]}
self.human_ids = {0: 0}
tf.reset_default_graph() # Reset the graph
# self.logger.debug('finished+')
return (self.human_keypoint, self.human_ids, humans)
# Basically, human_keypoint store a string of poses, length N_STEPS, and tracked.
# Humans is the result of a single inference, formatting still raw.
def draw_box(image, coord_type, bounds, text='', conf=1, loc=0, thickness=3):
# Based on the input detection coordinate
if coord_type == 0:
# Input (x, y) describes the top-left corner of detection
x = int(bounds[0])
y = int(bounds[1])
else: # Input (x, y) describes the center of detection
# Move it to the top-left corner
x = int(bounds[0] - bounds[2]/2)
y = int(bounds[1] - bounds[3]/2)
w = int(bounds[2])
h = int(bounds[3])
color = (int(255 * (1 - (conf ** 2))), int(255 * (conf ** 2)), 0)
# cv2.rectangle(img, pt1, pt2, color[, thickness[, lineType[, shift]]])
cv2.rectangle(image, (x, y), (x+w, y+h), color, thickness)
# Object text
if loc == 0:
cv2.putText(image, "%s %.2f" % (text, conf), (x, y-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
elif loc == 1:
cv2.putText(image, "%s %.2f" % (text, conf), (x, y+h+15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
return image, color
def write_coco_json(human, image_w, image_h):
keypoints = []
coco_ids = coco_ids = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]
for coco_id in coco_ids:
if coco_id not in human.body_parts.keys():
keypoints.extend([0, 0])
continue
body_part = human.body_parts[coco_id]
keypoints.extend([round(body_part.x * image_w, 3), round(body_part.y * image_h, 3)])
return keypoints
def push(traces, ids, new_skels, THRESHOLD = 100, TRACE_SIZE = N_STEPS):
###print("##### Multi-human")
"""Add the keypoints from a new frame into the buffer."""
# dists, neighbors = openpose_human.nearest_neighbors(traces, new_skels)
dists, neighbors = openpose_human.point(traces, new_skels)
keygen = []
# New skeletons which aren't close to a previously observed skeleton:
unslotted = []
# Previously observed skeletons which aren't close to a new one:
for each in traces.keys():
keygen.append(each)
unseen = set(keygen)
for skel, dist, neighbor in zip(new_skels, dists, neighbors):
###print(dist, neighbor)
if dist <= THRESHOLD:
if neighbor in traces:
traces[neighbor].append(skel)
else:
id = randint(0,100) # Only used for naming
traces[neighbor] = []
traces[neighbor].append(skel)
ids[neighbor] = id
if len(traces[neighbor]) > TRACE_SIZE:
traces[neighbor].pop(0)
unseen.discard(neighbor)
else:
unslotted.append(skel)
for i in unseen:
del traces[i]
del ids[i]
# Indices we didn't match, and the rest of the numbers are fair game
availible_slots = chain(sorted(unseen), count(len(traces)))
for slot, skel in zip(availible_slots, unslotted):
id = randint(0,100) # Only used for naming
if slot in traces:
traces[slot].append(skel)
else:
traces[slot] = []
traces[slot].append(skel)
ids[slot] = id
return traces, ids
def point(traces, skels, TRACE_IDX = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35]):
if not traces: # First pass
return np.zeros(len(skels)), np.arange(len(skels))
prev = np.array([ # Pull the most recent location of each skeleton, [-1] means get 1 data from behind
coords[-1][TRACE_IDX] for _, coords in sorted(traces.items())])
curr = skels[:, TRACE_IDX]
# Determine representative point, may use various method such as median, average, etc
prev_point = openpose_human.average(prev)
curr_point = openpose_human.average(curr)
# N is typically small (< 40) so brute force is fast
nn_model = NearestNeighbors(n_neighbors=1, algorithm='brute')
nn_model.fit(prev_point)
dist, nn = nn_model.kneighbors(curr_point, return_distance=True)
return dist.flatten(), nn.flatten()
def average(skels):
avg_skels = np.empty((0, 2))
for skel in skels:
# Remember that a point might not be detected, giving zero. Count the non-zero.
# Below line is equivalent to COUNTIF(not-zero).
# Count non-zeros
nzero_x = sum(1 if (x != 0) else 0 for x in skel[SKX])
nzero_y = sum(1 if (x != 0) else 0 for x in skel[SKY])
if (nzero_x == 0):
nzero_x = 1
if (nzero_y == 0):
nzero_y = 1
x = sum(skel[SKX]) / nzero_x
y = sum(skel[SKY]) / nzero_y
avg_skels = np.vstack((avg_skels, np.array([x, y])))
return avg_skels
def nearest_neighbors(traces, skels, TRACE_IDX = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35]):
if not traces: # First pass
return np.zeros(len(skels)), np.arange(len(skels))
prev = np.array([ # Pull the most recent location of each skeleton
coords[-1][TRACE_IDX] for _, coords in sorted(traces.items())])
curr = skels[:, TRACE_IDX]
# N is typically small (< 40) so brute force is fast
nn_model = NearestNeighbors(n_neighbors=1, algorithm='brute')
nn_model.fit(prev)
dist, nn = nn_model.kneighbors(curr, return_distance=True)
return dist.flatten(), nn.flatten()
class activity_human:
action = "null"
conf = 0
loc = []
# LABELS = [
# "JUMPING",
# "JUMPING_JACKS",
# # "BOXING",
# "WAVING_2HANDS",
# "WAVING_1HAND",
# "CLAPPING_HANDS"
# ]
def __init__(self):
self.LABELS = LABELS
self.n_input = 36
self.n_hidden = 36 # Hidden layer num of features
# n_classes = 6
n_classes = len(self.LABELS)
# N_STEPS = 32