def four_pts(pix_pts, pix_real, real_pix, real_origin, img):
#convert to gps coords
real_pts = tform.transform_pt_array(pix_pts, pix_real)
final = []
#calculate locations six feet away at given bearings and add to array
for pt in real_pts:
degrees = calc_angle(pt, real_origin)
# print(degrees)
for angle in degrees:
location = six_ft(pt, angle)
final.append(location)
#convert list of pts to numpy array
final = np.array([final])
final = np.squeeze(np.asarray(final))
#check if final has any elements?
#convert to pixel coords
# pt = final[5]
# pt2 = final[7]
# dist = math.sqrt((pt2[0] - pt[0])**2 + (pt2[1] - pt[1])**2)
# print(dist)
final = tform.transform_pt_array(final, real_pix)
#show edge circles or not
# for pt in final:
# pt2 = (int(pt[0]), int(pt[1]))
# cv2.circle(img, pt2, 10, (0,0,255), -1, 8)
return final, img
def overhead(pts, origin, pix_real):
a = np.array([2592, 1944])
b = np.array([2592, 972])
c = np.array([2592, 0])
d = np.array([1296, 0])
e = np.array([0, 0])
f = np.array([0, 972])
g = np.array([0, 1944])
h = np.array([1296, 1944])
corners = np.array([a, b, c, d, e, f, g, h])
real_corners = tform.transform_pt_array(corners, pix_real)
#find locations of extremes
real_pts = tform.transform_pt_array(pts, pix_real)
#find most negative values in both directions
mins = np.ndarray.min(real_corners, axis=0)
mins[mins > 0] = 0
mins = np.absolute(mins)
#add to both directions until all values are positive
shifted = real_corners + mins
real_pts = real_pts + mins
#scale frame size
maxs = np.ndarray.max(shifted, axis=0)
frame_hgt = int(maxs[0])
frame_wdt = int(maxs[1])
#generate blank frame
img = np.zeros((frame_hgt, frame_wdt, 3), np.uint8)
#draw circles for all included points
i = 0
for pt in real_pts:
x = int(pt[0])
y = int(pt[1])
cv2.circle(img, (x, y), 6, (0, 255, 255), -1)
# if i<4:
# cv2.circle(img, (x, y), 6, (0, 0, 255), -1)
# elif i<8:
# cv2.circle(img, (x, y), 6, (0, 255, 255), -1)
# elif i<12:
# cv2.circle(img, (x, y), 6, (0, 255, 0), -1)
# elif i<16:
# cv2.circle(img, (x, y), 6, (255, 255, 0), -1)
# else:
# cv2.circle(img, (x, y), 6, (255, 0, 0), -1)
i = i + 1
origin = origin + mins
x = int(origin[0])
y = int(origin[1])
cv2.circle(img, (x, y), 6, (255, 255, 0), -1)
return img
def show_overhead(parallel, corners, pix_real, length, wth, roi_only):
pts = tform.transform_pt_array(parallel, pix_real)
#scale frame size
if roi_only:
frame_hgt = length
frame_wdt = wth
#calculate points at extremeties
else:
#find locations of extremes
extremes = tform.transform_pt_array(corners, pix_real)
#find most negative values in both directions
mins = np.ndarray.min(extremes, axis=0)
mins[mins > 0] = 0
mins = np.absolute(mins)
#add to both directions until all values are positive
shifted = extremes + mins
#scale frame size
maxs = np.ndarray.max(shifted, axis=0)
pts = pts + mins
frame_hgt = int(maxs[1])
frame_wdt = int(maxs[0])
#generate blank frame
img = np.zeros((frame_hgt, frame_wdt, 3), np.uint8)
#draw circles for all included points
if not roi_only:
for pt in shifted:
x = int(pt[0])
y = int(pt[1])
cv2.circle(img, (x, y), 5, (0, 0, 255), -1)
for pt in pts:
x = int(pt[0])
y = int(pt[1])
cv2.circle(img, (x, y), 5, (0, 255, 255), -1)
#display image
try:
cv2.namedWindow("Result", cv2.WINDOW_NORMAL)
cv2.imshow("Result", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
except:
print("Unexpected error:", sys.exc_info())
cv2.destroyAllWindows()
def proc_video(ind, i_lock, frames, times, bbox_q, cameras, gpu):
classes = utils.read_class_names("./config/coco.names")
worker = Worker(gpu)
while(True):
if worker.avail:
worker.mark_unavail()
# save current index so it doesn't change while processing
#lock ensures that multiple processes aren't working on same frame
try:
with i_lock:
i = ind.value
ind.value = ind.value + 1
ind.value = ind.value % len(frames)
except:
worker.mark_avail()
continue
#loop through frames, find people, record occupants and infractions
camera = cameras[i]
try:
worker.set_frame(np.asarray(frames[i]))
except ValueError:
worker.mark_avail()
torch.cuda.empty_cache()
continue
ped_bboxes,veh_bboxes,blur = worker.get_bboxes()
# denormalize
im = F.normalize(worker.gpu_frame[0],mean = [-0.485/0.229, -0.456/0.224, -0.406/0.225],
std = [1/0.229, 1/0.224, 1/0.225])
im = F.to_pil_image(im.cpu())
open_cv_image = np.array(im)
im = open_cv_image.copy()/255.0
#im = im[:,:,::-1]
# blur all peds regardless of confidence
for ped in blur:
im = utils.find_blur_face(ped.int(),im)
# parse metrics and write output frame
filename = camera["output"]
cam_name = camera["name"]
pix_real = camera["im-gps"]
frame_save = camera["save_frames"]
dt = times[i]
#find ft pts and convert to real_world
ped_pts = utils.get_ftpts(ped_bboxes)
realpts = tform.transform_pt_array(ped_pts, pix_real)
# also convert veh points to realworld
real_veh_pts = tform.transform_pt_array(utils.get_ftpts(veh_bboxes),pix_real)
# verifies there is more than one point in the list (each point has size 2)]
if realpts.size > 2:
mytree = scipy.spatial.cKDTree(realpts)
errors = utils.compliance_count(mytree, realpts)
#FIXME can probably do these both in 1 function
avg_dist = utils.find_dist(mytree, realpts)
avg_min_dist = utils.find_min_dist(mytree, realpts)
else:
errors = 0
avg_min_dist = None
avg_dist = None
occupants = len(ped_bboxes)
#save frames with occupants
if frame_save and (occupants > 12 or errors > 5):
result = prep_frame(ped_pts, im, camera, errors, occupants, ped_bboxes,veh_bboxes,classes)
dt_fixed = str(dt).replace(":","-").split(".")[0]
frame_name = "{}/{}.jpg".format(camera["frame_dir"],dt_fixed)
cv2.imwrite(frame_name,result*255)
#combine so bounding boxes remain associated with camera
output = [realpts,real_veh_pts,dt,errors,occupants,avg_dist,avg_min_dist,cam_name,i,worker.id]
bbox_q.put(output)
worker.count += 1
worker.mark_avail()
return
def test():
# define where video comes from
# video_path = './data/AOTsample3.mp4'
video_path = 'C:/Users/Nikki/Documents/work/inputs-outputs/video/vid_short.mp4'
video_path = 'C:/Users/Nikki/Documents/work/inputs-outputs/video/AOTsample1_1.mp4'
# pt1 = np.array([36.150434, -86.800694])
# pt2 = np.array([36.150748, -86.800867])
# pt3 = np.array([36.150617, -86.801132])
# print(GPS_to_ft(pt1, pt2))
# print(GPS_to_ft(pt2, pt3))
# get transfer function from known GPS and pixel locations
#AOT 1
pix_real = [[-1.10159024e-01, -2.79958285e-01, 2.15642254e+02],
[-1.13855523e-02, -1.39473016e+00, 8.15814671e+02],
[-2.11104956e-04, -3.64903525e-03, 1.00000000e+00]]
real_pix = [[1.05161691e+01, -3.36930756e+00, 4.81000000e+02],
[-1.06900637e+00, -4.29603818e-01, 5.81000000e+02],
[-1.68082662e-03, -2.27891683e-03, 1.00000000e+00]]
pix_real = [[-2.07813620e-01, -5.14012432e-01, 4.01979808e+02],
[-1.45283091e-16, -3.02228294e+00, 1.72572356e+03],
[4.24715690e-04, -7.70456596e-03, 1.00000000e+00]]
real_pix = [[1.63574796e+01, -4.11269628e+00, 5.22000000e+02],
[1.16697172e+00, -6.02703438e-01, 5.71000000e+02],
[2.04373330e-03, -2.89684039e-03, 1.00000000e+00]]
#vid_short
# pix_real = [[ 2.91445619e-01, 4.86714183e-01, -2.14894512e+02],
# [ 2.36746272e-03, 1.20740599e+00, -4.15252961e+00],
# [ 7.42232523e-04, 5.70630790e-03, 1.00000000e+00]]
# real_pix = [[ 3.51000287e+00, -4.88385701e+00, 7.34000000e+02],
# [-1.55374099e-02, 1.28569924e+00, 2.00000000e+00],
# [-2.51657708e-03, -3.71163815e-03, 1.00000000e+00]]
# load in sample pts
# a = np.array([36.148342, -86.799332]) #closest lamp
# b = np.array([36.148139, -86.799375]) #lamp across street, right
# c = np.array([36.148349, -86.799135]) #closest left corner of furthest crosswalk dash to right
# d = np.array([36.147740, -86.799218]) #sixth tree down the street
a = np.array([1296, 1944 / 6 * 5]) #far left street pole
b = np.array([1296, 1944 / 6 * 4]) #pole by bike sign
c = np.array([1296, 1944 / 6 * 3]) #corner of sidewalk
d = np.array([1296,
1944 / 6 * 2]) #right of sidewalk stripe closest to camera
e = np.array([1296 / 2, 1944 / 6 * 2])
f = np.array([1296 / 2, 1944 / 6 * 3])
g = np.array([1296 / 2, 1944 / 6 * 4])
h = np.array([1296 / 2, 1944 / 6 * 5])
i = np.array([1296 / 2 * 3, 1944 / 6 * 2])
j = np.array([1296 / 2 * 3, 1944 / 6 * 3])
k = np.array([1296 / 2 * 3, 1944 / 6 * 4])
l = np.array([1296 / 2 * 3, 1944 / 6 * 5])
m = np.array([1296 / 4 * 3, 1944 / 6 * 2])
n = np.array([1296 / 4 * 3, 1944 / 6 * 3])
o = np.array([1296 / 4 * 3, 1944 / 6 * 4])
p = np.array([1296 / 4 * 3, 1944 / 6 * 5])
orig = np.array([1296, 1944])
#orig = np.array([1296*2, 0])
# a = np.array([1280/2, 720/6*5]) #far left street pole
# b = np.array([1280/2, 720/6*4]) #pole by bike sign
# c = np.array([1280/2, 720/6*3]) #corner of sidewalk
# d = np.array([1280/2, 720/6*2]) #right of sidewalk stripe closest to camera
# e = np.array([1280/2/2, 720/6*2])
# f = np.array([1280/2/2, 720/6*3])
# g = np.array([1280/2/2, 720/6*4])
# h = np.array([1280/2/2, 720/6*5])
# i = np.array([1280/2/2*3, 720/6*2])
# j = np.array([1280/2/2*3, 720/6*3])
# k = np.array([1280/2/2*3, 720/6*4])
# l = np.array([1280/2/2*3, 720/6*5])
# m = np.array([1280/2/4*3, 720/6*2])
# n = np.array([1280/2/4*3, 720/6*3])
# o = np.array([1280/2/4*3, 720/6*4])
# p = np.array([1280/2/4*3, 720/6*5])
# orig = np.array([1280/2, 720])
#d = np.array([1296, 1944])
x = np.array([a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, orig])
pix_pts = x
# start video
print("Video from: ", video_path)
vid = cv2.VideoCapture(video_path)
wdt = int(vid.get(3))
hgt = int(vid.get(4))
print(wdt)
print(hgt)
origin = orig
print(origin)
real_origin = tform.transform_pt_array(origin, pix_real)
real_origin = np.squeeze(np.asarray(origin))
print(real_origin)
try:
while True:
# skip desired number of frames to speed up processing
for i in range(10):
vid.grab()
# read frame
return_value, frame = vid.read()
# if frame doesn't exist, exit
if not return_value:
cv2.destroyWindow('result')
print('Video has ended')
break
# draw ellipse
img, count = draw_radius(frame, pix_pts, real_pix, pix_real,
real_origin)
pt = (int(orig[0]), int(orig[1]))
cv2.circle(img, pt, 20, (0, 0, 255), -1, 8)
img2 = overhead(pix_pts, origin, pix_real)
cv2.namedWindow("result", cv2.WINDOW_NORMAL)
cv2.imshow("result", img)
cv2.namedWindow("Overhead", cv2.WINDOW_NORMAL)
cv2.imshow("Overhead", img2)
if cv2.waitKey(1000) & 0xFF == ord('q'): break
# end video, close viewer, stop writing to file
vid.release()
cv2.destroyAllWindows()
# if interrupted, end video, close viewer, stop writing to file
except:
print("Unexpected error:", sys.exc_info())
vid.release()
cv2.destroyAllWindows()
def draw_ellipse(frame, pts, centers, mytree, pix_real):
#define qualities of the ellipse
thickness = -1
line_type = 8
#set transparency
alpha = 0.25
#create separate image for ellipses to be drawn into
ellipses = frame.copy()
#iterate through list of ellipse points and centers, drawing each into ellipse image
i = 0
count = 0
ct = 0
gps_centers = tform.transform_pt_array(centers, pix_real)
while i < pts.shape[0]:
a = pts[i]
b = pts[i + 1]
c = pts[i + 2]
d = pts[i + 3]
# this has performed worse on all samples except maybe aot2
# possible_minor = int((math.sqrt(math.pow((c[0]-a[0]), 2) + math.pow((c[1]-a[1]), 2)))/2)
possible_minor = int(abs(c[1] - a[1]) / 2)
possible_major = int((math.sqrt(
math.pow((d[0] - b[0]), 2) + math.pow((d[1] - b[1]), 2))) / 2)
minor = min([possible_major, possible_minor])
major = max([possible_major, possible_minor])
if centers.size <= 2:
centers = np.array([centers])
center = centers[i // 4]
x = int(center[0])
y = int(center[1])
# TODO could probably query all points simultaneously, could be a little more efficient
if centers.size > 2:
gps_center = gps_centers[i // 4]
dist, ind = mytree.query(gps_center, k=2)
closest = mytree.data[ind[1]]
#dist = GPS_to_ft(gps_center, closest)
if dist[1] < 6:
cv2.ellipse(ellipses, (x, y), (major, minor), 0, 0, 360,
(255, 0, 0), thickness, line_type)
count = count + 1
elif dist[1] < 8:
cv2.ellipse(ellipses, (x, y), (major, minor), 0, 0, 360,
(255, 140, 0), thickness, line_type)
elif dist[1] < 10:
cv2.ellipse(ellipses, (x, y), (major, minor), 0, 0, 360,
(255, 255, 0), thickness, line_type)
else:
cv2.ellipse(ellipses, (x, y), (major, minor), 0, 0, 360,
(0, 255, 0), thickness, line_type)
# if ct<4:
# cv2.ellipse(ellipses, (x,y), (major, minor), 0, 0, 360, (0, 0, 255), thickness, line_type)
# elif ct<8:
# cv2.ellipse(ellipses, (x,y), (major, minor), 0, 0, 360, (0, 255, 255), thickness, line_type)
# elif ct<12:
# cv2.ellipse(ellipses, (x,y), (major, minor), 0, 0, 360, (0, 255, 0), thickness, line_type)
# elif ct<16:
# cv2.ellipse(ellipses, (x,y), (major, minor), 0, 0, 360, (255, 255, 0), thickness, line_type)
else:
cv2.ellipse(ellipses, (x, y), (major, minor), 0, 0, 360,
(0, 255, 0), thickness, line_type)
i = i + 4
ct = ct + 1
#combine original image and ellipse image into one
all_img = cv2.addWeighted(ellipses, alpha, frame, 1 - alpha, 0)
return all_img, count
def load_tree(pix_pts, pix_real):
real = tform.transform_pt_array(pix_pts, pix_real)
mytree = scipy.spatial.cKDTree(real)
return mytree
def post_processor(bbox_q, cameras, out_q, frames, times, image_q=None):
classes = utils.read_class_names("./config/coco.names")
try:
while True:
if not bbox_q.empty():
box_ind = bbox_q.get()
ped_bboxes = box_ind[0]
veh_bboxes = box_ind[1]
i = box_ind[2]
frame = box_ind[3]
# first, try and show frame
#frame = frame.transpose(1, 2, 0)
# cv2.imshow("test",frame)
# cv2.waitKey(0)
camera = cameras[i]
filename = camera["address"]
pix_real = camera["im-gps"]
frame_show = camera["save_frames"]
dt = times[i]
#find ft pts and convert to real_world
ped_pts = utils.get_ftpts(ped_bboxes)
realpts = tform.transform_pt_array(ped_pts, pix_real)
# verifies there is more than one point in the list (each point has size 2)]
if realpts.size > 2:
mytree = scipy.spatial.cKDTree(realpts)
errors = utils.compliance_count(mytree, realpts)
#FIXME can probably do these both in 1 function
avg_dist = utils.find_dist(mytree, realpts)
avg_min_dist = utils.find_min_dist(mytree, realpts)
else:
errors = 0
avg_min_dist = None
avg_dist = None
occupants = len(ped_bboxes)
#output info to csv file
with open(filename, 'a', newline='') as base_f:
writer = csv.writer(base_f)
utils.video_write_info(writer, realpts, str(dt), errors,
occupants, avg_dist, avg_min_dist)
stats = [i, errors, occupants, avg_min_dist]
#put outpt data into queue so it is accessible by the analyzer
if out_q.full():
out_q.get()
out_q.put(stats)
result = prep_frame(ped_pts, frame, camera, errors, occupants,
ped_bboxes, veh_bboxes, classes)
cv2.imshow("frame", result)
cv2.waitKey(0)
# if frame_save or frame_show:
# result = prep_frame(ftpts, frame, vid, errors, occupants, bboxes)
### UNCOMMENT AND USE
# frame_save = True
# #save frames
# if frame_save:
# outpt_frame(result, vid)
# if frame_show:
# if image_q.full():
# image_q.get()
# image_q.put(result)
# # FIXME - just for debugging, show frame on screen
show_frame(result, i)
if cv2.waitKey(1) & 0xFF == ord('q'): break
except KeyboardInterrupt:
print("Unexpected postprocessing error:", sys.exc_info()[0])
cv2.destroyAllWindows()
return
def main(errs, ocpts, dists, updated, frames, times, avgs, avg_lock, i_lock,
ind, out_q, bbox_q, image_q, config, ctx):
# file containing camera information
# transform_f = 'C:/Users/Nikki/Documents/work/inputs-outputs/transforms.csv'
# transform_f = 'C:/Users/Nikki/Documents/work/inputs-outputs/test.csv'
# transform_f = 'C:/Users/Nikki/Documents/work/inputs-outputs/test_all.csv'
# transform_f = './config/LAMBDA_TEST.config'
#create VidObjs to store information about each camera
cameras = initialize_cams(config)
num_cams = len(cameras)
#length of queues, kinda arbitrary - this is the number that will be used for moving avg analytics
buf_num = 3
#need to fix these references
# errs = var_list[0]
# ocpts = var_list[1]
# dists = var_list[2]
# updated = var_list[3]
# frames = var_list[4]
# times = var_list[5]
# avgs = var_list[6]
# avg_lock = var_list[7]
# i_lock = var_list[8]
# ind = var_list[9]
# bbox_q = var_list[10]
# ind = var_list[11]
#uncomment if running from this file
# manager = mp.Manager()
# print('MP manager created')
# # create manager to handle shared variables across processes
# updated = manager.Value(c_bool, False)
# frames = manager.list([None]* num_cams)
# times = manager.list([None]* num_cams)
# avgs = manager.list([None] * 5)
# avg_lock = manager.Lock()
# i_lock = manager.Lock()
# out_q = manager.Queue(num_cams*2)
# bbox_q = manager.Queue()
# ind = manager.Value(int, 0)
# image_q = manager.Queue(num_cams*2)
# # sample = manager.list([None] * 5)
# errs = manager.list()
# ocpts = manager.list()
# dists = manager.list()
# s_lock = manager.Lock()
# # for _ in range(num_cams):
# # ocpts.append( [None]* buf_num)
# for i in range(num_cams):
# errs.append(manager.list([None]))
# ocpts.append(manager.list([None]))
# dists.append(manager.list([None]))
classes = utils.read_class_names("./config/coco.names")
#stores frame data that has been transfered to GPU
GPU_LIST = [i for i in range(torch.cuda.device_count())]
# workers = setup_gpus(vids, gpu_list = GPU_LIST)
#start model
# model = detector.start_model()
streamers = []
worker = Worker(2)
frame = cv2.imread("/home/worklab/Desktop/test1.png")
worker.set_frame(frame)
ped, veh = worker.get_bboxes()
cameras[0]["frame_size"] = (worker.gpu_frame[0].shape[:2])
im = F.normalize(worker.gpu_frame[0],
mean=[-0.485 / 0.229, -0.456 / 0.224, -0.406 / 0.225],
std=[1 / 0.229, 1 / 0.224, 1 / 0.225])
im = F.to_pil_image(im.cpu())
open_cv_image = np.array(im)
im = open_cv_image.copy() / 255.0
im = im[:, :, ::-1]
#combine so bounding boxes remain associated with camera
i = 0
box_ind = (ped, veh, i, im)
ped_bboxes = box_ind[0]
veh_bboxes = box_ind[1]
i = box_ind[2]
frame = box_ind[3]
# first, try and show frame
#frame = frame.transpose(1, 2, 0)
# cv2.imshow("test",frame)
# cv2.waitKey(0)
camera = cameras[i]
filename = camera["address"]
pix_real = camera["im-gps"]
frame_show = camera["save_frames"]
dt = times[i]
#find ft pts and convert to real_world
ped_pts = utils.get_ftpts(ped_bboxes)
realpts = tform.transform_pt_array(ped_pts, pix_real)
# verifies there is more than one point in the list (each point has size 2)]
if realpts.size > 2:
mytree = scipy.spatial.cKDTree(realpts)
errors = utils.compliance_count(mytree, realpts)
#FIXME can probably do these both in 1 function
avg_dist = utils.find_dist(mytree, realpts)
avg_min_dist = utils.find_min_dist(mytree, realpts)
else:
errors = 0
avg_min_dist = None
avg_dist = None
occupants = len(ped_bboxes)
#output info to csv file
with open(filename, 'a', newline='') as base_f:
writer = csv.writer(base_f)
utils.video_write_info(writer, realpts, str(dt), errors, occupants,
avg_dist, avg_min_dist)
stats = [i, errors, occupants, avg_min_dist]
#put outpt data into queue so it is accessible by the analyzer
if out_q.full():
out_q.get()
out_q.put(stats)
result = prep_frame(ped_pts, frame, camera, errors, occupants, ped_bboxes,
veh_bboxes, classes)
cv2.imshow("frame", result)
cv2.waitKey(0)
def post_processor(bbox_q, cameras, out_q, frames, times, image_q = None):
classes = utils.read_class_names("./config/coco.names")
start_time = time.time()
start_time = time.strftime('%Y-%m-%d %H-%M-%S', time.localtime(start_time))
f_directory = cameras[0]["output"].split("/")[:-1]
f_directory.append("{}_output_frames".format(start_time))
f_directory = "/".join(f_directory)
os.mkdir(f_directory)
frames_processed = np.zeros(len(cameras))
try:
while True:
if not bbox_q.empty():
box_ind = bbox_q.get()
ped_bboxes = box_ind[0]
veh_bboxes = box_ind[1]
i = int(box_ind[2])
frame = box_ind[3]
# first, try and show frame
#frame = frame.transpose(1, 2, 0)
# cv2.imshow("test",frame)
# cv2.waitKey(0)
camera = cameras[i]
filename = camera["output"].format(start_time)
cam_name = camera["name"]
pix_real = camera["im-gps"]
frame_save = camera["save_frames"]
dt = times[i]
#find ft pts and convert to real_world
ped_pts = utils.get_ftpts(ped_bboxes)
realpts = tform.transform_pt_array(ped_pts, pix_real)
# verifies there is more than one point in the list (each point has size 2)]
if realpts.size > 2:
mytree = scipy.spatial.cKDTree(realpts)
errors = utils.compliance_count(mytree, realpts)
#FIXME can probably do these both in 1 function
avg_dist = utils.find_dist(mytree, realpts)
avg_min_dist = utils.find_min_dist(mytree, realpts)
else:
errors = 0
avg_min_dist = None
avg_dist = None
occupants = len(ped_bboxes)
#output info to csv file
with open(filename, 'a', newline='') as base_f:
writer = csv.writer(base_f)
utils.video_write_info(writer, realpts, str(dt), errors, occupants, avg_dist, avg_min_dist,cam_name)
stats = [i, errors, occupants, avg_min_dist]
#put outpt data into queue so it is accessible by the analyzer
if out_q.full():
temp = out_q.get()
out_q.put(stats)
if frame_save:
result = prep_frame(ped_pts, frame, camera, errors, occupants, ped_bboxes,veh_bboxes,classes)
#save frames
if frame_save:
frame_name = "{}/{}_{}_processed.jpg".format(f_directory,cam_name,int(frames_processed[i]))
cv2.imwrite(frame_name,result*255)
# if frame_show:
# if image_q.full():
# image_q.get()
# image_q.put(result)
# # FIXME - just for debugging, show frame on screen
# show_frame(result, i)
# if cv2.waitKey(1) & 0xFF == ord('q'): break
frames_processed[i] += 1
except:
print("Unexpected postprocessing error:", sys.exc_info()[0])
cv2.destroyAllWindows()
return
def main():
#length of queues, kinda arbitrary - this is the number that will be used for moving avg analytics
buf_num = 3
#list of workers
#uncomment to verify that GPU is being used
tf.debugging.set_log_device_placement(False)
importlib.reload(mp)
ips = []
vids = []
# file containing camera information
transform_f = 'C:/Users/Nikki/Documents/work/inputs-outputs/transforms.csv'
transform_f = 'C:/Users/Nikki/Documents/work/inputs-outputs/test.csv'
transform_f = 'C:/Users/Nikki/Documents/work/inputs-outputs/test_all.csv'
transform_f = 'C:/Users/Nikki/Documents/work/inputs-outputs/aot_transforms_better.csv'
manager = mp.Manager()
#create VidObjs to store information about each camera
initialize_cams(transform_f, ips, vids)
num_cams = len(vids)
# create manager to handle shared variables across processes
updated = manager.Value(c_bool, False)
frames = manager.list([None] * num_cams)
times = manager.list([None] * num_cams)
avgs = manager.list([None] * 5)
errors = manager.list()
occupants = manager.list()
avg_dists = manager.list()
e_lock = manager.Lock()
o_lock = manager.list()
d_lock = manager.list()
avg_lock = manager.Lock()
out_q = manager.Queue(num_cams * 2)
#stores frame data that has been transfered to GPU
gpu_frames = [None] * num_cams
#start model
model = detector.start_model()
try:
#grab video frames in separate process
streamer = mp.Process(target=ip_streamer.stream_all,
args=(frames, times, ips, updated))
streamer.start()
print('Separate process started')
# analysis = mp.Process(target=adat.main, args=(all_output_stats, buf_num, avgs, removed))
analysis = mp.Process(target=adat.main,
args=(out_q, buf_num, num_cams, avgs, avg_lock,
errors, occupants, avg_dists, e_lock,
o_lock, d_lock))
analysis.start()
print('Separate process started')
#wait until frames are starting to be read
while (not updated.value):
continue
#find and assign the frame size of each stream
#TODO may have to resize frames or something for running through the model in batches
for i, vid in enumerate(vids):
vid.set_size(frames[i].shape[:2])
prev_time = time.time()
#continuously loop until keyboard interrupt
while (True):
curr_time = time.time()
# save outputs every 5 minutes
if (curr_time - prev_time) > (5 * 60):
save_files(vids)
prev_time = curr_time
# verify that frame is not the same as it was last time it was displayed
if updated.value == True:
# detector.batch_bboxes(model, frames)
# detector.batch_bboxes(model, frames)
#loop through frames and move to GPU (TODO - enable batching)
for i, frame in enumerate(frames):
gpu_frames[i] = detector.frame_to_gpu(frame)
#loop through frames, find people, record occupants and infractions
for i, vid in enumerate(vids):
frame = frames[i]
gpu_frame = gpu_frames[i]
dt = times[i]
bboxes = detector.person_bboxes(model, gpu_frame,
vid.frame_size)
#find ft pts and convert to real_world
ftpts = utils.get_ftpts(bboxes)
realpts = tform.transform_pt_array(ftpts, vid.pix_real)
# verifies there is more than one p[oint in the list (each point has size 2)]
if realpts.size > 2:
mytree = scipy.spatial.cKDTree(realpts)
errs = detector.compliance_count(mytree, realpts)
avg_dist = detector.find_dist(mytree, realpts)
else:
errs = 0
avg_dist = None
ocpts = ftpts.size // 2
#output info to csv file
utils.video_write_info(vid.csvfile, realpts, str(dt), errs,
ocpts, avg_dist)
stats = [i, errs, ocpts, avg_dist]
#put outpt data into queue so it is accessible by the analyzer
if out_q.full():
a = out_q.get()
print("Leaving queue: ", a)
out_q.put(stats)
# if len(avg_dists) > 0:
# print (adat.get_o_avg(occupants, i))
# print (adat.get_e_avg(errors, i))
# print (adat.get_dist_avg(avg_dists, i))
# print()
avg_lock.acquire()
print("Avgs: ", avgs)
avg_lock.release()
#save frames
if vid.frame_save:
outpt_frame(ftpts, frame, vid, errs, ocpts, bboxes)
# FIXME - just for debugging, show frame on screen
show_frame(ftpts, frame, vid, errs, ocpts, bboxes, i)
if cv2.waitKey(1) & 0xFF == ord('q'): break
except:
print("Unexpected error:", sys.exc_info())
cv2.destroyAllWindows()
for vid in vids:
vid.base_f.close()
streamer.terminate()
analysis.terminate()
def post_processor(bbox_q, vids, out_q, frames, times, image_q=None):
try:
while True:
if not bbox_q.empty():
box_ind = bbox_q.get()
bboxes = box_ind[0]
i = box_ind[1]
frame = box_ind[2]
vid = vids[i]
filename = vid[0]
frame_save = vid[1]
pix_real = vid[2]
dt = times[i]
#find ft pts and convert to real_world
ftpts = utils.get_ftpts(bboxes)
realpts = tform.transform_pt_array(ftpts, pix_real)
# verifies there is more than one p[oint in the list (each point has size 2)]
if realpts.size > 2:
mytree = scipy.spatial.cKDTree(realpts)
errors = detector.compliance_count(mytree, realpts)
#FIXME can probably do these both in 1 function
avg_dist = detector.find_dist(mytree, realpts)
avg_min_dist = detector.find_min_dist(mytree, realpts)
else:
errors = 0
avg_min_dist = None
avg_dist = None
occupants = ftpts.size // 2
#output info to csv file
with open(filename, 'a', newline='') as base_f:
writer = csv.writer(base_f)
utils.video_write_info(writer, realpts, str(dt), errors,
occupants, avg_dist, avg_min_dist)
stats = [i, errors, occupants, avg_min_dist]
#put outpt data into queue so it is accessible by the analyzer
if out_q.full():
out_q.get()
out_q.put(stats)
#FIXME this should be set somewhere else
frame_show = True
result = prep_frame(ftpts, frame, vid, errors, occupants,
bboxes)
# if frame_save or frame_show:
# result = prep_frame(ftpts, frame, vid, errors, occupants, bboxes)
#save frames
if frame_save:
outpt_frame(result, vid)
if frame_show:
if image_q.full():
image_q.get()
image_q.put(result)
# # FIXME - just for debugging, show frame on screen
# show_frame(result, i)
# if cv2.waitKey(1) & 0xFF == ord('q'): break
except:
print("Unexpected error:", sys.exc_info()[0])
cv2.destroyAllWindows()
return