def process_motion_frame(q, f, tick, ts, mfa=False, rotateAng=False, width=False, gBlur=(9, 9)):
'''
This function defines the image processing techniques that are applied
to a new thread when a frame is retreived from the camera.
'''
rects_sal = []
fgmask = None
f_copy = f.copy()
if rotateAng is not False and rotateAng != 0:
f = imutils.rotate(f, angle=rotateAng)
if width is not False:
f = imutils.resize(f, width=width)
# blur & bg sub
try:
fgmask = fgbg.apply(cv2.GaussianBlur(f, gBlur, 0), learningRate=config.computing.learning_rate)
except:
print("-"*60)
traceback.print_exc(file=sys.stdout)
print("-"*60)
raise
# get our frame outlines
f_rects, rects_mot = get_motions(f, fgmask, thickness=1)
rects_sal.extend(rects_mot)
num_motion = len(rects_mot)
if True:
# don't do anything else if there's no motion of any kind detected
# if num_motion > 0 or mfa is True:
num_bodies = 0
num_faces = 0
if config.computing.body_detection_en or config.computing.face_detection_en:
# generate a histogram equalized bw image if we're doing processing
# that needs it
f_bw = cv2.equalizeHist(cv2.cvtColor(f, cv2.COLOR_BGR2GRAY))
if config.computing.body_detection_en:
fBody, rectsBody = detectPerson(f, color=(255, 0, 0))
if len(rectsBody) > 0:
f_rects = cv2.add(f_rects, fBody)
num_bodies = len(rectsBody)
rects_sal.extend(rectsBody)
if config.computing.face_detection_en:
fFace, rectsFace = detectFace(f_bw, color=(0, 255, 0))
if len(rectsFace) > 0:
f_rects = cv2.add(f_rects, fFace)
num_faces = len(rectsFace)
rects_sal.extend(rectsFace)
f_rects = imutils.resize(f_rects, width=f_copy.shape[1])
q.put({"f": f_copy, "ts": ts, "rects_sal": rects_sal, "sz_scaled": getsize(
f), "num_motion": num_motion, "num_bodies": num_bodies, "num_faces": num_faces})
return f_copy, f_rects, fgmask, rects_sal, tick, ts
def process_response(q):
'''
This function defines the processes of the "Response Thread" that is
fully independent from the main thread. This one handles extracting
regions from the original (higher-res) frame and uploading the cropped
out portion locally or to the cloud.
'''
import boto3
s3 = boto3.resource("s3")
last_up = clock()
# initialize our largest detected area to 0
biggest_crop_area = 0
signal.signal(signal.SIGINT, signal.SIG_IGN)
while cap.isOpened():
# receive the data
data = q.get()
f = data["f"]
rects_sal = data["rects_sal"]
sz_scaled = data["sz_scaled"]
ts = data["ts"]
sz = getsize(f)
rr = (float(sz[0]) / sz_scaled[0], float(sz[1]) / sz_scaled[1])
# rescale the rectangular dimensions to our original resolution
bx = []
for x, y, w, h in rects_sal:
tmp = (x * rr[0], y * rr[1], w * rr[0], h * rr[1])
bx.append(tuple(int(x) for x in tmp))
if config.storage.save_cloud_crops or config.storage.save_local_crops:
if len(bx) > 0:
xx = tuple(
(min([min(x[0], x[0] + x[2]) for x in bx]), max([max(x[0], x[0] + x[2]) for x in bx])))
yy = tuple(
(min([min(x[1], x[1] + x[3]) for x in bx]), max([max(x[1], x[1] + x[3]) for x in bx])))
# only continue if the area of is not zero
if abs(yy[0] - yy[1]) > 0 and abs(xx[0] - xx[1]) > 0:
f_mask = f[min(yy):max(yy), min(xx):max(xx)]
cropped_area = (max(xx) - min(xx)) * (max(yy) - min(yy))
if (clock() - last_up) > config.storage.min_upload_delay:
biggest_crop_area = 0
# Always send the frames that contain detected people/faces.
# If detecting people/faces is disabled in the config, it
# is not affected.
if (cropped_area > biggest_crop_area) or data["num_bodies"] > 0 or data["num_faces"] > 0:
biggest_crop_area = cropped_area
root_path = join(
join(sDir, "cropped-regions"), date_pretty())
fn = root_path + "_regions.jpg"
res, img = cv2.imencode(
".jpg", f_mask, [int(cv2.IMWRITE_JPEG_QUALITY), config.storage.encoding_quality])
if res and config.storage.save_local_crops:
print("saving frame locally: {}".format(root_path))
# todo: save locally
if res and config.storage.save_cloud_crops:
last_up = clock()
img = img.tostring()
print(
"uploading frame to s3: {}".format(basename(fn)))
print(
"-- time since last upload: {}s".format(clock() - last_up))
s3.Object("cloudguard-in",
basename(fn)).put(Body=img,
Metadata={"Content-Type": "Image/jpeg",
"Number-Detected-Motion": str(data["num_motion"]),
"Number-Detected-Bodies": str(data["num_bodies"]),
"Number-Detected-Faces": str(data["num_faces"]),
"Captured-Timestamp": str(ts),
"Captured-Timestamp-Timezone": "UTC"})
