def person_detect(camera, date, hour):
gate_model_path = 'C:\\Users\\sgudla\\Downloads\\models\\person_gate_model\\frozen_inference_graph.pb'
stairs_model_path = 'C:\\Users\\sgudla\\Downloads\\models\\person_stairs_model\\frozen_inference_graph.pb'
threshold = 0.9
if "GatePhotos":
odapi = DetectorAPI(path_to_ckpt=stairs_model_path)
else:
odapi = DetectorAPI(path_to_ckpt=gate_model_path)
from shared import *
from detector import DetectorAPI
from pytz import timezone
gate_model_path = '/home/pi/Downloads/person_gate_model/frozen_inference_graph.pb'
stairs_model_path = '/home/pi/Downloads/person_stairs_model/frozen_inference_graph.pb'
threshold = 0.8
for photo_root in photo_root_dirs:
if "StairsPhotos" in photo_root:
odapi = DetectorAPI(path_to_ckpt=stairs_model_path)
else:
odapi = DetectorAPI(path_to_ckpt=gate_model_path)
# Runs upto the specified date (not including the date)
india = timezone('Asia/Calcutta')
today = datetime.datetime.now(india)
yday = today-datetime.timedelta(days=1)
today_date = today.strftime("%Y-%m-%d")
yday_date = yday.strftime("%Y-%m-%d")
days = [today_date,yday_date]
dt = datetime.datetime.strptime("2019-05-14", "%Y-%m-%d").date()
for date_dir in days:
if not os.path.exists(os.path.join(photo_root,date_dir)):
continue
print("Running on :"+os.path.join(photo_root,date_dir))
for hr_dir in get_sub_dirs(os.path.join(photo_root,date_dir)):
parser = argparse.ArgumentParser()
parser.add_argument('input_file',
type=argparse.FileType('r'),
help="Image to be processed")
parser.add_argument('inference_graph_file',
type=argparse.FileType('r'),
help="Inference graph to be used")
args = parser.parse_args()
model_path = args.inference_graph_file.name
img_path = args.input_file.name
print("Model={}".format(model_path))
print("Image={}".format(img_path))
odapi = DetectorAPI(path_to_ckpt=model_path)
threshold = 0.6
img = cv2.imread(img_path)
img = cv2.resize(img, (640, 360))
start_time = time.time()
boxes, scores, classes, num = odapi.processFrame(img)
label = ""
for i in range(len(boxes)):
box = boxes[i]
# Class 1 represents human
if classes[i] == 1 and scores[i] > threshold:
label = label + "%d %d %d %d %s\n" % (box[0], box[1], box[2], box[3],
str(round(scores[i] * 100, 2)))
print("Person found in image '%s': %s" % (img_path, label))
import time
import os.path
from shared import *
from detector import DetectorAPI
if not check_hdd():
exit(0)
start_time = time.time()
now = datetime.datetime.now()
lasthour = now - datetime.timedelta(hours=1)
date = lasthour.strftime("%Y-%m-%d")
model_path = '/home/pi/person_detect_models/latest/frozen_inference_graph.pb'
odapi = DetectorAPI(path_to_ckpt=model_path)
threshold = 0.6
hour = '%02dhour' % (lasthour.hour)
total = 0
for photo_root in photo_root_dirs:
cur_dir = os.path.join(photo_root, date, hour)
if not os.path.exists(cur_dir):
log_message("Directory does not exists: " + cur_dir)
continue
remove_duplicates(cur_dir)
total = total + runPersonDetect(photo_root, date, hour, odapi, threshold)
backup_hour(cur_dir)
total_time = time.time() - start_time
from gui import Gui from detector import DetectorAPI #define path to model, currently its in the app folder modelPath = 'model3.pb' threshold = 0.3 detector = DetectorAPI(modelPath) gui = Gui(detector)