def main(args):
det = Detector(model_path=args.model_path,
input_size=args.input_size,
num_classes=args.num_classes,
threshold=args.threshold)
if args.inputs.endswith('.mp4'):
cap = cv2.VideoCapture(args.inputs)
while True:
ret, img = cap.read()
if not ret: break
results = det.detect(img)
draw(img, results)
cv2.imshow('', img)
cv2.waitKey(1)
elif os.path.isdir(args.inputs):
paths = glob.glob(os.path.join(args.inputs, '*'))
for path in paths:
img = cv2.imread(path)
results = det.detect(img)
draw(img, results)
cv2.imshow('', img)
cv2.waitKey(0)
else:
img = cv2.imread(args.inputs)
results = det.detect(img)
draw(img, results)
cv2.imshow('', img)
cv2.waitKey(0)
def main(args):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
image_path = args.image_path
is_local_weights = args.is_local_weights
weights_base_path = args.weights_base_path
show_face = args.show_face
align_torch = args.align_torch
arch = args.arch
# detect faces from image
image = cv2.imread(image_path)
det = Detector()
boxes, landmarks = det.detect(image)
print(f"Found faces: {boxes.shape[0]}")
if boxes.shape[0] < 1:
print("Faces not found")
sys.exit(0)
if align_torch:
faces_aligned, _ = align_face_torch_batch(image, landmarks, boxes,
device)
else:
faces_aligned, _ = align_face_np(image, landmarks, boxes)
if show_face:
# show detected face
idx = 0
x_tl, y_tl, x_br, y_br = boxes[idx, 0], boxes[idx,
1], boxes[idx,
2], boxes[idx,
3]
face = image[y_tl:y_br, x_tl:x_br, :]
cv2.imshow("Detected face", face)
if align_torch:
face_aln = faces_aligned.cpu().numpy()[
idx, :, :, :].squeeze().copy()
print(face_aln.min())
print(face_aln.max())
face_aln = (face_aln * 255).astype(np.uint8)
face_aln = face_aln.transpose(1, 2, 0)
else:
face_aln = faces_aligned[idx].squeeze().copy().astype(np.uint8)
cv2.imshow("Aligned face", face_aln)
cv2.waitKey(0)
# create embedder and get features
embedder = Embedder(is_local_weights, arch, weights_base_path)
features = embedder.get_features(faces_aligned)
# print(features[idx, :])
print("Features calculation finished. ")
print(f"Features shape: {features.shape}")
def main():
for fileName in os.listdir(DEMO_FOLDER):
os.unlink(os.path.join(DEMO_FOLDER, fileName))
# for folder in ['tmp/hi_koov_archive/_background_noise_', 'tmp/hi_koov_archive/unknown', 'tmp/hi_koov_archive/hi_koov', 'tmp/hi_koov_demo/hi_koov']:
# for the_file in os.listdir(folder):
# file_path = os.path.join(folder, the_file)
# try:
# if os.path.isfile(file_path):
# os.unlink(file_path)
# # elif os.path.isdir(file_path): shutil.rmtree(file_path)
# except Exception as e:
# print(e)
d = Detector()
# i = 1
while True:
# filePath = os.path.join(
# 'tmp/hi_koov_demo/hi_koov/', 'voice-{}.wav'.format(i))
# record_test_voice(filePath)
record_test_voice(AUDIO_SAVE_PATH)
t1 = time.time()
result = d.evaluate()
t2 = time.time()
print('=======================================================')
if result == 2:
print('Hi KOOV!\t- Inference time: {}s'.format(round(t2 - t1, 2)))
else:
print('Unknown\t- Inference time: {}s'.format(round(t2 - t1, 2)))
# if result == 2:
# print('Hi KOOV!')
# shutil.move(filePath, os.path.join('tmp/hi_koov_archive/hi_koov'))
# elif result == 1:
# print('Unknown')
# shutil.move(filePath, os.path.join('tmp/hi_koov_archive/unknown'))
# else:
# print('Background')
# shutil.move(filePath, os.path.join(
# 'tmp/hi_koov_archive/_background_noise_'))
print('=======================================================')
# i += 1
time.sleep(2)
def __init__(self, cam_id):
self.id = cam_id
self.log = "[{}] ".format(self.id)
self.lock = Lock()
self.detector = Detector()
self.tracker = Tracker(skipped_th=5)
self.warper = Warper()
self.suspicious_regions = None
logger.debug(self.log + "New SpotMonitor instance created")
def on_mouse_click(event, x, y, flags, frame):
if event == cv2.EVENT_LBUTTONUP:
global color, det
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
print(hsv[y - 2:y + 2, x - 2:x + 2, 0])
# find average of 5 by 5 square around click
h = np.median(hsv[y - 2:y + 2, x - 2:x + 2, 0])
s = np.median(hsv[y - 2:y + 2, x - 2:x + 2, 1])
v = np.median(hsv[y - 2:y + 2, x - 2:x + 2, 2])
print(h, s, v)
color = (h, s, v)
det = Detector("color", color)
class SpotMonitor:
def __init__(self, cam_id):
self.id = cam_id
self.log = "[{}] ".format(self.id)
self.lock = Lock()
self.detector = Detector()
self.tracker = Tracker(skipped_th=5)
self.warper = Warper()
self.suspicious_regions = None
logger.debug(self.log + "New SpotMonitor instance created")
def process(self, im):
with self.lock:
filtered_centers, cntrs = self.detector.detect(im=im)
logger.debug(self.log + "Centers: {} Contours: {}".format(
len(filtered_centers), len(cntrs)))
nparr = np.frombuffer(im, np.uint8)
im = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
tracks, susp_reg = self.tracker.track(centers=filtered_centers,
cntrs=cntrs)
logger.debug(self.log +
"Suspicious region: {} ".format(len(susp_reg)))
bbox_warped = self.warper.warp_image(
img_shape=[im.shape[0], im.shape[1]],
coordinates=susp_reg,
camera_id=1)
logger.debug(
self.log +
"Warped suspicious region: {} ".format(len(bbox_warped)))
susp_reg = [i.tolist() for i in susp_reg]
bbox_warped = [i.tolist() for i in bbox_warped]
self.suspicious_regions = {
'suspicious': susp_reg,
"mapped": bbox_warped
}
import PyQt5
from utils.driver import Driver
from utils.neuron import Neuron
from utils.detector import Detector
from utils.player import Player
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
import time
import keyboard
from brian2 import NeuronGroup, ms
# initialise classes
det = Detector("hand")
plr = Player()
drv = Driver()
time.sleep(0.1)
drv.lowerTurn()
## Comment to remove plottingqq
fig, ax = plt.subplots()
ax.set_ylim([-0.08, 0.07])
line1, = ax.plot(np.linspace(0, 10000, 10000), np.zeros(10000), 'r-')
line2, = ax.plot(np.linspace(0, 10000, 10000), np.zeros(10000), 'b-')
line3, = ax.plot(np.linspace(0, 10000, 10000), np.zeros(10000), 'g-')
plt.show(block=False)
fig.canvas.draw()
## /Comment to remove plotting
# Izhikevitch Neuron Model from http://brian2.readthedocs.io/en/stable/introduction/brian1_to_2/library.html
def main():
data = []
# stream = "rtsp://*****:*****@168.120.33.119"
# dateday = datetime.date.today()
date = datetime.datetime.now()
start = date.minute
# ToD = ""
keys = ["in", "out", "total", "morning", "afternoon", "date", "ToD", "trackers"]
record = {key: 0 for key in keys}
ENTRY_LINES = [((0, 200, 640, 200), (0, 230, 640, 230), [1, 2])]
entry_boundaries = [Boundary(boundary[0], boundary[1], sequence=boundary[2]) for boundary in ENTRY_LINES]
tracker = PeopleTracker(entry_boundaries, entries=record["in"], exits=record["out"], count=record["total"])
detector = Detector("model/frozen_inference_graph.pb")
# streamer = FileVideoStream(stream).start()
streamer = WebcamVideoStream("3.mp4").start()
# streamer = RTSPVideoFeed(stream)
# streamer.open()
file_name = 'output/' + str(date.day) + '-' + str(date.minute) + '.avi'
file_path = os.path.join(os.getcwd(), file_name)
writer = cv2.VideoWriter(file_path, cv2.VideoWriter_fourcc(*'XVID'), 30.0, (640, 360))
COLORS = np.random.uniform(0, 255, (100, 3))
#Run people counting
while True:
# Working hours
# if date.hour >= 9 and date.hour <= 20:00:
# run
# else :
# stop
try:
ret, frame = streamer.read()
if ret:
# timeofday = datetime.datetime.now().strftime("%H:%M:%S")
# if timeofday < "12:00:00":
# ToD = "Morning"
# elif timeofday >= "12:00:00":
# ToD = "Afternoon"
frame = imutils.resize(frame, width=640)
points = detector.detect(frame, (20, 40), (100, 200), threshold=0.4)
tracker.update(points, update_type='distance')
tracker.check()
data = tracker.get_data(type= 'dict')
coords = tracker.get_tracker_dictionary()
# print(coords)
if coords:
for k, v in coords.items():
print(k, v)
cv2.circle(frame, (v['center'][0], v['center'][1]), 4, (0, 255, 0), -1)
cv2.rectangle(frame, (v['coord']),
(0, 255, 0), 2)
# for c in coords:
#
for bound in entry_boundaries:
points = bound.get_lines()
for point in points:
cv2.line(frame, point[0], point[1], (255, 0, 0), 2)
info = [
("Out", data['out']),
("In", data['in']),
]
for (i, (k, v)) in enumerate(info):
text = "{}: {}".format(k, v)
cv2.putText(frame, text, (24, 360 - ((i * 20) + 20)),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
writer.write(frame)
cv2.imshow('Frame', frame)
end = datetime.datetime.now().minute
if (end - start == 1):
row = [str(data['date']), str(data['morning']), str(data['afternoon']), str(data['in'])]
update_csv(row)
start = datetime.datetime.now().minute
# Reset everyday
# if dateday == datetime.date.today() - datetime.timedelta(days=1):
# main()
# keys = ["in", "out", "total", "morning", "afternoon", "date", "ToD", "trackers"]
# record = {key: 0 for key in keys}
# ENTRY_LINES = [((0, 200, 640, 200), (0, 230, 640, 230), [1, 2])]
# entry_boundaries = [Boundary(boundary[0], boundary[1], sequence=boundary[2]) for boundary in ENTRY_LINES]
# tracker = PeopleTracker(entry_boundaries, entries=record["in"], exits=record["out"], count=record["total"])
# dateday = datetime.date.today()
if cv2.waitKey(1) == ord('q'):
cv2.destroyAllWindows()
streamer.release()
writer.release()
break
else:
print("End of video")
cv2.destroyAllWindows()
streamer.release()
writer.release()
break
except Exception:
import logging
logging.exception('Oops: error occurred')
streamer.release()
writer.release()
sys.exit(1)
return
streamer.release()
writer.release()
return
from utils.player import Player
from utils.driver import Driver
from utils.detector import Detector
import keyboard
import time
# initialize videoplayer, car driver, and cup detector
plr = Player()
drv = Driver()
det = Detector("hand")
while True:
frame = plr.read()
p0, p1, p2 = det.predict(frame)
print("------------")
print(p0, "|", p1, "|", p2)
print("------------")
# if a hand is likely >50% on screen, engage movement
if max(p0, p1, p2) > 0.5:
if p2 > p1 and p2 > p0:
drv.right()
time.sleep(0.1)
drv.stop()
elif p0 > p1 and p0 > p2:
drv.left()
time.sleep(0.1)
drv.stop()
else:
drv.forward()
# 侦测
from utils.detector import Detector
import cv2
import time
from PIL import Image, ImageDraw
if __name__ == '__main__':
# 用摄像头框人脸
detector = Detector()
cap = cv2.VideoCapture(0)
while cap.isOpened():
ret, frame = cap.read()
if ret:
start_time = time.time()
frame = cv2.bilateralFilter(frame, 10, 30, 30)
frames = frame[:, :, ::-1]
image = Image.fromarray(frames, 'RGB')
imDraw = ImageDraw.Draw(image)
boxes = detector.detect(image)
len = 0.2
for box in boxes: # 多个框，没循环一次框一个人脸
x1 = int(box[0])
y1 = int(box[1])
x2 = int(box[2])
y2 = int(box[3])
px1 = int(box[5])
py1 = int(box[6])
px2 = int(box[7])
# always sort import statements by length
import re
import cv2
import time
import keyboard
import numpy as np
from utils.player import Player
from utils.driver import Driver
from utils.detector import Detector
# initialize player, driver, and detector
plr = Player()
drv = Driver()
det = Detector("color", "cyan")
path = []
count = 0
while True:
count += 1
if count > 30: # keep tcp from dying
drv.keepAwake()
frame = plr.read()
# p = size of contour, 0,1,2 is where in the screen its center is
p0, p1, p2 = det.predict(frame)
print(p0, p1, p2)
if max(p0, p1,
p2) > 100: # if contour size is greater than 100 pixels squared
if p2 > p1:
drv.right()
time.sleep(0.1)
from utils.detector import Detector
det = Detector("hand")
from utils.player import Player
from utils.driver import Driver
from utils.detector import Detector
# initialize videoplayer, car driver, and cup detector
plr = Player()
drv = Driver()
det = Detector("cup")
while True:
ret, frame = plr.read()
p0, p1, p2 = det.predict(frame)
print("------------")
print(p0, "|", p1, "|", p2)
print("------------")
# if a cup is likely >50% on screen, engage movement
if max(p0, p1, p2) > 0.5:
if p2 > p1 and p2 > p0:
drv.right()
elif p0 > p1 and p0 > p2:
drv.left()
else:
drv.forward()
# remember to close the streams
drv.close()
plr.close()
def main(args):
det = Detector(model_path=args.model_path,
input_size=args.input_size,
num_classes=args.num_classes,
threshold=0.0)
img_paths = glob.glob(os.path.join(args.image_dir, '*'))
predict_labels = []
true_labels = []
for img_path in tqdm.tqdm(img_paths):
label_path = os.path.join(
args.label_dir,
os.path.splitext(os.path.basename(img_path))[0] + '.txt')
if not os.path.exists(label_path):
continue
with open(label_path) as f:
lines = f.read().splitlines()
true_label = [line.split('\t') for line in lines]
true_labels.append(true_label)
img = cv2.imread(img_path)
h_img, w_img = img.shape[:2]
results = det.detect(img)
label = []
for cls, result in results.items():
result = sorted(result, key=lambda x: x[0], reverse=True)
for prob, coord in result:
xmin, ymin, xmax, ymax = [int(i) for i in coord]
xmin /= w_img
ymin /= h_img
xmax /= w_img
ymax /= h_img
label.append([prob, cls, xmin, ymin, xmax, ymax])
predict_labels.append(label)
print('data size: {}'.format(len(predict_labels)))
APs = []
for clsid in range(args.num_classes):
APs.append([])
for iou_threshold in np.arange(0.50, 0.96, 0.05):
precisions = []
recalls = []
for prob_threshold in np.arange(0.0, 1.01, 0.1):
precision, recall = calc_precision(predict_labels, true_labels,
clsid, prob_threshold,
iou_threshold)
if recall is not None:
precisions.append(precision)
recalls.append(recall)
if len(recalls) == 0:
continue
step_num = 10
maximum_precision = np.zeros(step_num + 1)
for jx in range(len(recalls)):
v = precisions[jx]
k = int(recalls[jx] * step_num) # horizontal axis
maximum_precision[k] = max(maximum_precision[k], v)
# intrepolation
v = 0
for jx in range(step_num + 1):
v = max(v, maximum_precision[-jx - 1])
maximum_precision[-jx - 1] = v
AP = np.mean(maximum_precision)
APs[clsid].append(AP)
mAP50 = []
mAP5095 = []
for ix, AP in enumerate(APs):
if len(AP) == 0:
continue
name = get_classes(ix)
mAP50.append(AP[0])
mAP5095.append(np.mean(AP))
print('{} {}: {}'.format(ix, name, AP[0] * 100))
print('----------')
print('[email protected]: {}'.format(np.mean(mAP50) * 100))
print('mAP@[0.5:0.95]: {}'.format(np.mean(mAP5095) * 100))
import PyQt5
from utils.driver import Driver
from utils.neuron import Neuron
from utils.detector import Detector
from utils.player import Player
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
import time
import keyboard
from brian2 import NeuronGroup, ms
# initialise classes
# drv = Driver()
det = Detector("color", "orange")
plr = Player()
## Comment to remove plotting
fig, ax = plt.subplots()
ax.set_ylim([-0.08, 0.07])
line1, = ax.plot(np.linspace(0, 10000, 10000), np.zeros(10000), 'r-')
line2, = ax.plot(np.linspace(0, 10000, 10000), np.zeros(10000), 'b-')
line3, = ax.plot(np.linspace(0, 10000, 10000), np.zeros(10000), 'g-')
## /Comment to remove plotting
plt.show(block=False)
fig.canvas.draw()
# Izhikevitch Neuron Model from http://brian2.readthedocs.io/en/stable/introduction/brian1_to_2/library.html
eqs = '''dv/dt = (0.04*active/ms/mV + 0.04/ms/mV)*v**2+(5/ms)*v+130*mV/ms-w + I : volt (unless refractory)
def main(args):
det = Detector(model_path=args.model_path,
input_size=args.input_size,
num_classes=args.num_classes,
use_sfam=args.sfam,
threshold=0.05)
img_paths = glob.glob(os.path.join(args.image_dir, '*'))
img_paths.sort()
predict_labels = []
true_labels = []
for img_path in tqdm.tqdm(img_paths):
label_path = os.path.join(
args.label_dir,
os.path.splitext(os.path.basename(img_path))[0] + '.txt')
if not os.path.exists(label_path):
continue
with open(label_path) as f:
lines = f.read().splitlines()
label = []
for line in lines:
elements = line.split('\t')
class_index = int(elements[0])
xmin = float(elements[1])
ymin = float(elements[2])
xmax = float(elements[3])
ymax = float(elements[4])
label.append([class_index, xmin, ymin, xmax, ymax])
true_labels.append(label)
img = cv2.imread(img_path)
h_img, w_img = img.shape[:2]
results = det.detect(img)
label = []
for res in results:
confidence = res['confidence']
class_index = get_class_index(res['name'])
xmin = res['left'] / w_img
ymin = res['top'] / h_img
xmax = res['right'] / w_img
ymax = res['bottom'] / h_img
label.append([confidence, class_index, xmin, ymin, xmax, ymax])
predict_labels.append(label)
print('data size: {}'.format(len(predict_labels)))
AP = {}
iou_threshold = 0.50
for clsid in range(args.num_classes):
precisions = []
recalls = []
for prob_threshold in np.arange(0.0, 1.01, 0.1):
precision, recall = calc_precision(predict_labels, true_labels,
clsid, prob_threshold,
iou_threshold)
if recall is not None:
precisions.append(precision)
recalls.append(recall)
if len(recalls) == 0:
continue
step_num = 10
maximum_precision = np.zeros(step_num + 1)
for jx in range(len(recalls)):
v = precisions[jx]
k = int(recalls[jx] * step_num) # horizontal axis
maximum_precision[k] = max(maximum_precision[k], v)
# intrepolation
v = 0
for jx in range(step_num + 1):
v = max(v, maximum_precision[-jx - 1])
maximum_precision[-jx - 1] = v
AP[clsid] = np.mean(maximum_precision)
for class_index, elem in AP.items():
class_name = get_class_name(class_index)
print('{} {}: {}'.format(class_index + 1, class_name, elem * 100))
print('----------')
print('[email protected]: {}'.format(np.mean([v for i, v in AP.items()]) * 100))