def detect(img_path):
img = cv2.imread(img_path)
assert img is not None
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
raw_img = CVRawImage(img)
detected = capturer.capture(raw_img)
print(f'On image detected {len(detected)} faces')
return detected
def process_one_face(img_path):
img = cv2.imread(img_path)
assert img is not None
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
raw_img = CVRawImage(img)
detected = capturer.capture(raw_img)
print(f'On image detected {len(detected)} faces')
assert len(detected) > 0
return recognizer.processing(detected[0])
def detect_and_save_templ(img_path, save_path):
img = cv2.imread(img_path)
assert img is not None
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
raw_img = CVRawImage(img)
detected = capturer.capture(raw_img)
assert len(detected) > 0
print('Detected', len(detected), 'faces')
with open(save_path, 'wb') as f:
recognizer.processing(detected[0]).save(f)
def camera_thread_func(self):
try:
prev_capture_time = time.time() * 1000
while True:
# check for stop
if self._shutdown:
break
# sleep some time to get stable frequency
if self.frame_min_delay_ms > 0:
now = time.time() * 1000
sleep_time_ms = max(
self.frame_min_delay_ms - (now - prev_capture_time), 0)
time.sleep(sleep_time_ms / 1000)
prev_capture_time = time.time() * 1000
data = ImageAndDepth()
self.source.get(data)
if not len(data.color_image):
self._shutdown = True
elif data.color_format == raw_image.Format.FORMAT_BGR:
image = np.reshape(
data.color_image,
[data.color_height, data.color_width, 3])
cvri = CVRawImage(image)
frame_id = self.video_worker.add_video_frame(
cvri, self.stream_id)
self.frames_mutex.wait_one()
new_queue_element = Pair(frame_id, data)
self.frames.put(new_queue_element)
self.frames_mutex.release_mutex()
except Exception as ex:
print('\n Worker.CameraThreadFunc exception: "{}"'.format(ex))
def __init__(self, database_list_filepath: str,
recognizer: recognizer.Recognizer, capturer: Capturer,
distance_threshold: float):
self.vw_elements = list()
self.samples = list()
self.thumbnails = list()
self.names = list()
file = open(database_list_filepath, 'r')
files = [x.strip() for x in file.readlines()]
files = [x for x in files if not x.endswith(".txt")]
files = list(filter(None, files))
files = sorted(files)
dirs = [
os.path.join(os.path.dirname(database_list_filepath),
os.path.dirname(f)) for f in files
]
files = [os.path.basename(x) for x in files]
prev_dir = ""
person_id_counter = 0
element_id_counter = 0
for (f, dir) in list(zip(files, dirs)):
if dir != prev_dir:
person_id_counter += 1
# try to open name.txt file in this dir
try:
name_file = open(os.path.join(dir, "name.txt"), 'r')
name = name_file.readline().strip()
except FileNotFoundError:
name = ""
prev_dir = dir
print(
f"processing '{f}' name: '{name}' person_id: {person_id_counter}"
)
# read image with opencv
image_path = os.path.join(dir, f)
cv_img = cv2.imread(image_path)
assert cv_img is not None, f"Not opened {image_path}"
image = CVRawImage(cv_img)
if image.format != raw_image.Format.FORMAT_BGR:
print(f"\n\nWarning: can't read image '{f}'\n\n")
continue
# capture the face
captured_samples = capturer.capture(image)
if len(captured_samples) != 1:
print(
f"\n\nWARNING: detected {len(captured_samples)} faces on '{f}' "
f"image instead of one, image ignored \n\n")
sample = captured_samples[0]
# make template
templ = recognizer.processing(sample)
# prepare data for VideoWorker
vw_element = video_worker.DatabaseElement(element_id_counter,
person_id_counter, templ,
distance_threshold)
element_id_counter += 1
thumbnail = self.make_thumbnail(sample, name)
self.vw_elements.append(vw_element)
self.samples.append(sample)
self.thumbnails.append(thumbnail)
self.names.append(name)
assert element_id_counter == len(self.vw_elements)
assert element_id_counter == len(self.samples)
assert element_id_counter == len(self.thumbnails)
assert element_id_counter == len(self.names)
def detect(img):
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
raw_img = CVRawImage(img)
detected = capturer.capture(raw_img)
print(f'On image detected {len(detected)} faces')
return detected