def classify_image(model_file, image_file, image_quantization=None):
"""Runs image classification and returns result with the highest score.
Args:
model_file: string, model file name.
image_file: string, image file name.
image_quantization: (scale: float, zero_point: float), assumed image
quantization parameters.
Returns:
Classification result with the highest score as (index, score) tuple.
"""
interpreter = make_interpreter(test_data_path(model_file))
interpreter.allocate_tensors()
image = test_image(image_file, common.input_size(interpreter))
input_type = common.input_details(interpreter, 'dtype')
if np.issubdtype(input_type, np.floating):
# This preprocessing is specific to MobileNet V1 with floating point input.
image = (input_type(image) - 127.5) / 127.5
if np.issubdtype(input_type, np.integer) and image_quantization:
image = rescale_image(
image, image_quantization,
common.input_details(interpreter, 'quantization'), input_type)
common.set_input(interpreter, image)
interpreter.invoke()
return classify.get_classes(interpreter)[0]
def predict():
data = {"success": False}
if flask.request.method == "POST":
if flask.request.files.get("image"):
image_file = flask.request.files["image"]
image = Image.open(image_file).convert('RGB').resize(
HOLDER['size'], Image.ANTIALIAS)
params = common.input_details(HOLDER['interpreter'],
'quantization_parameters')
scale = params['scales']
zero_point = params['zero_points']
mean = 128.0
std = 128.0
if abs(scale * std - 1) < 1e-5 and abs(mean - zero_point) < 1e-5:
# Input data does not require preprocessing.
common.set_input(HOLDER['interpreter'], image)
else:
# Input data requires preprocessing
normalized_input = (np.asarray(image) -
mean) / (std * scale) + zero_point
np.clip(normalized_input, 0, 255, out=normalized_input)
common.set_input(HOLDER['interpreter'],
normalized_input.astype(np.uint8))
start = time.perf_counter()
HOLDER['interpreter'].invoke()
inference_time = time.perf_counter() - start
classes = classify.get_classes(HOLDER['interpreter'],
HOLDER['top_k'], 0.0)
if classes:
data["success"] = True
data["inference-time"] = '%.2f ms' % (inference_time * 1000)
preds = []
for c in classes:
preds.append({
"score": float(c.score),
"label": HOLDER['labels'].get(c.id, c.id)
})
data["predictions"] = preds
return flask.jsonify(data)
def init(args):
global HOLDER
HOLDER['model'] = args.model
labels_file = args.models_directory + args.labels
labels = read_label_file(labels_file) if args.labels else {}
model_file = args.models_directory + args.model
interpreter = make_interpreter(model_file)
interpreter.allocate_tensors()
print("\n Loaded engine with model : {}".format(model_file))
# Model must be uint8 quantized
if common.input_details(interpreter, 'dtype') != np.uint8:
raise ValueError('Only support uint8 input type.')
size = common.input_size(interpreter)
HOLDER['labels'] = labels
HOLDER['interpreter'] = interpreter
HOLDER['size'] = size
HOLDER['top_k'] = args.top_k
def main():
global mot_tracker
default_model_dir = '../models'
default_model = 'mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite'
default_labels = 'coco_labels.txt'
parser = argparse.ArgumentParser()
parser.add_argument('--model', help='.tflite model path',
default=os.path.join(default_model_dir,default_model))
parser.add_argument('--labels', help='label file path',
default=os.path.join(default_model_dir, default_labels))
parser.add_argument('--top_k', type=int, default=3,
help='number of categories with highest score to display')
parser.add_argument('--camera_idx', type=int, help='Index of which video source to use. ', default = 0)
parser.add_argument('--threshold', type=float, default=0.1,
help='classifier score threshold')
parser.add_argument('--tracker', help='Name of the Object Tracker To be used.',
default=None,
choices=[None, 'sort'])
parser.add_argument('--videosrc', help='Directly connected (dev) or Networked (net) video source. ', choices=['dev','net','file'],
default='dev')
parser.add_argument('--display', help='Is a display attached',
default='False',
choices=['True', 'False'])
parser.add_argument('--netsrc', help="Networked video source, example format: rtsp://192.168.1.43/mpeg4/media.amp",)
parser.add_argument('--filesrc', help="Video file source. The videos subdirectory gets mapped into the Docker container, so place your files there.",)
parser.add_argument('--modelInt8', help="Model expects input tensors to be Int8, not UInt8", default='False', choices=['True', 'False'])
args = parser.parse_args()
trackerName=args.tracker
''' Check for the object tracker.'''
if trackerName != None:
if trackerName == 'mediapipe':
if detectCoralDevBoard():
objectOfTracker = ObjectTracker('mediapipe')
else:
print("Tracker MediaPipe is only available on the Dev Board. Keeping the tracker as None")
trackerName = None
else:
objectOfTracker = ObjectTracker(trackerName)
else:
pass
if trackerName != None and objectOfTracker:
mot_tracker = objectOfTracker.trackerObject.mot_tracker
else:
mot_tracker = None
print('Loading {} with {} labels.'.format(args.model, args.labels))
interpreter = make_interpreter(args.model)
interpreter.allocate_tensors()
labels = read_label_file(args.labels)
inference_size = input_size(interpreter)
if args.modelInt8=='True':
model_int8 = True
else:
model_int8 = False
if args.videosrc=='dev':
cap = cv2.VideoCapture(args.camera_idx)
elif args.videosrc=='file':
cap = cv2.VideoCapture(args.filesrc)
else:
if args.netsrc==None:
print("--videosrc was set to net but --netsrc was not specified")
sys.exit()
cap = cv2.VideoCapture(args.netsrc)
cap.set(cv2.CAP_PROP_BUFFERSIZE, 0)
while cap.isOpened():
ret, frame = cap.read()
if not ret:
if args.videosrc=='file':
cap = cv2.VideoCapture(args.filesrc)
continue
else:
break
cv2_im = frame
cv2_im_rgb = cv2.cvtColor(cv2_im, cv2.COLOR_BGR2RGB)
cv2_im_rgb = cv2.resize(cv2_im_rgb, inference_size)
if model_int8:
im_pil = Image.fromarray(cv2_im_rgb)
input_type = common.input_details(interpreter, 'dtype')
img = (input_type(cv2_im_rgb)- 127.5) / 128.0
run_inference(interpreter, img.flatten())
else:
run_inference(interpreter, cv2_im_rgb.tobytes())
objs = get_objects(interpreter, args.threshold)[:args.top_k]
height, width, channels = cv2_im.shape
scale_x, scale_y = width / inference_size[0], height / inference_size[1]
detections = [] # np.array([])
for obj in objs:
bbox = obj.bbox.scale(scale_x, scale_y)
element = [] # np.array([])
element.append(bbox.xmin)
element.append(bbox.ymin)
element.append(bbox.xmax)
element.append(bbox.ymax)
element.append(obj.score) # print('element= ',element)
element.append(obj.id)
detections.append(element) # print('dets: ',dets)
# convert to numpy array # print('npdets: ',dets)
detections = np.array(detections)
trdata = []
trackerFlag = False
if detections.any():
if mot_tracker != None:
trdata = mot_tracker.update(detections)
trackerFlag = True
cv2_im = append_objs_to_img(cv2_im, detections, labels, trdata, trackerFlag)
if args.display == 'True':
cv2.imshow('frame', cv2_im)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def main():
global mot_tracker
global mqtt_bridge
global mqtt_topic
camera_width = 1280
camera_height = 720
default_model_dir = '../models'
default_model = 'mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite'
default_labels = 'coco_labels.txt'
parser = argparse.ArgumentParser()
parser.add_argument('--model',
help='.tflite model path',
default=os.path.join(default_model_dir, default_model))
parser.add_argument('--labels',
help='label file path',
default=os.path.join(default_model_dir,
default_labels))
parser.add_argument(
'--top_k',
type=int,
default=3,
help='number of categories with highest score to display')
parser.add_argument('--camera_idx',
type=int,
help='Index of which video source to use. ',
default=0)
parser.add_argument('--threshold',
type=float,
default=0.1,
help='classifier score threshold')
parser.add_argument('--tracker',
help='Name of the Object Tracker To be used.',
default=None,
choices=[None, 'sort'])
parser.add_argument(
'--videosrc',
help='Directly connected (dev) or Networked (net) video source. ',
choices=['dev', 'net', 'file'],
default='dev')
parser.add_argument('--display',
help='Is a display attached',
default='False',
choices=['True', 'False'])
parser.add_argument(
'--netsrc',
help=
"Networked video source, example format: rtsp://192.168.1.43/mpeg4/media.amp",
)
parser.add_argument(
'--filesrc',
help=
"Video file source. The videos subdirectory gets mapped into the Docker container, so place your files there.",
)
parser.add_argument(
'--modelInt8',
help="Model expects input tensors to be Int8, not UInt8",
default='False',
choices=['True', 'False'])
parser.add_argument('--mqtt-host',
help="MQTT broker hostname",
default='127.0.0.1')
parser.add_argument('--mqtt-port',
type=int,
help="MQTT broker port number (default 1883)",
default=1883)
parser.add_argument('--mqtt-topic',
dest='mqtt_topic',
help="MQTT Object Tracking topic",
default="skyscan/object/json")
args = parser.parse_args()
trackerName = args.tracker
''' Check for the object tracker.'''
if trackerName != None:
if trackerName == 'mediapipe':
if detectCoralDevBoard():
objectOfTracker = ObjectTracker('mediapipe')
else:
print(
"Tracker MediaPipe is only available on the Dev Board. Keeping the tracker as None"
)
trackerName = None
else:
objectOfTracker = ObjectTracker(trackerName)
else:
pass
if trackerName != None and objectOfTracker:
mot_tracker = objectOfTracker.trackerObject.mot_tracker
else:
mot_tracker = None
mqtt_topic = args.mqtt_topic
mqtt_bridge = mqtt_wrapper.bridge(host=args.mqtt_host,
port=args.mqtt_port,
client_id="skyscan-object-tracker-%s" %
(ID))
mqtt_bridge.publish("skyscan/registration",
"skyscan-adsb-mqtt-" + ID + " Registration", 0, False)
print('Loading {} with {} labels.'.format(args.model, args.labels))
interpreter = make_interpreter(args.model)
interpreter.allocate_tensors()
labels = read_label_file(args.labels)
inference_size = input_size(interpreter)
if args.modelInt8 == 'True':
model_int8 = True
else:
model_int8 = False
if args.videosrc == 'dev':
cap = cv2.VideoCapture(args.camera_idx)
elif args.videosrc == 'file':
cap = cv2.VideoCapture(args.filesrc)
else:
if args.netsrc == None:
print("--videosrc was set to net but --netsrc was not specified")
sys.exit()
cap = cv2.VideoCapture(args.netsrc)
cap.set(cv2.CAP_PROP_BUFFERSIZE, 0)
timeHeartbeat = 0
while cap.isOpened():
if timeHeartbeat < time.mktime(time.gmtime()):
timeHeartbeat = time.mktime(time.gmtime()) + 10
mqtt_bridge.publish("skyscan/heartbeat",
"skyscan-object-tracker-" + ID + " Heartbeat",
0, False)
start_time = time.monotonic()
ret, frame = cap.read()
if not ret:
if args.videosrc == 'file':
cap = cv2.VideoCapture(args.filesrc)
continue
else:
break
cv2_im = frame
cv2_im_rgb = cv2.cvtColor(cv2_im, cv2.COLOR_BGR2RGB)
cv2_im_rgb = cv2.resize(cv2_im_rgb, inference_size)
if model_int8:
im_pil = Image.fromarray(cv2_im_rgb)
input_type = common.input_details(interpreter, 'dtype')
img = (input_type(cv2_im_rgb) - 127.5) / 128.0
run_inference(interpreter, img.flatten())
else:
run_inference(interpreter, cv2_im_rgb.tobytes())
objs = get_objects(interpreter, args.threshold)[:args.top_k]
height, width, channels = cv2_im.shape
scale_x, scale_y = width / inference_size[0], height / inference_size[1]
detections = [] # np.array([])
for obj in objs:
bbox = obj.bbox.scale(scale_x, scale_y)
element = [] # np.array([])
element.append(bbox.xmin)
element.append(bbox.ymin)
element.append(bbox.xmax)
element.append(bbox.ymax)
element.append(obj.score) # print('element= ',element)
element.append(obj.id)
detections.append(element) # print('dets: ',dets)
# convert to numpy array # print('npdets: ',dets)
detections = np.array(detections)
trdata = []
trackerFlag = False
if detections.any():
if mot_tracker != None:
trdata = mot_tracker.update(detections)
trackerFlag = True
cv2_im = append_objs_to_img(cv2_im, detections, labels, trdata,
trackerFlag)
follow_x, follow_y = object_to_follow(detections, labels, trdata,
trackerFlag)
if args.display == 'True':
cv2.imshow('frame', cv2_im)
if follow_x != None:
follow_x = int(follow_x * (camera_height / height))
follow_y = int(follow_y * (camera_width / width))
coordinates = motionControl(follow_x, follow_y)
follow = {"x": coordinates[0], "y": coordinates[1]}
follow_json = json.dumps(follow)
end_time = time.monotonic()
print("x: {} y:{} new_x: {} new_y: {} Inference: {:.2f} ms".format(
follow_x, follow_y, coordinates[0], coordinates[1],
(end_time - start_time) * 1000))
mqtt_bridge.publish(mqtt_topic, follow_json, 0, False)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-m',
'--model',
required=True,
help='File path of .tflite file.')
parser.add_argument('-i',
'--input',
required=True,
help='Image to be classified.')
parser.add_argument('-l', '--labels', help='File path of labels file.')
parser.add_argument('-k',
'--top_k',
type=int,
default=1,
help='Max number of classification results')
parser.add_argument('-t',
'--threshold',
type=float,
default=0.0,
help='Classification score threshold')
parser.add_argument('-c',
'--count',
type=int,
default=5,
help='Number of times to run inference')
parser.add_argument('-a',
'--input_mean',
type=float,
default=128.0,
help='Mean value for input normalization')
parser.add_argument('-s',
'--input_std',
type=float,
default=128.0,
help='STD value for input normalization')
args = parser.parse_args()
labels = read_label_file(args.labels) if args.labels else {}
interpreter = make_interpreter(*args.model.split('@'))
interpreter.allocate_tensors()
# Model must be uint8 quantized
if common.input_details(interpreter, 'dtype') != np.uint8:
raise ValueError('Only support uint8 input type.')
size = common.input_size(interpreter)
image = Image.open(args.input).convert('RGB').resize(size, Image.ANTIALIAS)
# Image data must go through two transforms before running inference:
# 1. normalization: f = (input - mean) / std
# 2. quantization: q = f / scale + zero_point
# The following code combines the two steps as such:
# q = (input - mean) / (std * scale) + zero_point
# However, if std * scale equals 1, and mean - zero_point equals 0, the input
# does not need any preprocessing (but in practice, even if the results are
# very close to 1 and 0, it is probably okay to skip preprocessing for better
# efficiency; we use 1e-5 below instead of absolute zero).
params = common.input_details(interpreter, 'quantization_parameters')
scale = params['scales']
zero_point = params['zero_points']
mean = args.input_mean
std = args.input_std
if abs(scale * std - 1) < 1e-5 and abs(mean - zero_point) < 1e-5:
# Input data does not require preprocessing.
common.set_input(interpreter, image)
else:
# Input data requires preprocessing
normalized_input = (np.asarray(image) - mean) / (std *
scale) + zero_point
np.clip(normalized_input, 0, 255, out=normalized_input)
common.set_input(interpreter, normalized_input.astype(np.uint8))
# Run inference
print('----INFERENCE TIME----')
print('Note: The first inference on Edge TPU is slow because it includes',
'loading the model into Edge TPU memory.')
for _ in range(args.count):
start = time.perf_counter()
interpreter.invoke()
inference_time = time.perf_counter() - start
classes = classify.get_classes(interpreter, args.top_k, args.threshold)
print('%.1fms' % (inference_time * 1000))
print('-------RESULTS--------')
for c in classes:
print('%s: %.5f' % (labels.get(c.id, c.id), c.score))