def __init__(self):
config = ConfigProto()
config.gpu_options.allow_growth = True
#print('config: ', config)
#session = InteractiveSession(config=config)
STRIDES, ANCHORS, NUM_CLASS, XYSCALE = utils.load_config(FLAGS)
self.input_size = 416 # /FLAGS.size
# /images = FLAGS.images
# load model
# / if FLAGS.framework == 'tflite':
# / interpreter = tf.lite.Interpreter(model_path=FLAGS.weights)
# / else:
self.saved_model_loaded = tf.saved_model.load(
'tfyolov4/checkpoints/custom-416', tags=[tag_constants.SERVING])
def main(_argv):
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
STRIDES, ANCHORS, NUM_CLASS, XYSCALE = utils.load_config(FLAGS)
input_size = FLAGS.size
video_path = FLAGS.video
if FLAGS.framework == 'tflite':
interpreter = tf.lite.Interpreter(model_path=FLAGS.weights)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
print(input_details)
print(output_details)
else:
saved_model_loaded = tf.saved_model.load(FLAGS.weights,
tags=[tag_constants.SERVING])
infer = saved_model_loaded.signatures['serving_default']
# begin video capture
try:
vid = cv2.VideoCapture(int(video_path))
except:
vid = cv2.VideoCapture(video_path)
out = None
if FLAGS.output:
# by default VideoCapture returns float instead of int
width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(vid.get(cv2.CAP_PROP_FPS))
codec = cv2.VideoWriter_fourcc(*FLAGS.output_format)
out = cv2.VideoWriter(FLAGS.output, codec, fps, (width, height))
while True:
return_value, frame = vid.read()
if return_value:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image = Image.fromarray(frame)
else:
print('Video has ended or failed, try a different video format!')
break
frame_size = frame.shape[:2]
image_data = cv2.resize(frame, (input_size, input_size))
image_data = image_data / 255.
image_data = image_data[np.newaxis, ...].astype(np.float32)
start_time = time.time()
if FLAGS.framework == 'tflite':
interpreter.set_tensor(input_details[0]['index'], image_data)
interpreter.invoke()
pred = [
interpreter.get_tensor(output_details[i]['index'])
for i in range(len(output_details))
]
if FLAGS.model == 'yolov3' and FLAGS.tiny == True:
boxes, pred_conf = filter_boxes(pred[1],
pred[0],
score_threshold=0.25,
input_shape=tf.constant(
[input_size, input_size]))
else:
boxes, pred_conf = filter_boxes(pred[0],
pred[1],
score_threshold=0.25,
input_shape=tf.constant(
[input_size, input_size]))
else:
batch_data = tf.constant(image_data)
pred_bbox = infer(batch_data)
for key, value in pred_bbox.items():
boxes = value[:, :, 0:4]
pred_conf = value[:, :, 4:]
boxes, scores, classes, valid_detections = tf.image.combined_non_max_suppression(
boxes=tf.reshape(boxes, (tf.shape(boxes)[0], -1, 1, 4)),
scores=tf.reshape(
pred_conf,
(tf.shape(pred_conf)[0], -1, tf.shape(pred_conf)[-1])),
max_output_size_per_class=50,
max_total_size=50,
iou_threshold=FLAGS.iou,
score_threshold=FLAGS.score)
pred_bbox = [
boxes.numpy(),
scores.numpy(),
classes.numpy(),
valid_detections.numpy()
]
image = utils.draw_bbox(frame, pred_bbox)
fps = 1.0 / (time.time() - start_time)
print("FPS: %.2f" % fps)
result = np.asarray(image)
cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE)
result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
cv2.imshow("result", result)
if FLAGS.output:
out.write(result)
if cv2.waitKey(1) & 0xFF == ord('q'): break
cv2.destroyAllWindows()
# physical_device name
tf.config.list_physical_devices('GPU')
# number of GPU's available
len(tf.config.experimental.list_physical_devices('GPU'))
# code to confirm tensorflow is using GPU
tf.config.experimental.list_physical_devices('GPU')
# CONFIG GPU
from tensorflow._api.v2.compat.v1 import ConfigProto
from tensorflow._api.v2.compat.v1 import InteractiveSession
# avoid using 100% of GPU, else GPU overclock.
config = ConfigProto()
# use 50% of the GPU memory
config.gpu_options.per_process_gpu_memory_fraction = 0.5
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
# BEGIN THE PROGRAM
# import the lib's
from tensorflow.keras.layers import Input, Lambda, Dense, Flatten
from tensorflow.keras.models import Model
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input
from glob import glob
def __init__(self):
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)