hy,
I’m using a Jetson Nano with JetPack 4.3 and tensorflow 2.1.0.
I try to run this tutorial GitHub - heartkilla/yolo-v3: Yolo v3 object detection implemented in Tensorflow.
When I execute the python code below with this command python3 detect.py images 0.5 0.5 data/images/dog.jpg data/images/office.jpg
an error occurs.
Any idea what i can do?
cheers
chris
Error message
2020-09-24 11:04:54.072558: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-09-24 11:04:58.047422: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libnvinfer.so.6
2020-09-24 11:04:58.049168: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libnvinfer_plugin.so.6
Traceback (most recent call last):
File "detect.py", line 104, in <module>
main(sys.argv[1], float(sys.argv[2]), float(sys.argv[3]), sys.argv[4:])
File "detect.py", line 39, in main
inputs = tf.placeholder(tf.float32, [batch_size, *_MODEL_SIZE, 3])
AttributeError: module 'tensorflow' has no attribute 'placeholder'
Complete python code
import tensorflow as tf
import sys
import cv2
from yolo_v3 import Yolo_v3
from utils import load_images, load_class_names, draw_boxes, draw_frame
_MODEL_SIZE = (416, 416)
_CLASS_NAMES_FILE = './data/labels/coco.names'
_MAX_OUTPUT_SIZE = 20
def main(type, iou_threshold, confidence_threshold, input_names):
class_names = load_class_names(_CLASS_NAMES_FILE)
n_classes = len(class_names)
model = Yolo_v3(n_classes=n_classes, model_size=_MODEL_SIZE,
max_output_size=_MAX_OUTPUT_SIZE,
iou_threshold=iou_threshold,
confidence_threshold=confidence_threshold)
if type == 'images':
batch_size = len(input_names)
batch = load_images(input_names, model_size=_MODEL_SIZE)
inputs = tf.placeholder(tf.float32, [batch_size, *_MODEL_SIZE, 3])
# loesungsversuch inputs = tf.Variable(tf.ones(shape=[None, self._num_states]), dtype=tf.float32, [batch_size, *_MODEL_SIZE, 3])
# loesungsversuch inputs = tf.Variable(tf.float32, [batch_size, *_MODEL_SIZE, 3])
detections = model(inputs, training=False)
saver = tf.train.Saver(tf.global_variables(scope='yolo_v3_model'))
with tf.Session() as sess:
saver.restore(sess, './weights/model.ckpt')
detection_result = sess.run(detections, feed_dict={inputs: batch})
draw_boxes(input_names, detection_result, class_names, _MODEL_SIZE)
print('Detections have been saved successfully.')
elif type == 'video':
# original zeile inputs = tf.placeholder(tf.float32, [1, *_MODEL_SIZE, 3])
inputs = tf.Variable(tf.float32, [1, *_MODEL_SIZE, 3])
#inputs = tf.Variable(tf.ones(shape=[None, self._num_states]), dtype=tf.float32)
detections = model(inputs, training=False)
saver = tf.train.Saver(tf.global_variables(scope='yolo_v3_model'))
with tf.Session() as sess:
saver.restore(sess, './weights/model.ckpt')
win_name = 'Video detection'
cv2.namedWindow(win_name)
cap = cv2.VideoCapture(input_names[0])
frame_size = (cap.get(cv2.CAP_PROP_FRAME_WIDTH),
cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fourcc = cv2.VideoWriter_fourcc(*'X264')
fps = cap.get(cv2.CAP_PROP_FPS)
out = cv2.VideoWriter('./detections/detections.mp4', fourcc, fps,
(int(frame_size[0]), int(frame_size[1])))
try:
while True:
ret, frame = cap.read()
if not ret:
break
resized_frame = cv2.resize(frame, dsize=_MODEL_SIZE[::-1],
interpolation=cv2.INTER_NEAREST)
detection_result = sess.run(detections,
feed_dict={inputs: [resized_frame]})
draw_frame(frame, frame_size, detection_result,
class_names, _MODEL_SIZE)
cv2.imshow(win_name, frame)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
out.write(frame)
finally:
cv2.destroyAllWindows()
cap.release()
print('Detections have been saved successfully.')
else:
raise ValueError("Inappropriate data type. Please choose either 'video' or 'images'.")
if __name__ == '__main__':
main(sys.argv[1], float(sys.argv[2]), float(sys.argv[3]), sys.argv[4:])