TelloPyのvideo_effectを実行するとエラーが起きてしまいます

Question

Telloから物体認識を行いたくてTelloPyのvideo_effectを実行すると

Traceback (most recent call last):
  File "C:\Users\one\Anaconda3\envs\py3.5\lib\site-packages\tellopy\examples\video_effect.py", line 106, in main
    image = getSSDImage(imgpil)
  File "C:\Users\one\Anaconda3\envs\py3.5\lib\site-packages\tellopy\examples\video_effect.py", line 67, in getSSDImage
    score = top_conf[i]
UnboundLocalError: local variable 'i' referenced before assignment
local variable 'i' referenced before assignment

Traceback (most recent call last):
  File "C:\Users\one\Anaconda3\envs\py3.5\lib\site-packages\tensorflow\python\client\session.py", line 582, in __del__
UnboundLocalError: local variable 'status' referenced before assignment

Traceback (most recent call last):
  File "C:\Users\one\Anaconda3\envs\py3.5\lib\site-packages\tensorflow\python\client\session.py", line 582, in __del__
AttributeError: 'NoneType' object has no attribute 'TF_DeleteStatus'

というエラーが出てしまいます。
解決方法がわかる方、よろしくお願いします。

#ソースコード
video_effect.py(下記)のコードはTelloドローンでプログラミング！ーディープラーニングで物体認識編ーから元のコードから変更しています。

import cv2
import keras
from keras.applications.imagenet_utils import preprocess_input
from keras.backend.tensorflow_backend import set_session
from keras.models import Model
from keras.preprocessing import image
import matplotlib.pyplot as plt
import numpy as np
from scipy.misc import imread
import tensorflow as tf
from ssd_v2 import SSD300v2
from ssd_utils import BBoxUtility
from PIL import Image
import sys
import traceback
import tellopy
import av
import numpy
plt.rcParams['figure.figsize'] = (8, 8)
plt.rcParams['image.interpolation'] = 'nearest'
np.set_printoptions(suppress=True)
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.45
set_session(tf.Session(config=config))
voc_classes = ['Aeroplane', 'Bicycle', 'Bird', 'Boat', 'Bottle',
'Bus', 'Car', 'Cat', 'Chair', 'Cow', 'Diningtable',
'Dog', 'Horse','Motorbike', 'Person', 'Pottedplant',
'Sheep', 'Sofa', 'Train', 'Tvmonitor']
NUM_CLASSES = len(voc_classes) + 1
input_shape=(100, 100, 3)
model = SSD300v2(input_shape, num_classes=NUM_CLASSES)
model.load_weights('weights_SSD300.hdf5', by_name=True)
bbox_util = BBoxUtility(NUM_CLASSES)
def getSSDImage(frame):
	#global i
	img2 = image.img_to_array(frame.resize((100, 100)))
	img = np.asarray(frame)
	inputs = []
	inputs.append(img2.copy())
	inputs = preprocess_input(np.array(inputs))
	preds = model.predict(inputs, batch_size=1, verbose=1)
	results = bbox_util.detection_out(preds)
	# Parse the outputs.
	det_label = results[0][:, 0]
	det_conf = results[0][:, 1]
	det_xmin = results[0][:, 2]
	det_ymin = results[0][:, 3]
	det_xmax = results[0][:, 4]
	det_ymax = results[0][:, 5]
	# Get detections with confidence higher than 0.6.
	top_indices = [i for i, conf in enumerate(det_conf) if conf >= 0.6]
	top_conf = det_conf[top_indices]
	top_label_indices = det_label[top_indices].tolist()
	top_xmin = det_xmin[top_indices]
	top_ymin = det_ymin[top_indices]
	top_xmax = det_xmax[top_indices]
	top_ymax = det_ymax[top_indices]
	colors = plt.cm.hsv(np.linspace(0, 1, 21)).tolist()
	#print(top_conf.shape[0])
	for i in range(top_conf.shape[0]):
		xmin = int(round(top_xmin[i] * img.shape[1]))
		ymin = int(round(top_ymin[i] * img.shape[0]))
		xmax = int(round(top_xmax[i] * img.shape[1]))
		ymax = int(round(top_ymax[i] * img.shape[0]))
	#print(i)
		
	score = top_conf[i]
	label = int(top_label_indices[i])
	label_name = voc_classes[label - 1]
	display_txt = '{:0.2f}, {}'.format(score, label_name)
	color = colors[label]
	cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (int(colors[label][0]*255), int(colors[label][1]*255), int(colors[label][2]*255)), 2)
	cv2.rectangle(img, (xmin, ymin-15), (xmin+100, ymin+5), (int(colors[label][0]*255), int(colors[label][1]*255), int(colors[label][2]*255)),-1)
	cv2.putText(img, display_txt, (xmin, ymin), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1, cv2.LINE_AA)
	
	print(display_txt)
	
	imgcv = img[:, :, ::-1].copy()
	return imgcv
def main():
	drone = tellopy.Tello()
	try:
		drone.connect()
		drone.wait_for_connection(60.0)
		drone.set_loglevel(drone.LOG_INFO)
		drone.set_exposure(0)
		#container = av.open(drone.get_video_stream())
		
		retry=3
		container=None
		while container is None and 0 < retry:
			retry -= 1
			try:
				container = av.open(drone.get_video_stream())
			except av.AVError as ave:
				print(ave)
				print('retry...')
		
		frame_skip=300
		frame_count = 0
		while True:
			for frame in container.decode(video=0):
				frame_count = frame_count + 1
				if (frame_count > 300) and (frame_count%50 == 0):
					imgpil = frame.to_image()
					image = getSSDImage(imgpil)
					
					cv2.imshow('Original', image)
					cv2.waitKey(1)
	except Exception as ex:
		exc_type, exc_value, exc_traceback = sys.exc_info()
		traceback.print_exception(exc_type, exc_value, exc_traceback)
		print(ex)
	finally:
		drone.quit()
		cv2.destroyAllWindows()
if __name__ == '__main__':
	main()

Answer 1

インデントの問題でした。

import cv2
import keras
from keras.applications.imagenet_utils import preprocess_input
from keras.backend.tensorflow_backend import set_session
from keras.models import Model
from keras.preprocessing import image
import matplotlib.pyplot as plt
import numpy as np
from scipy.misc import imread
import tensorflow as tf
from ssd_v2 import SSD300v2
from ssd_utils import BBoxUtility
from PIL import Image
import sys
import traceback
import tellopy
import av
import numpy
plt.rcParams['figure.figsize'] = (8, 8)
plt.rcParams['image.interpolation'] = 'nearest'
np.set_printoptions(suppress=True)
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.45
set_session(tf.Session(config=config))
voc_classes = ['Aeroplane', 'Bicycle', 'Bird', 'Boat', 'Bottle',
'Bus', 'Car', 'Cat', 'Chair', 'Cow', 'Diningtable',
'Dog', 'Horse','Motorbike', 'Person', 'Pottedplant',
'Sheep', 'Sofa', 'Train', 'Tvmonitor']
NUM_CLASSES = len(voc_classes) + 1
input_shape=(100, 100, 3)
model = SSD300v2(input_shape, num_classes=NUM_CLASSES)
model.load_weights('weights_SSD300.hdf5', by_name=True)
bbox_util = BBoxUtility(NUM_CLASSES)
global label_name
def getSSDImage(frame):
	img2 = image.img_to_array(frame.resize((100, 100)))
	img = np.asarray(frame)
	inputs = []
	inputs.append(img2.copy())
	inputs = preprocess_input(np.array(inputs))
	preds = model.predict(inputs, batch_size=1, verbose=1)
	results = bbox_util.detection_out(preds)
	# Parse the outputs.
	det_label = results[0][:, 0]
	det_conf = results[0][:, 1]
	det_xmin = results[0][:, 2]
	det_ymin = results[0][:, 3]
	det_xmax = results[0][:, 4]
	det_ymax = results[0][:, 5]
	# Get detections with confidence higher than 0.6.
	top_indices = [i for i, conf in enumerate(det_conf) if conf >= 0.6]
	top_conf = det_conf[top_indices]
	top_label_indices = det_label[top_indices].tolist()
	top_xmin = det_xmin[top_indices]
	top_ymin = det_ymin[top_indices]
	top_xmax = det_xmax[top_indices]
	top_ymax = det_ymax[top_indices]
	colors = plt.cm.hsv(np.linspace(0, 1, 21)).tolist()
	for i in range(top_conf.shape[0]):
		xmin = int(round(top_xmin[i] * img.shape[1]))
		ymin = int(round(top_ymin[i] * img.shape[0]))
		xmax = int(round(top_xmax[i] * img.shape[1]))
		ymax = int(round(top_ymax[i] * img.shape[0]))
		score = top_conf[i]
		label = int(top_label_indices[i])
		label_name = voc_classes[label - 1]
		display_txt = '{:0.2f}, {}'.format(score, label_name)
		color = colors[label]
		cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (int(colors[label][0]*255), int(colors[label][1]*255), int(colors[label][2]*255)), 2)
		cv2.rectangle(img, (xmin, ymin-15), (xmin+100, ymin+5), (int(colors[label][0]*255), int(colors[label][1]*255), int(colors[label][2]*255)),-1)
		cv2.putText(img, display_txt, (xmin, ymin), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1, cv2.LINE_AA)
	imgcv = img[:, :, ::-1].copy()
	return imgcv
def main():
	drone = tellopy.Tello()
	try:
		drone.connect()
		drone.wait_for_connection(60.0)
		drone.set_loglevel(drone.LOG_INFO)
		drone.set_exposure(0)
		#container = av.open(drone.get_video_stream())
		
		retry=3
		container=None
		while container is None and 0 < retry:
			retry -= 1
			try:
				container = av.open(drone.get_video_stream())
			except av.AVError as ave:
				print(ave)
				print('retry...')
		
		frame_skip=300
		frame_count = 0
		while True:
			for frame in container.decode(video=0):
				frame_count = frame_count + 1
				if (frame_count > 300) and (frame_count%50 == 0):
					imgpil = frame.to_image()
					image = getSSDImage(imgpil)
					
					cv2.imshow('Original', image)
					cv2.waitKey(1)
					
	except Exception as ex:
		exc_type, exc_value, exc_traceback = sys.exc_info()
		traceback.print_exception(exc_type, exc_value, exc_traceback)
		print(ex)
	finally:
		drone.quit()
		cv2.destroyAllWindows()
if __name__ == '__main__':
	main()