AttributeError: module 'tensorflow' has no attribute 'python'
Verfasst: Donnerstag 24. September 2020, 09:23
Hallo,
im Rahmen dieses Tutorials (https://github.com/AntonMu/TrainYourOwnYOLO) versuche ich mit meinem Jetson Nano (Ubuntu 18.04) das unten gezeigte Programm auszuführen.
Leider tritt dieser Fehler auf:
Vielen Dank schon einmal im Voraus.
Tom
im Rahmen dieses Tutorials (https://github.com/AntonMu/TrainYourOwnYOLO) versuche ich mit meinem Jetson Nano (Ubuntu 18.04) das unten gezeigte Programm auszuführen.
Leider tritt dieser Fehler auf:
Hat jemand eine Idee, wie ich dieses Problem lösen kann?AttributeError: module 'tensorflow' has no attribute 'python'
Vielen Dank schon einmal im Voraus.
Tom
Code: Alles auswählen
# -*- coding: utf-8 -*-
"""
Class definition of YOLO_v3 style detection model on image and video
"""
import colorsys
import os
from timeit import default_timer as timer
import numpy as np
from keras.models import load_model
from keras.layers import Input
from PIL import Image, ImageFont, ImageDraw
from .yolo3.model import yolo_eval, yolo_body, tiny_yolo_body
from .yolo3.utils import letterbox_image
import os
from keras.utils import multi_gpu_model
import tensorflow.compat.v1 as tf
import tensorflow.python.keras.backend as K
tf.disable_eager_execution()
class YOLO(object):
_defaults = {
"model_path": "model_data/yolo.h5",
"anchors_path": "model_data/yolo_anchors.txt",
"classes_path": "model_data/coco_classes.txt",
"score": 0.3,
"iou": 0.45,
"model_image_size": (416, 416),
"gpu_num": 1,
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
def __init__(self, **kwargs):
self.__dict__.update(self._defaults) # set up default values
self.__dict__.update(kwargs) # and update with user overrides
self.class_names = self._get_class()
self.anchors = self._get_anchors()
self.sess = K.get_session()
self.boxes, self.scores, self.classes = self.generate()# -*- coding: utf-8 -*-
"""
Class definition of YOLO_v3 style detection model on image and video
"""
import colorsys
import os
from timeit import default_timer as timer
import numpy as np
from keras.models import load_model
from keras.layers import Input
from PIL import Image, ImageFont, ImageDraw
def _get_class(self):
classes_path = os.path.expanduser(self.classes_path)
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return class_names
def _get_anchors(self):
anchors_path = os.path.expanduser(self.anchors_path)
with open(anchors_path) as f:
anchors = f.readline()
anchors = [float(x) for x in anchors.split(",")]
return np.array(anchors).reshape(-1, 2)
def generate(self):
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith(".h5"), "Keras model or weights must be a .h5 file."
# Load model, or construct model and load weights.
start = timer()
num_anchors = len(self.anchors)
num_classes = len(self.class_names)
is_tiny_version = num_anchors == 6 # default setting
try:
self.yolo_model = load_model(model_path, compile=False)
except:
self.yolo_model = (
tiny_yolo_body(
Input(shape=(None, None, 3)), num_anchors // 2, num_classes
)
if is_tiny_version
else yolo_body(
Input(shape=(None, None, 3)), num_anchors // 3, num_classes
)
)
self.yolo_model.load_weights(
self.model_path
) # make sure model, anchors and classes match
else:
assert self.yolo_model.layers[-1].output_shape[-1] == num_anchors / len(
self.yolo_model.output
) * (
num_classes + 5
), "Mismatch between model and given anchor and class sizes"
end = timer()
print(
"{} model, anchors, and classes loaded in {:.2f}sec.".format(
model_path, end - start
)
)
# Generate colors for drawing bounding boxes.
if len(self.class_names) == 1:
self.colors = ["GreenYellow"]
else:
hsv_tuples = [
(x / len(self.class_names), 1.0, 1.0)
for x in range(len(self.class_names))
]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(
lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors,
)
)
np.random.seed(10101) # Fixed seed for consistent colors across runs.
np.random.shuffle(
self.colors
) # Shuffle colors to decorrelate adjacent classes.
np.random.seed(None) # Reset seed to default.
# Generate output tensor targets for filtered bounding boxes.
self.input_image_shape = K.placeholder(shape=(2,))
if self.gpu_num >= 2:
self.yolo_model = multi_gpu_model(self.yolo_model, gpus=self.gpu_num)
boxes, scores, classes = yolo_eval(
self.yolo_model.output,
self.anchors,
len(self.class_names),
self.input_image_shape,
score_threshold=self.score,
iou_threshold=self.iou,
)
return boxes, scores, classes
def detect_image(self, image, show_stats=True):
start = timer()
if self.model_image_size != (None, None):
assert self.model_image_size[0] % 32 == 0, "Multiples of 32 required"
assert self.model_image_size[1] % 32 == 0, "Multiples of 32 required"
boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size)))
else:
new_image_size = (
image.width - (image.width % 32),
image.height - (image.height % 32),
)
boxed_image = letterbox_image(image, new_image_size)
image_data = np.array(boxed_image, dtype="float32")
if show_stats:
print(image_data.shape)
image_data /= 255.0
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
out_boxes, out_scores, out_classes = self.sess.run(
[self.boxes, self.scores, self.classes],
feed_dict={
self.yolo_model.input: image_data,
self.input_image_shape: [image.size[1], image.size[0]],
K.learning_phase(): 0,
},
)
if show_stats:
print("Found {} boxes for {}".format(len(out_boxes), "img"))
out_prediction = []
font_path = os.path.join(os.path.dirname(__file__), "font/FiraMono-Medium.otf")
font = ImageFont.truetype(
font=font_path, size=np.floor(3e-2 * image.size[1] + 0.5).astype("int32")
)
thickness = (image.size[0] + image.size[1]) // 300
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
box = out_boxes[i]
score = out_scores[i]
label = "{} {:.2f}".format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
top, left, bottom, right = box
top = max(0, np.floor(top + 0.5).astype("int32"))
left = max(0, np.floor(left + 0.5).astype("int32"))
bottom = min(image.size[1], np.floor(bottom + 0.5).astype("int32"))
right = min(image.size[0], np.floor(right + 0.5).astype("int32"))
# image was expanded to model_image_size: make sure it did not pick
# up any box outside of original image (run into this bug when
# lowering confidence threshold to 0.01)
if top > image.size[1] or right > image.size[0]:
continue
if show_stats:
print(label, (left, top), (right, bottom))
# output as xmin, ymin, xmax, ymax, class_index, confidence
out_prediction.append([left, top, right, bottom, c, score])
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, bottom])
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
draw.rectangle(
[left + i, top + i, right - i, bottom - i], outline=self.colors[c]
)
draw.rectangle(
[tuple(text_origin), tuple(text_origin + label_size)],
fill=self.colors[c],
)
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
del draw
end = timer()
if show_stats:
print("Time spent: {:.3f}sec".format(end - start))
return out_prediction, image
def close_session(self):
self.sess.close()
def detect_video(yolo, video_path, output_path=""):
import cv2
vid = cv2.VideoCapture(video_path)
if not vid.isOpened():
raise IOError("Couldn't open webcam or video")
video_FourCC = cv2.VideoWriter_fourcc(*"mp4v") # int(vid.get(cv2.CAP_PROP_FOURCC))
video_fps = vid.get(cv2.CAP_PROP_FPS)
video_size = (
int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)),
)
isOutput = True if output_path != "" else False
if isOutput:
print(
"Processing {} with frame size {} at {:.1f} FPS".format(
os.path.basename(video_path), video_size, video_fps
)
)
# print("!!! TYPE:", type(output_path), type(video_FourCC), type(video_fps), type(video_size))
out = cv2.VideoWriter(output_path, video_FourCC, video_fps, video_size)
accum_time = 0
curr_fps = 0
fps = "FPS: ??"
prev_time = timer()
while vid.isOpened():
return_value, frame = vid.read()
if not return_value:
break
# opencv images are BGR, translate to RGB
frame = frame[:, :, ::-1]
image = Image.fromarray(frame)
out_pred, image = yolo.detect_image(image, show_stats=False)
result = np.asarray(image)
curr_time = timer()
exec_time = curr_time - prev_time
prev_time = curr_time
accum_time = accum_time + exec_time
curr_fps = curr_fps + 1
if accum_time > 1:
accum_time = accum_time - 1
fps = "FPS: " + str(curr_fps)
curr_fps = 0
cv2.putText(
result,
text=fps,
org=(3, 15),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=0.50,
color=(255, 0, 0),
thickness=2,
)
# cv2.namedWindow("result", cv2.WINDOW_NORMAL)
# cv2.imshow("result", result)
if isOutput:
out.write(result[:, :, ::-1])
# if cv2.waitKey(1) & 0xFF == ord('q'):
# break
vid.release()
out.release()
# yolo.close_session()