我正在使用SSD_mobilenet_v1_coco模型（根据model zoo最快）来开发对象检测模型，并且已经使用TKinter为其编写了GUI。用户选择一个视频，然后将其通过模型传递给标签。标记完成后，用户可以在应用程序中查看标记的视频。但是，在处理视频时，窗口会完全冻结，直到视频结束。它的处理时间也大约是视频长度的6倍。我已经使用了与各种教程类似的代码，这些教程解释了如何使用网络摄像头运行模型，因此我不知道为什么会发生这种情况，它应该运行并实时显示给我理解。我对为什么会发生这种情况的唯一想法是，该模型尚未经过完全训练，我只完成了大约10％的工作。运行时间会随着经验减少吗？
这是我的注释代码：
def annotate(self):
if("annotated" in self.video_path):
messagebox.showinfo("Error", "You can't annotate an annotated video!")
elif(self.mode == "V" and not self.video_path is None):
exporting = False
MsgBox = tk.messagebox.askquestion ('Export to CSV','Do you want to export the video to CSV?',icon = 'warning')
if MsgBox == 'yes':
exporting = True
else:
b2 = tk.messagebox.askquestion('Export to CSV', "Are you sure you don't want to export the video to CSV?", icon = 'warning')
if b2 == 'no':
exporting = True
fourcc = cv2.VideoWriter_fourcc(*'MP4V')
time = datetime.datetime.now().strftime('%Y-%m-%d %H_%M_%S')
path = 'output/videos/annotated_' + time + '_output.mp4'
path = os.path.abspath(path)
out = cv2.VideoWriter(path, fourcc, 20.0, (960, 540))
self.rewind()
NUM_CLASSES = self.get_num_classes(self.label_map)
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.compat.v1.GraphDef()
with tf.io.gfile.GFile(self.model_graph, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
lmap = label_map_util.load_labelmap(self.label_map)
categories = label_map_util.convert_label_map_to_categories(lmap, max_num_classes=NUM_CLASSES, use_display_name=True)
self.category_index = label_map_util.create_category_index(categories)
fps = self.video.get(cv2.CAP_PROP_FPS)
with detection_graph.as_default():
with tf.compat.v1.Session(graph=detection_graph) as sess:
while not self.currentFrame is None:
image_np = self.get_just_frame()
if(image_np is None):
break
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
self.boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
self.scores = detection_graph.get_tensor_by_name('detection_scores:0')
self.classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name(
'num_detections:0')
(self.boxes, self.scores, self.classes, num_detections) = sess.run(
[self.boxes, self.scores, self.classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(self.boxes),
np.squeeze(self.classes).astype(np.int32),
np.squeeze(self.scores),
self.category_index,
use_normalized_coordinates=True,
line_thickness=2)
score, hsi = self.find_highest_score(self.scores)
score = score * 100
if(score > 50):
score = (str(score))[:2] + "%"
box = self.boxes[0][hsi]
box = ("{}, {}, {}, {}".format(round(box[1]*960, 2), round(box[0]*540, 2), round(box[3]*960, 2), round(box[2]*540, 2))).split(', ')
class_ = (self.category_index[self.classes[0][hsi]])['name']
timestamp = (round((float)(self.true_frame_count/fps), 2))
timestamp = str(datetime.timedelta(seconds=timestamp))
self.csv_output.append((class_, score, timestamp, self.true_frame_count, box[0], box[1], box[2], box[3], self.video_path, "../../" + path))
# Display output
out.write(image_np)
self.video.release()
out.release()
self.video = None
self.set_video_path(path)
self.video = cv2.VideoCapture(self.video_path)
if(not self.video.isOpened()):
raise ValueError("Unable to open video source", self.video_path)
ret, frame = self.get_frame()
if(ret and not frame is None):
self.photo = PIL.ImageTk.PhotoImage(image = PIL.Image.fromarray(frame))
self.canvas.create_image(0, 0, image = self.photo, anchor = NW)
anomalies_found = []
if(exporting):
anomalies_found = self.export_CSV()
if(len(anomalies_found) > 0):
message = ""
for a in anomalies_found:
message = message + a + "\n"
popup = tk.Toplevel()
popup.wm_title("Anomalies Found!")
m = tk.Text(popup)
m.pack()
m.insert(tk.END, message)
popup.mainloop()
else:
messagebox.showinfo("Notification", "No anomalies detected")
os.startfile(path)
if(self.video_path is None):
messagebox.showinfo("Error", "No video selected")`
这是基本视频播放的代码：
def update(self):
ret, frame = self.get_frame()
if(ret and not frame is None):
self.photo = PIL.ImageTk.PhotoImage(image = PIL.Image.fromarray(frame))
self.canvas.create_image(0, 0, image = self.photo, anchor = NW)
self.parent.after(self.delay, self.update)
def get_frame(self):
if(self.video.isOpened()):
ret, frame = self.video.read()
self.currentFrame = frame
height = None
width = None
if not frame is None:
height, width, channels = frame.shape
if(not height == 540 and not width == 960 and not frame is None):
frame = cv2.resize(frame, (960, 540))
if ret:
self.true_frame_count += 1
self.last_60_frames.append(frame)
while(len(self.last_60_frames) > 60):
self.last_60_frames.pop()
return (True, cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
else:
self.delay = 1000000
self.video.release()
return (True, None)
else:
self.delay = 1000000
self.video.release()
return (True, None)
def get_just_frame(self):
if(self.video.isOpened() and not self.currentFrame is None):
ret, frame = self.video.read()
self.currentFrame = frame
if ret:
frame = cv2.resize(frame, (960, 540))
self.true_frame_count += 1
return frame
#return cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
else:
self.delay = 1000000
self.video.release()
return None
else:
self.delay = 1000000
self.video.release()
return None
如何提高模型速度并保持窗口不冻结？