Open conect opened 4 years ago
Any hint? Thanks very much...
This seems like an issue with the absl package that was downloaded. Attribute error "ouput" looks like a type for output.
How can I fix it?
HI,there is an wrong with the writing of the word, it is not "ouput" it is "output" you have to change it so that it works. Thanks for your code the AI Guys Code!!!
You need to paste the following code into object tracker.py
:
import time, random
import numpy as np
from absl import app, flags, logging
from absl.flags import FLAGS
import cv2
import matplotlib.pyplot as plt
import tensorflow as tf
from yolov3_tf2.models import (
YoloV3, YoloV3Tiny
)
from yolov3_tf2.dataset import transform_images
from yolov3_tf2.utils import draw_outputs, convert_boxes
from deep_sort import preprocessing
from deep_sort import nn_matching
from deep_sort.detection import Detection
from deep_sort.tracker import Tracker
from tools import generate_detections as gdet
from PIL import Image
flags.DEFINE_string('classes', './data/labels/coco.names', 'path to classes file')
flags.DEFINE_string('weights', './weights/yolov3.tf',
'path to weights file')
flags.DEFINE_boolean('tiny', False, 'yolov3 or yolov3-tiny')
flags.DEFINE_integer('size', 416, 'resize images to')
flags.DEFINE_string('video', './data/video/test.mp4',
'path to video file or number for webcam)')
flags.DEFINE_string('output', None, 'path to output video')
flags.DEFINE_string('output_format', 'XVID', 'codec used in VideoWriter when saving video to file')
flags.DEFINE_integer('num_classes', 80, 'number of classes in the model')
def main(_argv):
# Definition of the parameters
max_cosine_distance = 0.5
nn_budget = None
nms_max_overlap = 1.0
#initialize deep sort
model_filename = 'model_data/mars-small128.pb'
encoder = gdet.create_box_encoder(model_filename, batch_size=1)
metric = nn_matching.NearestNeighborDistanceMetric("cosine", max_cosine_distance, nn_budget)
tracker = Tracker(metric)
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
if FLAGS.tiny:
yolo = YoloV3Tiny(classes=FLAGS.num_classes)
else:
yolo = YoloV3(classes=FLAGS.num_classes)
yolo.load_weights(FLAGS.weights)
logging.info('weights loaded')
class_names = [c.strip() for c in open(FLAGS.classes).readlines()]
logging.info('classes loaded')
try:
vid = cv2.VideoCapture(int(FLAGS.video))
except:
vid = cv2.VideoCapture(FLAGS.video)
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))
list_file = open('detection.txt', 'w')
frame_index = -1
fps = 0.0
count = 0
while True:
_, img = vid.read()
if img is None:
logging.warning("Empty Frame")
time.sleep(0.1)
count+=1
if count < 3:
continue
else:
break
img_in = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_in = tf.expand_dims(img_in, 0)
img_in = transform_images(img_in, FLAGS.size)
t1 = time.time()
boxes, scores, classes, nums = yolo.predict(img_in)
classes = classes[0]
names = []
for i in range(len(classes)):
names.append(class_names[int(classes[i])])
names = np.array(names)
converted_boxes = convert_boxes(img, boxes[0])
features = encoder(img, converted_boxes)
detections = [Detection(bbox, score, class_name, feature) for bbox, score, class_name, feature in zip(converted_boxes, scores[0], names, features)]
#initialize color map
cmap = plt.get_cmap('tab20b')
colors = [cmap(i)[:3] for i in np.linspace(0, 1, 20)]
# run non-maxima suppresion
boxs = np.array([d.tlwh for d in detections])
scores = np.array([d.confidence for d in detections])
classes = np.array([d.class_name for d in detections])
indices = preprocessing.non_max_suppression(boxs, classes, nms_max_overlap, scores)
detections = [detections[i] for i in indices]
# Call the tracker
tracker.predict()
tracker.update(detections)
for track in tracker.tracks:
if not track.is_confirmed() or track.time_since_update > 1:
continue
bbox = track.to_tlbr()
class_name = track.get_class()
color = colors[int(track.track_id) % len(colors)]
color = [i * 255 for i in color]
cv2.rectangle(img, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), color, 2)
cv2.rectangle(img, (int(bbox[0]), int(bbox[1]-30)), (int(bbox[0])+(len(class_name)+len(str(track.track_id)))*17, int(bbox[1])), color, -1)
cv2.putText(img, class_name + "-" + str(track.track_id),(int(bbox[0]), int(bbox[1]-10)),0, 0.75, (255,255,255),2)
### UNCOMMENT BELOW IF YOU WANT CONSTANTLY CHANGING YOLO DETECTIONS TO BE SHOWN ON SCREEN
#for det in detections:
# bbox = det.to_tlbr()
# cv2.rectangle(img,(int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])),(255,0,0), 2)
# print fps on screen
fps = ( fps + (1./(time.time()-t1)) ) / 2
cv2.putText(img, "FPS: {:.2f}".format(fps), (0, 30),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
cv2.imshow('output', img)
if FLAGS.output:
out.write(img)
frame_index = frame_index + 1
list_file.write(str(frame_index)+' ')
if len(converted_boxes) != 0:
for i in range(0,len(converted_boxes)):
list_file.write(str(converted_boxes[i][0]) + ' '+str(converted_boxes[i][1]) + ' '+str(converted_boxes[i][2]) + ' '+str(converted_boxes[i][3]) + ' ')
list_file.write('\n')
# press q to quit
if cv2.waitKey(1) == ord('q'):
break
vid.release()
if FLAGS.output:
out.release()
list_file.close()
cv2.destroyAllWindows()
if __name__ == '__main__':
try:
app.run(main)
except SystemExit:
pass
after executing: python object_tracker.py --video 0 --output ./data/video/results.avi
I get following error: