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object_tracker2.py
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object_tracker2.py
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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
class_file = open(f'{FLAGS.video}_classes.csv', 'w')
class_file.write('frame_index,track_id,class_name,bbox0,bbox1,bbox2,bbox3')
class_file.write('\n')
shape_printed = False
while True:
_, img = vid.read()
if not shape_printed:
print(f'video height: {img.shape[0]} width: {img.shape[1]}')
shape_printed = True
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)
# frame_index,track_id,class_name,bbox0,bbox1,bbox2,bbox3
class_file.write(f'{frame_index+1},{track.track_id},{class_name},{int(bbox[0])},{int(bbox[1])},{int(bbox[2])},{int(bbox[3])}')
class_file.write('\n')
### 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.putText(img, "frame: {}".format(frame_index+1), (0, 60),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
# cv2.imshow('output', img)
if FLAGS.output:
out.write(img)
if frame_index % 50 == 0:
print(f'frame: {frame_index}')
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()
class_file.close()
# cv2.destroyAllWindows()
if __name__ == '__main__':
try:
app.run(main)
except SystemExit:
pass