Я работал с Yolov3 Object Detection and Tracking. Ниже приведен код для обнаружения объектов и отслеживания центроидов для отмеченных объектов. Я могу нарисовать линию трассировки для этого объекта. Мой вопрос: как назначить уникальный идентификатор для каждого объекта в общем видео и отслеживать их.
import os
os.chdir("C:/Users/PavanGoli/YOLO_Implementation/")
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
from YOLO_Implementation.tracker import Tracker
import copy
import colorsys
#import os
from timeit import default_timer as timer
import cv2
import numpy as np
from keras import backend as K
from keras.models import load_model
from keras.layers import Input
from PIL import Image, ImageFont, ImageDraw
Image.MAX_IMAGE_PIXELS = None
from YOLO_Implementation.yolo3.model import yolo_eval, yolo_body, tiny_yolo_body
from YOLO_Implementation.yolo3.utils import image_preporcess
#from keras.utils import multi_gpu_model
import csv
class YOLO(object):
_defaults = {
"model_path": 'C:/Users/PavanGoli/OIDv4_ToolKit/logs/trained_weights_final.h5',
"anchors_path": 'C:/Users/PavanGoli/OIDv4_ToolKit/model_data/tiny_yolo_anchors.txt',
"classes_path": 'C:/Users/PavanGoli/OIDv4_ToolKit/4_CLASS_test_classes.txt',
"score" : 0.5,
"iou" : 0.5,
"model_image_size" : (416, 416),
#"gpu_num" : 0,
}
@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()
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.
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'
print('{} model, anchors, and classes loaded.'.format(model_path))
# Generate colors for drawing bounding boxes.
hsv_tuples = [(x / len(self.class_names), 1., 1.)
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, ))
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):
start = timer()
centers=[]
images = []
Objects_list = []
image_id = 0
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 = image_preporcess(np.copy(image), tuple(reversed(self.model_image_size)))
image_data = boxed_image
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]], #[image.shape[0], image.shape[1]]
K.learning_phase(): 0
})
#print(len(out_boxes))
#print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
font = ImageFont.truetype(font='arial.ttf',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]
number=len(out_boxes)
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
tracker = Tracker(160, 30, 6, 1)
label_size = draw.textsize(label, font)
top, left, bottom, right = box
center=(int ((left+right)//2),int((top+bottom)//2))
b=np.array([[(left+right)//2],[(top+bottom)//2]])
#boxes.append([top,left,bottom,right])
centers.append(b)
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'))
Objects_list.append([top,left,bottom,right])
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# 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.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()
#print(end - start)
return image,centers,number,Objects_list
def close_session(self):
self.sess.close()
def detect_video(yolo, video_path, output_path=""):
tracker_id = []
vid = cv2.VideoCapture(video_path)
if not vid.isOpened():
raise IOError("Couldn't open webcam or video")
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(output_path,fourcc,25.0, (1280,720),isColor = True)
tracker = Tracker(160, 30, 6, 1)
# Variables initialization
skip_frame_count = 0
track_colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0),
(0, 255, 255), (255, 0, 255), (255, 127, 255),
(127, 0, 255), (127, 0, 127)]
frame_id = 0
# count = 0
while True:
return_value, frame = vid.read()
if not return_value:
break
image = Image.fromarray(frame)
image,centers,number,Objects_list = yolo.detect_image(image)
result = np.asarray(image)
font = cv2.FONT_HERSHEY_SIMPLEX
if (len(centers) > 0):
# Track object using Kalman Filter
tracker.Update(centers)
# For identified object tracks draw tracking line
# Use various colors to indicate different track_id
for i in range(len(tracker.tracks)):
# print(tracker.tracks[i].track_id)
if (len(tracker.tracks[i].trace) > 1):
for j in range(len(tracker.tracks[i].trace) - 1):
# Draw trace line
x1 = tracker.tracks[i].trace[j][0][0]
y1 = tracker.tracks[i].trace[j][1][0]
x2 = tracker.tracks[i].trace[j + 1][0][0]
y2 = tracker.tracks[i].trace[j + 1][1][0]
clr = tracker.tracks[i].track_id % 9
cv2.line(result, (int(x1), int(y1)), (int(x2), int(y2)),track_colors[clr], 4)
# Display the resulting tracking frame
cv2.imshow('Tracking', result)
###################################################
cv2.namedWindow("result", cv2.WINDOW_NORMAL)
cv2.imshow("result", result)
#out.write(result)
if cv2.waitKey(100) & 0xFF == ord('q'):
break
vid.release()
cv2.destroyAllWindows()
yolo.close_session()
if __name__ == '__main__':
yolo = YOLO()
detect_video(yolo,"C:/Users/PavanGoli/YOLO_Implementation/New_Video1.mp4","C:/Users/PavanGoli/YOLO_Implementation/Example_4_tracker.mp4")
Как назначить уникальный идентификатор для каждого объекта в верхней части окна и отслеживать их?