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run_deepsort.py
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run_deepsort.py
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import os
import time
import random
import cv2
import imutils
import dlib
import numpy as np
import tensorflow as tf
from utils import get_input_args, math_utils, file_utils
from utils import image_utils, model_utils
from trackers.deep_sort import preprocessing, nn_matching
from trackers.deep_sort.detection import Detection
from trackers.deep_sort.tracker import Tracker
from utils import generate_detections as gdet
# Initialize tracker
max_cosine_distance = 0.3
nn_budget = None
nms_max_overlap = 1.0
# deep_sort
model_filename = './trackers/deep_sort/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)
entry = 0
exit = 0
args = get_input_args.parse_user_input()
file_utils.create_paths()
if args['model'] == 'ground-truth':
input_file = './input_videos/TownCentreXVID.mp4'
else:
args['input']
# initialize the video stream, pointer to output video file, and frame dimensions
vs = cv2.VideoCapture(input_file)
fps = int(vs.get(cv2.CAP_PROP_FPS))
total = int(vs.get(cv2.CAP_PROP_FRAME_COUNT))
(W, H) = (int(vs.get(cv2.CAP_PROP_FRAME_WIDTH)), int(vs.get(cv2.CAP_PROP_FRAME_HEIGHT)))
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
writer = cv2.VideoWriter('output/output.mp4', fourcc, fps, (W, H), True)
# get line info
line = image_utils.define_ROI(int(args['line']), input_file, H, W)
if args['model'] == 'ground-truth':
detector = model_utils.GroundTruthDetections()
frame_index = 0
while True:
(grabbed, frame) = vs.read()
if not grabbed:
break
detections = detector.get_detected_items(frame_index)
features, boxes = encoder(frame, detections)
detections = [Detection(bbox, 1.0, feature) for bbox, feature in zip(boxes, features)]
tracker.update(detections)
current = {}
for track in tracker.tracks:
d = track.to_tlbr()
d = np.append(d, track.track_id)
d = d.astype(np.int32)
frame = image_utils.draw_box(frame, d, (0,255,0))
if detections != []:
cv2.putText(frame, 'Detection active', (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)
current[d[4]] = (d[0], d[1], d[2], d[3])
if d[4] in tracker.previous:
previous_box = tracker.previous[d[4]]
entry, exit = math_utils.compare_with_prev_position(previous_box, d, line, entry, exit)
frame = image_utils.annotate_frame(frame, line, entry, exit, H, W)
tracker.previous = current
writer.write(frame)
frame_index = frame_index + 1
writer.release()
vs.release()
elif args['model'] == 'haar':
person_cascade = cv2.CascadeClassifier('./detectors/haar_cascade/pedestrian.xml')
frame_index = 0
while True:
(grabbed, frame) = vs.read()
if not grabbed:
break
detections = model_utils.get_haar_detections(frame, person_cascade, frame_index)
features, boxes = encoder(frame, detections)
detections = [Detection(bbox, 1.0, feature) for bbox, feature in zip(boxes, features)]
tracker.update(detections)
current = {}
for track in tracker.tracks:
d = track.to_tlbr()
d = np.append(d, track.track_id)
d = d.astype(np.int32)
frame = image_utils.draw_box(frame, d, (0,255,0))
if detections != []:
cv2.putText(frame, 'Detection active', (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)
current[d[4]] = (d[0], d[1], d[2], d[3])
if d[4] in tracker.previous:
previous_box = tracker.previous[d[4]]
entry, exit = math_utils.compare_with_prev_position(previous_box, d, line, entry, exit)
frame = image_utils.annotate_frame(frame, line, entry, exit, H, W)
tracker.previous = current
writer.write(frame)
frame_index = frame_index + 1
writer.release()
vs.release()
elif args['model'] == 'hog':
frame_index = 0
while True:
(grabbed, frame) = vs.read()
if not grabbed:
break
detections = model_utils.get_hog_svm_detections(frame, frame_index)
features, boxes = encoder(frame, detections)
detections = [Detection(bbox, 1.0, feature) for bbox, feature in zip(boxes, features)]
tracker.update(detections)
current = {}
for track in tracker.tracks:
d = track.to_tlbr()
d = np.append(d, track.track_id)
d = d.astype(np.int32)
frame = image_utils.draw_box(frame, d, (0,255,0))
if detections != []:
cv2.putText(frame, 'Detection active', (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)
current[d[4]] = (d[0], d[1], d[2], d[3])
if d[4] in tracker.previous:
previous_box = tracker.previous[d[4]]
entry, exit = math_utils.compare_with_prev_position(previous_box, d, line, entry, exit)
frame = image_utils.annotate_frame(frame, line, entry, exit, H, W)
tracker.previous = current
writer.write(frame)
frame_index = frame_index + 1
writer.release()
vs.release()
elif args['model'] == 'yolo':
labels, net, ln = model_utils.set_yolo_model()
frame_index = 0
while True:
(grabbed, frame) = vs.read()
if not grabbed:
break
# run YOLO object detector
blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
outputs = net.forward(ln)
detections = model_utils.get_yolo_detections(outputs, labels, args['confidence'], args['threshold'], W, H, frame_index)
features, boxes = encoder(frame, detections)
detections = [Detection(bbox, 1.0, feature) for bbox, feature in zip(boxes, features)]
tracker.update(detections)
current = {}
for track in tracker.tracks:
d = track.to_tlbr()
d = np.append(d, track.track_id)
d = d.astype(np.int32)
frame = image_utils.draw_box(frame, d, (0,255,0))
if detections != []:
cv2.putText(frame, 'Detection active', (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)
current[d[4]] = (d[0], d[1], d[2], d[3])
if d[4] in tracker.previous:
previous_box = tracker.previous[d[4]]
entry, exit = math_utils.compare_with_prev_position(previous_box, d, line, entry, exit)
frame = image_utils.annotate_frame(frame, line, entry, exit, H, W)
tracker.previous = current
writer.write(frame)
frame_index = frame_index + 1
writer.release()
vs.release()
else:
# load TF detection graph and COCO class labels
detection_graph, category_index = model_utils.set_tf_model(args['model'], 'mscoco_label_map.pbtxt')
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# for all the frames that are extracted from input video
frame_index = 0
while True:
(grabbed, frame) = vs.read()
if not grabbed:
break
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: np.expand_dims(frame, axis=0)}
)
detections = model_utils.get_detections(frame, args['confidence'], args['threshold'], boxes, classes, scores, category_index, W, H, frame_index)
features, boxes = encoder(frame, detections)
detections = [Detection(bbox, 1.0, feature) for bbox, feature in zip(boxes, features)]
tracker.update(detections)
current = {}
for track in tracker.tracks:
d = track.to_tlbr()
d = np.append(d, track.track_id)
d = d.astype(np.int32)
frame = image_utils.draw_box(frame, d, (0,255,0))
if detections != []:
cv2.putText(frame, 'Detection active', (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)
current[d[4]] = (d[0], d[1], d[2], d[3])
if d[4] in tracker.previous:
previous_box = tracker.previous[d[4]]
entry, exit = math_utils.compare_with_prev_position(previous_box, d, line, entry, exit)
frame = image_utils.annotate_frame(frame, line, entry, exit, H, W)
print(frame_index)
tracker.previous = current
writer.write(frame)
frame_index = frame_index + 1
writer.release()
vs.release()