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import cv2
import datetime
import imutils
import numpy as np
from centroidtracker import CentroidTracker
protopath = "MobileNetSSD_deploy.prototxt"
modelpath = "MobileNetSSD_deploy.caffemodel"
detector = cv2.dnn.readNetFromCaffe(prototxt=protopath, caffeModel=modelpath)
detector.setPreferableBackend(cv2.dnn.DNN_BACKEND_INFERENCE_ENGINE)
detector.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
"sofa", "train", "tvmonitor"]
tracker = CentroidTracker(maxDisappeared=80, maxDistance=90)
def non_max_suppression_fast(boxes, overlapThresh):
try:
if len(boxes) == 0:
return []
if boxes.dtype.kind == "i":
boxes = boxes.astype("float")
pick = []
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
area = (x2 - x1 + 1) * (y2 - y1 + 1)
idxs = np.argsort(y2)
while len(idxs) > 0:
last = len(idxs) - 1
i = idxs[last]
pick.append(i)
xx1 = np.maximum(x1[i], x1[idxs[:last]])
yy1 = np.maximum(y1[i], y1[idxs[:last]])
xx2 = np.minimum(x2[i], x2[idxs[:last]])
yy2 = np.minimum(y2[i], y2[idxs[:last]])
w = np.maximum(0, xx2 - xx1 + 1)
h = np.maximum(0, yy2 - yy1 + 1)
overlap = (w * h) / area[idxs[:last]]
idxs = np.delete(idxs, np.concatenate(([last],
np.where(overlap > overlapThresh)[0])))
return boxes[pick].astype("int")
except Exception as e:
print("Exception occurred in non_max_suppression : {}".format(e))
def main():
cap = cv2.VideoCapture('test_video.mp4')
fps_start_time = datetime.datetime.now()
fps = 0
total_frames = 0
lpc_count = 0
opc_count = 0
object_id_list = []
while True:
ret, frame = cap.read()
frame = imutils.resize(frame, width=600)
total_frames = total_frames + 1
(H, W) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(frame, 0.007843, (W, H), 127.5)
detector.setInput(blob)
person_detections = detector.forward()
rects = []
for i in np.arange(0, person_detections.shape[2]):
confidence = person_detections[0, 0, i, 2]
if confidence > 0.5:
idx = int(person_detections[0, 0, i, 1])
if CLASSES[idx] != "person":
continue
person_box = person_detections[0, 0, i, 3:7] * np.array([W, H, W, H])
(startX, startY, endX, endY) = person_box.astype("int")
rects.append(person_box)
boundingboxes = np.array(rects)
boundingboxes = boundingboxes.astype(int)
rects = non_max_suppression_fast(boundingboxes, 0.3)
objects = tracker.update(rects)
for (objectId, bbox) in objects.items():
x1, y1, x2, y2 = bbox
x1 = int(x1)
y1 = int(y1)
x2 = int(x2)
y2 = int(y2)
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)
text = "ID: {}".format(objectId)
cv2.putText(frame, text, (x1, y1-5), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
if objectId not in object_id_list:
object_id_list.append(objectId)
fps_end_time = datetime.datetime.now()
time_diff = fps_end_time - fps_start_time
if time_diff.seconds == 0:
fps = 0.0
else:
fps = (total_frames / time_diff.seconds)
fps_text = "FPS: {:.2f}".format(fps)
cv2.putText(frame, fps_text, (5, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
lpc_count = len(objects)
opc_count = len(object_id_list)
lpc_txt = "LPC: {}".format(lpc_count)
opc_txt = "OPC: {}".format(opc_count)
cv2.putText(frame, lpc_txt, (5, 60), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
cv2.putText(frame, opc_txt, (5, 90), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
cv2.imshow("Application", frame)
key = cv2.waitKey(1)
if key == ord('q'):
break
cv2.destroyAllWindows()
main()
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