I would need to detect a specific object from the library on the video input.
There are various pictures in the library. Which should be gradually loaded and compared with the video input. If the read image from the library will match one object on the video input, the program should stop and print the output log (time required for detection, detection accuracy, etc.)
I tried to use OpenCV - Python, TM_CCOEFF_NORMED function, to detect it only on a static image (not on video) and I can't.
Can you help me?
Here is a link to a file with a library and video input.
https://drive.google.com/drive/folders/1rR9U2jkoHwbY7tW1-XXhThNahYQHAPHA?usp=sharing
You can use mobilenet model with open cv for object detection
import datetime
import imutils
import numpy as np
import csv
# from centroidtracker import CentroidTracker
from pyimagesearch.centroidtracker import CentroidTracker
protopath = "mobilenet_ss/MobileNetSSD_deploy.prototxt"
modelpath = "mobilenet_ss/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)
outputlist=[]
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('project_video.mp4')
fourcc = cv2.VideoWriter_fourcc('m','p','4','v')
out = cv2.VideoWriter("output/output.mp4", fourcc, 5.0, (600,337))
fps_start_time = datetime.datetime.now()
fps = 0
total_frames = 0
lpc_count = 0
opc_count = 0
object_id_list = []
# dtime = dict()
# dwell_time = dict()
while True:
ret, frame = cap.read()
if not ret:
break
frame = imutils.resize(frame, width=600)
total_frames = total_frames + 1
(H, W) = frame.shape[:2]
#print("h,w",H,W)
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])
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)
# dtime[objectId] = datetime.datetime.now()
# dwell_time[objectId] = 0
# else:
# curr_time = datetime.datetime.now()
# old_time = dtime[objectId]
# time_diff = curr_time - old_time
# dtime[objectId] = datetime.datetime.now()
# sec = time_diff.total_seconds()
# dwell_time[objectId] += sec
# text = "{}|{}".format(objectId, int(dwell_time[objectId]))
# cv2.putText(frame, text, (x1, y1-5), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
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)
# writing to csv file
outputlist.append([lpc_count,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)
out.write(frame)
cv2.imshow("Application", frame)
key = cv2.waitKey(1)
if key == ord('q'):
break
cv2.destroyAllWindows()
main()```
**create CentroidTracker.py file and paste the below code on CentroidTracker.py file and import CentroidTracker.py where you want to use**
# import the necessary packages
from scipy.spatial import distance as dist
from collections import OrderedDict
import numpy as np
class CentroidTracker:
def __init__(self, maxDisappeared=50, maxDistance=50):
# initialize the next unique object ID along with two ordered
# dictionaries used to keep track of mapping a given object
# ID to its centroid and number of consecutive frames it has
# been marked as "disappeared", respectively
self.nextObjectID = 0
self.objects = OrderedDict()
self.disappeared = OrderedDict()
self.bbox = OrderedDict() # CHANGE
# store the number of maximum consecutive frames a given
# object is allowed to be marked as "disappeared" until we
# need to deregister the object from tracking
self.maxDisappeared = maxDisappeared
# store the maximum distance between centroids to associate
# an object -- if the distance is larger than this maximum
# distance we'll start to mark the object as "disappeared"
self.maxDistance = maxDistance
def register(self, centroid, inputRect):
# when registering an object we use the next available object
# ID to store the centroid
self.objects[self.nextObjectID] = centroid
self.bbox[self.nextObjectID] = inputRect # CHANGE
self.disappeared[self.nextObjectID] = 0
self.nextObjectID += 1
def deregister(self, objectID):
# to deregister an object ID we delete the object ID from
# both of our respective dictionaries
del self.objects[objectID]
del self.disappeared[objectID]
del self.bbox[objectID] # CHANGE
def update(self, rects):
# check to see if the list of input bounding box rectangles
# is empty
if len(rects) == 0:
# loop over any existing tracked objects and mark them
# as disappeared
for objectID in list(self.disappeared.keys()):
self.disappeared[objectID] += 1
# if we have reached a maximum number of consecutive
# frames where a given object has been marked as
# missing, deregister it
if self.disappeared[objectID] > self.maxDisappeared:
self.deregister(objectID)
# return early as there are no centroids or tracking info
# to update
# return self.objects
return self.bbox
# initialize an array of input centroids for the current frame
inputCentroids = np.zeros((len(rects), 2), dtype="int")
inputRects = []
# loop over the bounding box rectangles
for (i, (startX, startY, endX, endY)) in enumerate(rects):
# use the bounding box coordinates to derive the centroid
cX = int((startX + endX) / 2.0)
cY = int((startY + endY) / 2.0)
inputCentroids[i] = (cX, cY)
inputRects.append(rects[i]) # CHANGE
# if we are currently not tracking any objects take the input
# centroids and register each of them
if len(self.objects) == 0:
for i in range(0, len(inputCentroids)):
self.register(inputCentroids[i], inputRects[i]) # CHANGE
# otherwise, are are currently tracking objects so we need to
# try to match the input centroids to existing object
# centroids
else:
# grab the set of object IDs and corresponding centroids
objectIDs = list(self.objects.keys())
objectCentroids = list(self.objects.values())
# compute the distance between each pair of object
# centroids and input centroids, respectively -- our
# goal will be to match an input centroid to an existing
# object centroid
D = dist.cdist(np.array(objectCentroids), inputCentroids)
# in order to perform this matching we must (1) find the
# smallest value in each row and then (2) sort the row
# indexes based on their minimum values so that the row
# with the smallest value as at the *front* of the index
# list
rows = D.min(axis=1).argsort()
# next, we perform a similar process on the columns by
# finding the smallest value in each column and then
# sorting using the previously computed row index list
cols = D.argmin(axis=1)[rows]
# in order to determine if we need to update, register,
# or deregister an object we need to keep track of which
# of the rows and column indexes we have already examined
usedRows = set()
usedCols = set()
# loop over the combination of the (row, column) index
# tuples
for (row, col) in zip(rows, cols):
# if we have already examined either the row or
# column value before, ignore it
if row in usedRows or col in usedCols:
continue
# if the distance between centroids is greater than
# the maximum distance, do not associate the two
# centroids to the same object
if D[row, col] > self.maxDistance:
continue
# otherwise, grab the object ID for the current row,
# set its new centroid, and reset the disappeared
# counter
objectID = objectIDs[row]
self.objects[objectID] = inputCentroids[col]
self.bbox[objectID] = inputRects[col] # CHANGE
self.disappeared[objectID] = 0
# indicate that we have examined each of the row and
# column indexes, respectively
usedRows.add(row)
usedCols.add(col)
# compute both the row and column index we have NOT yet
# examined
unusedRows = set(range(0, D.shape[0])).difference(usedRows)
unusedCols = set(range(0, D.shape[1])).difference(usedCols)
# in the event that the number of object centroids is
# equal or greater than the number of input centroids
# we need to check and see if some of these objects have
# potentially disappeared
if D.shape[0] >= D.shape[1]:
# loop over the unused row indexes
for row in unusedRows:
# grab the object ID for the corresponding row
# index and increment the disappeared counter
objectID = objectIDs[row]
self.disappeared[objectID] += 1
# check to see if the number of consecutive
# frames the object has been marked "disappeared"
# for warrants deregistering the object
if self.disappeared[objectID] > self.maxDisappeared:
self.deregister(objectID)
# otherwise, if the number of input centroids is greater
# than the number of existing object centroids we need to
# register each new input centroid as a trackable object
else:
for col in unusedCols:
self.register(inputCentroids[col], inputRects[col])
# return the set of trackable objects
# return self.objects
return self.bbox