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‎02-07-2019-at-night/multiple_object_detection1.py‎

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from imutils.video import VideoStream
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import imutils
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import time
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import cv2
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# OpenCV object tracker implementations
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OPENCV_OBJECT_TRACKERS = {
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#"csrt": cv2.TrackerCSRT_create,
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"kcf": cv2.TrackerKCF_create,
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"boosting": cv2.TrackerBoosting_create,
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"mil": cv2.TrackerMIL_create,
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"tld": cv2.TrackerTLD_create,
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"medianflow": cv2.TrackerMedianFlow_create,
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#"mosse": cv2.TrackerMOSSE_create
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}
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# initialize OpenCV's special multi-object tracker
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trackers = cv2.MultiTracker_create()
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video = 'videos/los_angeles.mp4'
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cap = cv2.VideoCapture(video)
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while True:
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frame = cap.read()
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frame = frame[1]
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# check to see if we have reached the end of the stream
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if frame is None:
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break
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# resize the frame (so we can process it faster)
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frame = imutils.resize(frame, width=600)
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# grab the updated bounding box coordinates (if any) for each object that is being tracked
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(success, boxes) = trackers.update(frame)
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# loop over the bounding boxes and draw then on the frame
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for box in boxes:
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(x, y, w, h) = [int(v) for v in box]
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cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
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# show the output frame
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cv2.imshow("Frame", frame)
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key = cv2.waitKey(1) & 0xFF
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# if the 's' key is selected, we are going to "select" a bounding
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# box to track
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if key == ord("s"):
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# select the bounding box of the object we want to track (make
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# sure you press ENTER or SPACE after selecting the ROI)
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box = cv2.selectROI("Frame", frame, fromCenter=False, showCrosshair=True)
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# create a new object tracker for the bounding box and add it to our multi-object tracker
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tracker = OPENCV_OBJECT_TRACKERS["kcf"]()
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trackers.add(tracker, frame, box)
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# if the `q` key was pressed, break from the loop
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elif key == ord("q"):
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break
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cap.release()
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cv2.destroyAllWindows()

‎02-07-2019-at-night/multiple_object_detection2.py‎

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import cv2
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import sys
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(major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')
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if __name__ == '__main__' :
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# Set up tracker.
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# Instead of MIL, you can also use
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tracker_types = ['BOOSTING', 'MIL','KCF', 'TLD', 'MEDIANFLOW', 'GOTURN', 'MOSSE', 'CSRT']
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tracker_type = tracker_types[2]
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if int(minor_ver) < 3:
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tracker = cv2.Tracker_create(tracker_type)
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else:
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if tracker_type == 'BOOSTING':
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tracker = cv2.TrackerBoosting_create()
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if tracker_type == 'MIL':
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tracker = cv2.TrackerMIL_create()
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if tracker_type == 'KCF':
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tracker = cv2.TrackerKCF_create()
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if tracker_type == 'TLD':
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tracker = cv2.TrackerTLD_create()
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if tracker_type == 'MEDIANFLOW':
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tracker = cv2.TrackerMedianFlow_create()
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if tracker_type == 'GOTURN':
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tracker = cv2.TrackerGOTURN_create()
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if tracker_type == 'MOSSE':
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tracker = cv2.TrackerMOSSE_create()
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if tracker_type == "CSRT":
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tracker = cv2.TrackerCSRT_create()
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# Read video
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video = 'videos/traffic.mp4'
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cap = cv2.VideoCapture(video)
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# Exit if video not opened.
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if not cap.isOpened():
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print ("Could not open video")
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sys.exit()
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# Read first frame.
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ok, frame = cap.read()
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if not ok:
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print ('Cannot read video file')
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sys.exit()
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# Define an initial bounding box
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bbox = (287, 23, 86, 320)
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# Uncomment the line below to select a different bounding box
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bbox = cv2.selectROI(frame, False)
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# Initialize tracker with first frame and bounding box
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ok = tracker.init(frame, bbox)
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while True:
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# Read a new frame
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ok, frame = cap.read()
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if not ok:
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break
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# Start timer
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timer = cv2.getTickCount()
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# Update tracker
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ok, bbox = tracker.update(frame)
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# Calculate Frames per second (FPS)
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fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer);
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# Draw bounding box
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if ok:
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# Tracking success
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p1 = (int(bbox[0]), int(bbox[1]))
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p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
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cv2.rectangle(frame, p1, p2, (255,0,0), 2, 1)
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else :
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# Tracking failure
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cv2.putText(frame, "Tracking failure detected", (100,80), cv2.FONT_HERSHEY_SIMPLEX, 0.75,(0,0,255),2)
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# Display tracker type on frame
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cv2.putText(frame, tracker_type + " Tracker", (100,20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50),2);
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# Display FPS on frame
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cv2.putText(frame, "FPS : " + str(int(fps)), (100,50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50), 2);
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# Display result
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cv2.imshow("Tracking", frame)
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# Exit if ESC pressed
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k = cv2.waitKey(1) & 0xff
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if k == 27 : break

‎02-07-2019-at-night/multiple_object_detection3.py‎

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# https://www.learnopencv.com/object-tracking-using-opencv-cpp-python/
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from imutils.video import VideoStream
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import imutils
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import time
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import cv2
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# OpenCV object tracker implementations
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OPENCV_OBJECT_TRACKERS = {
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#"csrt": cv2.TrackerCSRT_create,
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"kcf": cv2.TrackerKCF_create,
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"boosting": cv2.TrackerBoosting_create,
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"mil": cv2.TrackerMIL_create,
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"tld": cv2.TrackerTLD_create,
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"medianflow": cv2.TrackerMedianFlow_create,
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#"mosse": cv2.TrackerMOSSE_create
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}
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# initialize OpenCV's special multi-object tracker
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trackers = cv2.MultiTracker_create()
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video = 'videos/traffic.mp4'
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cap = cv2.VideoCapture(video)
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tracker_type = "kcf"
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while True:
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frame = cap.read()
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frame = frame[1]
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# check to see if we have reached the end of the stream
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if frame is None:
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break
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# resize the frame (so we can process it faster)
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frame = imutils.resize(frame, width=800)
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# Start timer
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timer = cv2.getTickCount()
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# grab the updated bounding box coordinates (if any) for each object that is being tracked
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(success, boxes) = trackers.update(frame)
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# Calculate Frames per second (FPS)
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fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer);
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# loop over the bounding boxes and draw then on the frame
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for box in boxes:
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(x, y, w, h) = [int(v) for v in box]
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if success:
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cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
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else:
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# Tracking failure
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cv2.putText(frame, "Tracking failure detected", (100,80), cv2.FONT_HERSHEY_SIMPLEX, 0.75,(0,0,255),2)
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# Display tracker type on frame
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cv2.putText(frame, tracker_type + " Tracker", (100,20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50),2);
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# Display FPS on frame
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cv2.putText(frame, "FPS : " + str(int(fps)), (100,50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50), 2);
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# show the output frame
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cv2.imshow("Frame", frame)
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key = cv2.waitKey(1) & 0xFF
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# if the 's' key is selected, we are going to "select" a bounding
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# box to track
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if key == ord("s"):
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# select the bounding box of the object we want to track (make
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# sure you press ENTER or SPACE after selecting the ROI)
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box = cv2.selectROI("Frame", frame, fromCenter=False, showCrosshair=True)
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success = True
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# create a new object tracker for the bounding box and add it to our multi-object tracker
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tracker = OPENCV_OBJECT_TRACKERS["kcf"]()
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trackers.add(tracker, frame, box)
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# if the `q` key was pressed, break from the loop
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elif key == ord("q"):
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break
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cap.release()
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cv2.destroyAllWindows()

‎02-07-2019-at-night/object_tracking_dlib.py‎

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'''
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Using Correlation Trackers in Dlib, you can track any object in a video stream without needing to train a custom object detector.
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Check out the tutorial at: http://www.codesofinterest.com/2018/02/track-any-object-in-video-with-dlib.html
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'''
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import numpy as np
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import cv2
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import dlib
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# this variable will hold the coordinates of the mouse click events.
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mousePoints = []
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def mouseEventHandler(event, x, y, flags, param):
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# references to the global mousePoints variable
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global mousePoints
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# if the left mouse button was clicked, record the starting coordinates.
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if event == cv2.EVENT_LBUTTONDOWN:
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mousePoints = [(x, y)]
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# when the left mouse button is released, record the ending coordinates.
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elif event == cv2.EVENT_LBUTTONUP:
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mousePoints.append((x, y))
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# create the video capture.
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video_capture = cv2.VideoCapture(0)
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# create a named window in OpenCV and attach the mouse event handler to it.
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cv2.namedWindow("Webcam stream")
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cv2.setMouseCallback("Webcam stream", mouseEventHandler)
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# initialize the correlation tracker.
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tracker = dlib.correlation_tracker()
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# this is the variable indicating whether to track the object or not.
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tracked = False
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while True:
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# start capturing the video stream.
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ret, frame = video_capture.read()
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if ret:
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image = frame
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# if we have two sets of coordinates from the mouse event, draw a rectangle.
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if len(mousePoints) == 2:
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cv2.rectangle(image, mousePoints[0], mousePoints[1], (0, 255, 0), 2)
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dlib_rect = dlib.rectangle(mousePoints[0][0], mousePoints[0][1], mousePoints[1][0], mousePoints[1][1])
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# tracking in progress, update the correlation tracker and get the object position.
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if tracked == True:
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tracker.update(image)
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track_rect = tracker.get_position()
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x = int(track_rect.left())
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y = int(track_rect.top())
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x1 = int(track_rect.right())
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y1 = int(track_rect.bottom())
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cv2.rectangle(image, (x, y), (x1, y1), (0, 0, 255), 2)
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# show the current frame.
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cv2.imshow("Webcam stream", image)
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# capture the keyboard event in the OpenCV window.
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ch = 0xFF & cv2.waitKey(1)
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# press "r" to stop tracking and reset the points.
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if ch == ord("r"):
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mousePoints = []
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tracked = False
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# press "t" to start tracking the currently selected object/area.
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if ch == ord("t"):
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if len(mousePoints) == 2:
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tracker.start_track(image, dlib_rect)
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tracked = True
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mousePoints = []
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# press "q" to quit the program.
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if ch == ord('q'):
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break
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# cleanup.
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video_capture.release()
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cv2.destroyAllWindows()

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