if int(minor_ver) < 3:
tracker = cv2.Tracker_create(tracker_type)
else:
if tracker_type == 'BOOSTING':
tracker = cv2.TrackerBoosting_create()
if tracker_type == 'MIL':
tracker = cv2.TrackerMIL_create()
if tracker_type == 'KCF':
tracker = cv2.TrackerKCF_create()
if tracker_type == 'TLD':
tracker = cv2.TrackerTLD_create()
if tracker_type == 'MEDIANFLOW':
tracker = cv2.TrackerMedianFlow_create()
if tracker_type == 'GOTURN':
tracker = cv2.TrackerGOTURN_create()
if tracker_type == 'MOSSE':
tracker = cv2.TrackerMOSSE_create()
if tracker_type == "CSRT":
tracker = cv2.TrackerCSRT_create()
# Read video
video = cv2.VideoCapture("los_angeles.mp4")
# Exit if video not opened.
if not video.isOpened():
print("Could not open video")
sys.exit()
# Read first frame.
ok, frame = video.read()
def main():
tracker_types = [
'BOOSTING', 'MIL', 'KCF', 'TLD', 'MEDIANFLOW', 'GOTURN', 'MOSSE',
'CSRT'
]
tracker_type = tracker_types[2]
if int(minor_ver) < 3:
tracker = cv2.Tracker_create(tracker_type)
else:
if tracker_type == 'BOOSTING':
tracker = cv2.TrackerBoosting_create()
if tracker_type == 'MIL':
tracker = cv2.TrackerMIL_create()
if tracker_type == 'KCF':
tracker = cv2.TrackerKCF_create()
if tracker_type == 'TLD':
tracker = cv2.TrackerTLD_create()
if tracker_type == 'MEDIANFLOW':
tracker = cv2.TrackerMedianFlow_create()
if tracker_type == 'GOTURN':
tracker = cv2.TrackerGOTURN_create()
if tracker_type == 'MOSSE':
tracker = cv2.TrackerMOSSE_create()
if tracker_type == "CSRT":
tracker = cv2.TrackerCSRT_create()
x0 = 200
y0 = 200
w0 = 224
h0 = 224
track_window = (x0, y0, w0, h0)
# Reference Distance
L0 = 100
S0 = 50176 #224x224 #take#here.
# Base Distance
LB = 100
# Define an initial bounding box
bbox = (x0, y0, w0, h0) #(287, 23, 86, 320)
#CX=int(bbox[0]+0.5*bbox[2]+3) #adding
#CY=int(bbox[1]+0.5*bbox[3]+3) #adding
drone = tellopy.Tello()
try:
drone.connect()
drone.wait_for_connection(60.0)
retry = 3
container = None
while container is None and 0 < retry:
retry -= 1
try:
container = av.open(drone.get_video_stream())
except av.AVError as ave:
print(ave)
print('retry...')
drone.takeoff()
# skip first 300 frames
frame_skip = 300
while True:
#------------------------------------------for start
for frame in container.decode(video=0):
speed = 100
if 0 < frame_skip:
frame_skip = frame_skip - 1
continue
start_time = time.time()
image = cv2.cvtColor(numpy.array(frame.to_image()),
cv2.COLOR_RGB2BGR)
# Start timer
timer = cv2.getTickCount()
#cv2.imshow('Canny', cv2.Canny(image, 100, 200))
#cv2.waitKey(1)
# Update tracker
ok, bbox = tracker.update(image)
# Calculate Frames per second (FPS)
fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer)
# Draw bounding box
if ok:
#print('Tracking ok')
(x, y, w, h) = (int(bbox[0]), int(bbox[1]), int(bbox[2]),
int(bbox[3]))
CX = int(bbox[0] + 0.5 * bbox[2]) #Center of X
CY = int(bbox[1] + 0.5 * bbox[3])
S0 = bbox[2] * bbox[3]
print("CX,CY,S0,x,y=", CX, CY, S0, x, y)
# Tracking success
p1 = (x, y)
p2 = (x + w, y + h)
cv2.rectangle(image, p1, p2, (255, 0, 0), 2, 1)
p10 = (x0, y0)
p20 = (x0 + w0, y0 + h0)
cv2.rectangle(image, p10, p20, (0, 255, 0), 2, 1)
d = round(L0 * m.sqrt(S0 / (w * h)))
dx = x + w / 2 - CX0 #no change dx
dy = y + h / 2 - CY0
print(d, dx, dy)
tracking(drone, d, dx, dy, LB)
else:
# Tracking failure
#print('Tracking failure')
cv2.putText(image, "Tracking failure detected", (100, 80),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
cv2.imshow('Original', image)
key = cv2.waitKey(1) & 0xff
if key == ord('q'):
print('Q!')
break
if key == ord('r'):
roi_time = time.time()
bbox = cv2.selectROI(image, False)
print(bbox)
(x0, y0, w0, h0) = (int(bbox[0]), int(bbox[1]),
int(bbox[2]), int(bbox[3]))
CX0 = int(x0 + 0.5 * w0) #Center of X
CY0 = int(y0 + 0.5 * h0)
# Initialize tracker with first frame and bounding box
ok = tracker.init(image, bbox)
'''
if frame.time_base < 1.0/60:
time_base = 1.0/60
else:
time_base = frame.time_base
frame_skip2 = int((time.time() - roi_time)/time_base)
if 0 < frame_skip2:
frame_skip2 = frame_skip2 - 1
continue
'''
if frame.time_base < 1.0 / 60:
time_base = 1.0 / 60
else:
time_base = frame.time_base
frame_skip = int((time.time() - start_time) / time_base)
#-------------------------------------------------for end
break
print('stop fly')
except Exception as ex:
exc_type, exc_value, exc_traceback = sys.exc_info()
traceback.print_exception(exc_type, exc_value, exc_traceback)
print(ex)
finally:
drone.quit()
drone.land()
cv2.destroyAllWindows()
def goturnTracker(video):
tracker = cv2.TrackerGOTURN_create()
excute(video, tracker)
def __init__(self):
self.tracker = cv2.TrackerGOTURN_create()
self.init_flag = False # False, the tracker was not initialized.
self.check_model()
def __init__(self):
self.tracker = cv2.TrackerGOTURN_create()
def test_createTracker(self):
t = cv.TrackerMIL_create()
try:
t = cv.TrackerGOTURN_create()
except cv.error as e:
pass # may fail due to missing DL model files
def detect_moving_target(old_gray, new_gray):
# open the camera for warming up
cap = cv2.VideoCapture('slow.MOV')
# grab the first frame
old_frame = get_frame_from(cap)
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
# initiate KCF tracker
tracker = cv2.TrackerKCF_create()
# possible movements ROI
# start the tracking
while True:
# Record FPS
timer = cv2.getTickCount()
# read the current frame
cur_frame = get_frame_from(cap)
# covert to gray scale
cur_gray = cv2.cvtColor(cur_frame, cv2.COLOR_BGR2GRAY)
# Calculate Frames per second (FPS)
fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer)
# Display FPS on frame
cv2.putText(cur_frame, "FPS : " + str(int(fps)), (100, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2);
# display cur frame
cv2.imshow('video', cur_frame)
# waitkey
if cv2.waitKey(1) == ord('q'):
break
#(major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')
if __name__ == '__main__':
# Set up tracker.
# Instead of MIL, you can also use
tracker_types = ['BOOSTING', 'MIL', 'KCF', 'TLD', 'MEDIANFLOW', 'GOTURN']
tracker_type = tracker_types[2]
if int(minor_ver) < 3:
tracker = cv2.Tracker_create(tracker_type)
else:
if tracker_type == 'BOOSTING':
tracker = cv2.TrackerBoosting_create()
if tracker_type == 'MIL':
tracker = cv2.TrackerMIL_create()
if tracker_type == 'KCF':
tracker = cv2.TrackerKCF_create()
if tracker_type == 'TLD':
tracker = cv2.TrackerTLD_create()
if tracker_type == 'MEDIANFLOW':
tracker = cv2.TrackerMedianFlow_create()
if tracker_type == 'GOTURN':
tracker = cv2.TrackerGOTURN_create()
# Read video
video = cv2.VideoCapture('slow.MOV')
# Exit if video not opened.
if not video.isOpened():
print
"Could not open video"
sys.exit()
# Read first frame.
ok, frame = video.read()
frame = cv2.resize(frame, dsize=(0, 0), fx=0.5, fy=0.5)
if not ok:
print
'Cannot read video file'
sys.exit()
# Define an initial bounding box
bbox = (287, 23, 86, 320)
# Uncomment the line below to select a different bounding box
bbox = cv2.selectROI(frame, False)
# Initialize tracker with first frame and bounding box
ok = tracker.init(frame, bbox)
while True:
# Read a new frame
ok, frame = video.read()
frame = cv2.resize(frame, dsize=(0, 0), fx=0.5, fy=0.5)
if not ok:
break
# Start timer
timer = cv2.getTickCount()
# Update tracker
ok, bbox = tracker.update(frame)
# Calculate Frames per second (FPS)
fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer)
# Draw bounding box
if ok:
# Tracking success
p1 = (int(bbox[0]), int(bbox[1]))
p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
cv2.rectangle(frame, p1, p2, (255, 0, 0), 2, 1)
else:
# Tracking failure
cv2.putText(frame, "Tracking failure detected", (100, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
# Display tracker type on frame
cv2.putText(frame, tracker_type + " Tracker", (100, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2);
# Display FPS on frame
cv2.putText(frame, "FPS : " + str(int(fps)), (100, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2);
# Display result
cv2.imshow("Tracking", frame)
# Exit if ESC pressed
k = cv2.waitKey(1) & 0xff
if k == 27: break
# if the class label is not a person, ignore it
if CLASSES[idx] != "person":
continue
# compute the (x, y)-coordinates of the bounding box
box = detections[0, 0, i, 3:7] * np.array([W, H, W, H])
(startX, startY, endX, endY) = box.astype("int")
# save detection as image
# print(startX, startY, endX, endY)
roi = frame[startY:endY, startX:endX]
cv2.imwrite("roi" + str(numberOfDetection) + ".jpg", roi)
numberOfDetection += 1
# inicialize GOTURN tracker
goturn = cv2.TrackerGOTURN_create()
goturn.init(frame, (startX, startY, endX, endY))
# add the tracker to list of trackers
trackers.append(goturn)
# otherwise, run tracking algorithm
else:
# loop over the trackers
for tracker in trackers:
# set the status
status = "Tracking"
# update the tracker and grab the updated position
(success, box) = goturn.update(frame)
def main():
"""
LoadsMulti-Level Otsu masks and tracks the face.
Returns a set of ROIs containing only the face.
:return:
"""
tag = 'MLO_'
filepath = 'E:\\GitHub\\CovPySourceFile\\MultiLevelOtsu\\'
otsu_masks = load_images_from_folder(
folder=filepath,
name_tag=tag,
)
# region MIL Tracking
# use binary otsu mask to detect the face
(major_ver, minor_ver, subminor_ver) = cv2.__version__.split('.')
# Set up tracker
tracker_types = [
'BOOSTING', 'MIL', 'KCF', 'TLD', 'MEDIANFLOW', 'GOTURN', 'MOSSE',
'CSRT'
]
tracker_type = tracker_types[4]
if int(minor_ver) < 3:
tracker = cv2.Tracker_create(tracker_type)
else:
if tracker_type == 'BOOSTING':
tracker = cv2.TrackerBoosting_create()
if tracker_type == 'MIL':
tracker = cv2.TrackerMIL_create()
if tracker_type == 'KCF':
tracker = cv2.TrackerKCF_create()
if tracker_type == 'TLD':
tracker = cv2.TrackerTLD_create()
if tracker_type == 'MEDIANFLOW':
tracker = cv2.TrackerMedianFlow_create()
if tracker_type == 'GOTURN':
tracker = cv2.TrackerGOTURN_create()
if tracker_type == 'MOSSE':
tracker = cv2.TrackerMOSSE_create()
if tracker_type == "CSRT":
tracker = cv2.TrackerCSRT_create()
initial_frame = otsu_masks[0]
# Define initial bounding box from roi
bbox = cv2.selectROI(initial_frame, showCrosshair=True, fromCenter=False)
# Initialize tracker with first frame and bounding box
ok = tracker.init(initial_frame, bbox)
# roi points
points = []
failed_idx = []
for n, mask in enumerate(otsu_masks):
# Update tracker
ok, bbox = tracker.update(mask)
# Draw bounding box
if ok:
# Tracking success
p1 = [int(bbox[0]), int(bbox[1])]
p2 = [int(bbox[0] + bbox[2]), int(bbox[1])]
p3 = [int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3])]
p4 = [int(bbox[0]), int(bbox[1] + bbox[3])]
points.append([p1, p2, p3, p4])
cv2.rectangle(mask, tuple(p1), tuple(p3), (255, 0, 0), 2, 1)
else:
# Tracking failure
cv2.putText(mask, "Tracking failure detected", (100, 80),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
failed_idx.append(n)
# Display result
cv2.imshow("Tracking", mask)
# Exit if ESC pressed
k = cv2.waitKey(1) & 0xff
if k == 27:
break
cv2.destroyAllWindows()
# endregion
# get rois
rois = []
for n, rp in enumerate(points):
img = Image.open(
r"E:\GitHub\CovPySourceFile\Normalized\NF_{}.png".format(n))
left = rp[0][0]
top = rp[0][1]
right = rp[2][0]
bottom = rp[2][1]
cropped = img.crop((left, top, right, bottom))
rois.append(cropped)
# plt.imshow(cropped)
# plt.show()
destination_dir = 'E:\\GitHub\\CovPySourceFile\\FaceROI\\'
if not os.path.exists(destination_dir):
os.makedirs(destination_dir)
for n, r in enumerate(rois):
r.save(destination_dir + 'FR_{}.png'.format(n))
def main():
dataset, timestamps = load_thermal_file(
_filename='ThermalData_18_06_2020_13_19_36.h5',
_folder='E:\\GitHub\\CovPySourceFile')
# region Control Variables
is_writing = False
is_drawing = False
# endregion
# region Data Pre-Processing
# region Timestamps to Sampling Rate
# # convert timestamps into datetime objects
# dt_obj = [datetime.fromtimestamp(ts / 1000).time() for ts in timestamps]
# # convert datetime objects into time strings
# time_strings = [dt.strftime("%M:%S:%f") for dt in dt_obj]
# # finally convert time strings into seconds
# timestamp_in_seconds = []
# for s in time_strings:
# date_time = datetime.strptime(s, "%M:%S:%f")
# a_timedelta = date_time - datetime(1900, 1, 1)
# in_seconds = a_timedelta.total_seconds()
# timestamp_in_seconds.append(in_seconds)
#
# # calculate the mean interval between samples from seconds
# ts_mean = np.mean(np.diff(timestamp_in_seconds))
# # finally calculate the mean sampling rate of the signal
# fs = int(1 / ts_mean)
# endregion
# region Get Raw Thermal Data
# get data set attributes
n_frames, height, width, total_time_ms = [
dataset.attrs[i] for i in list(dataset.attrs)
]
# extract thermal frames from the hdf5 dataset
thermal_frames = []
# convert raw data into temperature values [deg Celsius]
# temp_frames = []
# normalize raw data for further processing steps [0 - 255]
norm_frames = []
for n in range(0, n_frames):
raw_frame = load_frame_from_dataset(dataset, height, n)
thermal_frames.append(raw_frame)
# temp_frames.append(raw_frame * 0.1 - 273.15)
norm_frames.append(
cv2.normalize(raw_frame,
None,
alpha=0,
beta=255,
norm_type=cv2.NORM_MINMAX,
dtype=cv2.CV_8U))
# get unsharpened img for edge detection later on
unsharp_frames = []
# for n, n_frame in enumerate(norm_frames):
# u_frame = unsharp_mask(image=n_frame, radius=3, amount=2)
# unsharp_frames.append(u_frame)
#
# if is_writing:
# cv2.imwrite('E:\\GitHub\\CovPySourceFile\\UnsharpenedMask\\UM_{}.png'.format(n), u_frame)
#
# if is_drawing:
# fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 3.5))
#
# # Plotting the original image.
# ax[0].imshow(norm_frames[n])
# ax[0].set_title('Thermal Data - Normalized')
#
# # ax[1].imshow(temp_frames[n])
# # ax[1].set_title('Temp Frame [C]')
#
# ax[1].imshow(unsharp_frames[n])
# ax[1].set_title('Unsharpened Image')
#
# # ax[1].imshow(norm_frames[n])
# # ax[1].set_title('Thermal Data - Normalized [0-255]')
#
# plt.subplots_adjust()
# plt.show()
#
# if is_drawing:
# plt.close('all')
# endregion
# endregion
# region Feature Extraction Algorithm
# region Automatic ROI Detection
# face segmentation using multi-level Otsu
otsu_masks = multi_level_otsu(images=norm_frames,
n_regions=4,
target_region=3,
method=OtsuMethods.BINARY,
write=is_writing,
draw=is_drawing)
# to proceed the masks need to be converted into 3d array
empty_array = np.zeros((height, width))
_3d_otsu_masks = [
np.dstack((mask, empty_array, empty_array)) for mask in otsu_masks
]
# use binary otsu mask to detect the face
(major_ver, minor_ver, subminor_ver) = cv2.__version__.split('.')
# Set up tracker
tracker_types = [
'BOOSTING', 'MIL', 'KCF', 'TLD', 'MEDIANFLOW', 'GOTURN', 'MOSSE',
'CSRT'
]
tracker_type = tracker_types[4]
if int(minor_ver) < 3:
tracker = cv2.Tracker_create(tracker_type)
else:
if tracker_type == 'BOOSTING':
tracker = cv2.TrackerBoosting_create()
if tracker_type == 'MIL':
tracker = cv2.TrackerMIL_create()
if tracker_type == 'KCF':
tracker = cv2.TrackerKCF_create()
if tracker_type == 'TLD':
tracker = cv2.TrackerTLD_create()
if tracker_type == 'MEDIANFLOW':
tracker = cv2.TrackerMedianFlow_create()
if tracker_type == 'GOTURN':
tracker = cv2.TrackerGOTURN_create()
if tracker_type == 'MOSSE':
tracker = cv2.TrackerMOSSE_create()
if tracker_type == "CSRT":
tracker = cv2.TrackerCSRT_create()
# video = cv2.VideoCapture('E:\\GitHub\\CovPySourceFile\\Video\\OtsuMask.avi')
#
# # Exit if video not opened.
# if not video.isOpened():
# print("Could not open video file!")
# sys.exit()
#
# # Read first frame
# ok, frame = video.read()
# if not ok:
# print("Could not read video file!")
# sys.exit()
tracked_frame = _3d_otsu_masks[0]
# Define initial bounding box from roi
bbox = cv2.selectROI(tracked_frame, showCrosshair=True, fromCenter=False)
# Initialize tracker with first frame and bounding box
ok = tracker.init(tracked_frame, bbox)
# roi points
roi_points = []
tracked_frames = []
# while True:
# # Read a new frame
# ok, frame = video.read()
# if not ok:
# break
for mask in _3d_otsu_masks:
tracked_frame = mask
# Start timer
timer = cv2.getTickCount()
# Update tracker
ok, bbox = tracker.update(tracked_frame)
# Calculate Frames per second (FPS)
fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer)
# Draw bounding box
if ok:
# Tracking success
p1 = (int(bbox[0]), int(bbox[1]))
p2 = (int(bbox[0] + bbox[2])), int(bbox[1])
p3 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
p4 = (int(bbox[0]), int(bbox[1] + bbox[3]))
cv2.rectangle(tracked_frame, p1, p3, (255, 0, 0), 2, 1)
points = [p1, p2, p3, p4]
# roi_values = get_values_from_roi(points, t_frame)
roi_points.append(points)
else:
# Tracking failure
cv2.putText(tracked_frame, "Tracking failure detected", (100, 80),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
roi_points.append([])
# Display tracker type on frame
cv2.putText(tracked_frame, tracker_type + " Tracker", (100, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)
# Display FPS on frame
cv2.putText(tracked_frame, "FPS : " + str(int(fps)), (100, 50),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)
tracked_frames.append(tracked_frame)
# Display result
cv2.imshow("Tracking", tracked_frame)
# Exit if ESC pressed
k = cv2.waitKey(1) & 0xff
if k == 27:
break
is_writing = True
if is_writing:
for n, img in enumerate(tracked_frames):
cv2.imwrite(
'E:\\GitHub\\CovPySourceFile\\TrackedFrames\\TF_{}.png'.format(
n), img)
norm_face_rois = []
for n in range(0, len(roi_points)):
# get values inside of roi
norm_roi_values = get_values_from_roi(roi_points[n], norm_frames[n])
# my_roi = np.zeros((roi_shapes[n][2], roi_shapes[n][3]))
x1 = roi_points[n][0][0]
x2 = roi_points[n][2][0]
y1 = roi_points[n][0][1]
y2 = roi_points[n][2][1]
norm_face_roi = norm_roi_values[y1:y2, x1:x2]
if is_drawing:
cv2.imshow("ROI", norm_face_roi)
# Exit if ESC pressed
k = cv2.waitKey(1) & 0xff
if k == 27:
break
norm_face_rois.append(norm_face_roi)
if is_writing:
for n, img in enumerate(tracked_frames):
cv2.imwrite(
'E:\\GitHub\\CovPySourceFile\\FaceROI\\TF_{}.png'.format(n),
img)
# endregion
# endregion
print('Bye Bye')
def run_concurrent(self, args, sign_progress):
annotation_id = args[0]
bbox = tuple(args[1])
movie_path = args[2]
fps = args[5]
start_frame = ms_to_frames(args[3], fps)
end_frame = ms_to_frames(args[4], fps)
method = args[6]
resolution = args[7]
keys = []
# TRACKING
if method == 'BOOSTING':
tracker = cv2.TrackerBoosting_create()
elif method == 'MIL':
tracker = cv2.TrackerMIL_create()
elif method == 'KCF':
tracker = cv2.TrackerKCF_create()
elif method == 'TLD':
tracker = cv2.TrackerTLD_create()
elif method == 'MEDIANFLOW':
tracker = cv2.TrackerMedianFlow_create()
elif method == 'GOTURN':
tracker = cv2.TrackerGOTURN_create()
else:
raise Exception("Tracking Method not identifiable. " + str(method))
# Read video
capture = cv2.VideoCapture(movie_path)
# Exit if video not opened.
if not capture.isOpened():
raise RuntimeError("Tracking: Could not open video.")
# Read first frame.
capture.set(cv2.CAP_PROP_POS_FRAMES, start_frame)
ok, frame = capture.read()
if not ok:
raise RuntimeError("Tracking: Could not read Frame.")
# Initialize tracker with first frame and bounding box
ok = tracker.init(frame, bbox)
for i in range(start_frame, end_frame, 1):
sign_progress(
round(float(i - start_frame) / (end_frame - start_frame), 2))
# Read a new frame
ok, frame = capture.read()
if not ok:
break
# Update tracker
ok, bbox = tracker.update(frame)
# Draw bounding box
if ok:
# Tracking success
if i % resolution == 0:
time = frame2ms(i, fps)
pos = [bbox[0], bbox[1]]
keys.append([time, pos])
p1 = (int(bbox[0]), int(bbox[1]))
p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
cv2.rectangle(frame, p1, p2, (255, 0, 0), 2, 1)
# cv2.imshow("Returned", frame)
# cv2.waitKey(30)
return [annotation_id, keys]
def single_tracking(filepath):
# Set up tracker.
# Instead of MIL, you can also use
if tracker_type == 'BOOSTING':
tracker = cv2.TrackerBoosting_create()
if tracker_type == 'MIL':
tracker = cv2.TrackerMIL_create()
if tracker_type == 'KCF':
tracker = cv2.TrackerKCF_create()
if tracker_type == 'TLD':
tracker = cv2.TrackerTLD_create()
if tracker_type == 'MEDIANFLOW':
tracker = cv2.TrackerMedianFlow_create()
if tracker_type == 'GOTURN':
tracker = cv2.TrackerGOTURN_create()
if tracker_type == 'MOSSE':
tracker = cv2.TrackerMOSSE_create()
if tracker_type == 'CSRT':
tracker = cv2.TrackerCSRT_create()
# Read video
video = cv2.VideoCapture(filepath)
# Exit if video not opened.
if not video.isOpened():
print("Could not open video")
sys.exit()
# Read first frame.
ok, frame = video.read()
if not ok:
print('Cannot read video file')
sys.exit()
# Define an initial bounding box
# bbox = (287, 23, 86, 320)
# Uncomment the line below to select a different bounding box
bbox = cv2.selectROI('tracking', frame)
# Initialize tracker with first frame and bounding box
t1 = time.clock()
ok = tracker.init(frame, bbox)
t = time.clock() - t1
print('cost time: ', t)
while True:
# Read a new frame
ok, frame = video.read()
if not ok:
break
# Start timer
timer = cv2.getTickCount()
# Update tracker
ok, bbox = tracker.update(frame)
# Calculate Frames per second (FPS)
fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer)
# Draw bounding box
if ok:
# Tracking success
p1 = (int(bbox[0]), int(bbox[1]))
p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
cv2.rectangle(frame, p1, p2, (255, 0, 0), 2, 1)
else:
# Tracking failure
cv2.putText(frame, "Tracking failure detected", (100, 80),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
# Display tracker type on frame
cv2.putText(frame, tracker_type + " Tracker", (100, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)
# Display FPS on frame
cv2.putText(frame, "FPS : " + str(int(fps)), (100, 50),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)
# Display result
cv2.imshow("Tracking", frame)
# Exit if ESC pressed
k = cv2.waitKey(1) & 0xff
if k == 27:
break
def track_ecoli(regions):
tracker_types = ['BOOSTING', 'MIL', 'KCF', 'TLD', 'MEDIANFLOW', 'GOTURN']
# MIL appears to work best...
tracker_type = tracker_types[1]
if int(minor_ver) < 3:
tracker = cv2.Tracker_create(tracker_type)
else:
if tracker_type == 'BOOSTING':
tracker = cv2.TrackerBoosting_create()
if tracker_type == 'MIL':
tracker = cv2.TrackerMIL_create()
if tracker_type == 'KCF':
tracker = cv2.TrackerKCF_create()
if tracker_type == 'TLD':
tracker = cv2.TrackerTLD_create()
if tracker_type == 'MEDIANFLOW':
tracker = cv2.TrackerMedianFlow_create()
if tracker_type == 'GOTURN':
tracker = cv2.TrackerGOTURN_create()
root_path = 'C:\\dev\\courses\\2.131 - Advanced Instrumentation\\'
img_path = root_path + 'E.coli.tif'
pil_img = Image.open(img_path)
frame = np.array(pil_img)
# frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
fourcc = cv2.VideoWriter_fourcc(*'DIVX')
# out = cv2.VideoWriter(root_path + 'ecoli_track1.avi',
# fourcc, 5.0,(640,480) )
# Define an initial bounding box
bbox = (287, 23, 86, 320)
# Uncomment the line below to select a different bounding box
bbox = cv2.selectROI(frame, False)
# Initialize tracker with first frame and bounding box
ok = tracker.init(frame, bbox)
csvfile = open(root_path + 'E.coli1.csv', 'w', newline='')
bac_writer = csv.writer(csvfile,
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
bac_writer.writerow(['BacteriaId', 'Time', 'X', 'Y'])
for i in range(149):
# Read a new frame
pil_img.seek(i)
frame = np.array(pil_img)
# Start timer
timer = cv2.getTickCount()
# Update tracker
ok, bbox = tracker.update(frame)
# Calculate Frames per second (FPS)
fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer)
# Draw bounding box
if ok:
# Tracking success
p1 = (int(bbox[0]), int(bbox[1]))
p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
cv2.rectangle(frame, p1, p2, (255, 0, 0), 2, 1)
bac_writer.writerow(
[1, i,
int(bbox[0] + bbox[2] / 2),
int(bbox[1] + bbox[3] / 2)])
else:
# Tracking failure
cv2.putText(frame, "Tracking failure detected", (100, 80),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
# Display tracker type on frame
cv2.putText(frame, "2.131 E.Coli Tracker", (100, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)
# Display FPS on frame
cv2.putText(frame, "FPS : " + str(int(fps)), (100, 50),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)
frame = frame[:480, :640, ...]
array_alpha = np.array([1.45])
array_beta = np.array([-150.0])
cv2.add(frame, array_beta, frame)
# multiply every pixel value by alpha
cv2.multiply(frame, array_alpha, frame)
# out.write(frame)
# out.write(frame)
# Display result
cv2.imshow("Tracking", frame)
# Exit if ESC pressed
k = cv2.waitKey(1) & 0xff
if k == 27: break
csvfile.close()
def main():
tracker_types = ['BOOSTING', 'MIL', 'KCF', 'TLD', 'MEDIANFLOW', 'GOTURN', 'MOSSE', 'CSRT']
tracker_type = tracker_types[2]
if int(minor_ver) < 3:
tracker = cv2.Tracker_create(tracker_type)
else:
if tracker_type == 'BOOSTING':
tracker = cv2.TrackerBoosting_create()
if tracker_type == 'MIL':
tracker = cv2.TrackerMIL_create()
if tracker_type == 'KCF':
tracker = cv2.TrackerKCF_create()
if tracker_type == 'TLD':
tracker = cv2.TrackerTLD_create()
if tracker_type == 'MEDIANFLOW':
tracker = cv2.TrackerMedianFlow_create()
if tracker_type == 'GOTURN':
tracker = cv2.TrackerGOTURN_create()
if tracker_type == 'MOSSE':
tracker = cv2.TrackerMOSSE_create()
if tracker_type == "CSRT":
tracker = cv2.TrackerCSRT_create()
# 追跡する枠の座標とサイズ
x = 200
y = 200
w = 224
h = 224
track_window=(x,y,w,h)
# Reference Distance
L0 = 100
#S0 = 50176 #224x224
# Base Distance
LB = 100
# Define an initial bounding box
bbox = (x, y, w, h) #(287, 23, 86, 320)
drone = tellopy.Tello()
drone.connect()
container = av.open(drone.get_video_stream())
drone.takeoff()
#drone.is_autopilot="True"
drone.is_autopilot="False"
while True:
for frame in container.decode(video=0):
image = cv2.cvtColor(numpy.array(frame.to_image()), cv2.COLOR_RGB2BGR)
# Start timer
timer = cv2.getTickCount()
# Update tracker
ok, bbox = tracker.update(image)
# Calculate Frames per second (FPS)
fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer);
# Draw bounding box
if ok:
(x,y,w,h) = (int(bbox[0]),int(bbox[1]),int(bbox[2]),int(bbox[3]))
CX=int(bbox[0]+0.5*bbox[2])
CY=int(bbox[1]+0.5*bbox[3])
S0=bbox[2]*bbox[3]
print("CX,CY,S0=",CX,CY,S0)
# Tracking success
p1 = (x, y)
p2 = (x + w, y + h)
cv2.rectangle(image, p1, p2, (255,0,0), 2, 1)
else :
# Tracking failure
cv2.putText(image, "Tracking failure detected", (100,80), cv2.FONT_HERSHEY_SIMPLEX, 0.75,(0,0,255),2)
# Display tracker type on frame
cv2.putText(image, tracker_type + " Tracker", (100,20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50),2)
# Display FPS on frame
cv2.putText(image, "FPS : " + str(int(fps)), (100,50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50), 2)
cv2.imshow('test',image)
key = cv2.waitKey(1)&0xff
if key == ord('a'):
drone.is_autopilot="True"
elif key == ord('s'):
drone.is_autopilot="False"
if drone.is_autopilot=="True":
d = round(L0 * m.sqrt(S0 / (w * h)))
dx = x + w/2 - CX
dy = y + h/2 - CY
print(d,dx,dy,drone.is_autopilot,w,h)
tracking(drone,d,dx,dy,LB)
elif drone.is_autopilot=="False":
key_Operation(drone,key)
print("key=",key,ord('q'))
#key = cv2.waitKey(1)&0xff
print("key=",key,ord('q'))
if key == ord('q'):
cv2.destroyAllWindows()
break
elif key == ord('r'):
bbox = cv2.selectROI(image, False)
print(bbox)
(x,y,w,h) = (int(bbox[0]),int(bbox[1]),int(bbox[2]),int(bbox[3]))
# Initialize tracker with first frame and bounding box
ok = tracker.init(image, bbox)
break
drone.down(50)
sleep(5)
drone.land()
drone.subscribe(drone.EVENT_FLIGHT_DATA, handler)
drone.quit()
def tracking_face(vi_path="try.avi", t_type=7):
# Set up tracker.
# Instead of MIL, you can also use
(major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')
tracker_types = [
'BOOSTING', 'MIL', 'KCF', 'TLD', 'MEDIANFLOW', 'GOTURN', 'MOSSE',
'CSRT'
]
tracker_type = tracker_types[t_type]
# if int(minor_ver) < 3:
if int(major_ver) < 3:
tracker = cv2.Tracker_create(tracker_type)
else:
if tracker_type == 'BOOSTING':
tracker = cv2.TrackerBoosting_create()
if tracker_type == 'MIL':
tracker = cv2.TrackerMIL_create()
if tracker_type == 'KCF':
tracker = cv2.TrackerKCF_create()
if tracker_type == 'TLD':
tracker = cv2.TrackerTLD_create()
if tracker_type == 'MEDIANFLOW':
tracker = cv2.TrackerMedianFlow_create()
if tracker_type == 'GOTURN':
tracker = cv2.TrackerGOTURN_create()
if tracker_type == 'MOSSE':
tracker = cv2.TrackerMOSSE_create()
if tracker_type == "CSRT":
tracker = cv2.TrackerCSRT_create()
# Read video
video = cv2.VideoCapture(vi_path)
# Exit if video not opened.
if not video.isOpened():
print("Could not open video")
sys.exit()
# Read first frame.
ok, frame = video.read()
if not ok:
print('Cannot read video file')
sys.exit()
# Define an initial bounding box
height, width, layers = frame.shape
# box
# bbox_transfer()
bbox, score = bbox_score(frame)
bbox = np.squeeze(bbox)[0]
score = np.squeeze(score)[0]
while True:
if score > 0.7:
break
ok, frame = video.read()
bbox, score = bbox_score(frame)
bbox = np.squeeze(bbox)[0]
score = np.squeeze(score)[0]
bbox = bbox_transfer(bbox, height, width)
# bbox = (bbox[1]*width,bbox[0]*height,bbox[2]-bbox[0],bbox[3]-bbox[1])
# bbox = tuple(bbox)
# bbox = (265, 48, 70, 82)# x,y, w, h
# bbox = (48,151,128,186) # *H ymin, xmin, ymax, xmax
# bbox = (85,270,228,332) # *W
# bbox = (85,151,228,186) A - 151 85 (228-85) 186-151
# bbox = (48,270,128,332) B
# print(bbox.shape,score.shape)
# Uncomment the line below to select a different bounding box
# bbox = cv2.selectROI(frame, False)
# Initialize tracker with first frame and bounding box
ok = tracker.init(frame, bbox)
# bbox = bbox_transfer(bbox)
while True:
# Read a new frame
ok, frame = video.read(
) # I think this line should be put after tracker.update
if not ok:
break
# Start timer
timer = cv2.getTickCount()
# Update tracker
ok, bbox = tracker.update(frame)
# Calculate Frames per second (FPS)
fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer)
# Draw bounding box
if ok:
# Tracking success
p1 = (int(bbox[0]), int(bbox[1]))
p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
cv2.rectangle(frame, p1, p2, (255, 0, 0), 2, 1)
else:
# Tracking failure
cv2.putText(frame, "Tracking failure detected", (100, 80),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
# Display tracker type on frame
cv2.putText(frame, tracker_type + " Tracker", (100, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)
# Display FPS on frame
cv2.putText(frame, "FPS : " + str(int(fps)), (100, 50),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)
# Display result
cv2.imshow("Tracking", frame)
# Exit if ESC pressed
k = cv2.waitKey(1) & 0xff
if k == 27: break
