本文整理汇总了Python中cv2.createBackgroundSubtractorMOG2方法的典型用法代码示例。如果您正苦于以下问题:Python cv2.createBackgroundSubtractorMOG2方法的具体用法?Python cv2.createBackgroundSubtractorMOG2怎么用?Python cv2.createBackgroundSubtractorMOG2使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类cv2
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在下文中一共展示了cv2.createBackgroundSubtractorMOG2方法的12个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: create_background
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import createBackgroundSubtractorMOG2 [as 别名]
def create_background(video_frames):
# type: (np.ndarray) -> np.ndarray
"""
Create the background of a video via MOGs.
:param video_frames: list of ordered frames (i.e., a video).
:return: the estimated background of the video.
"""
mog = cv2.createBackgroundSubtractorMOG2()
for frame in video_frames:
img = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
mog.apply(img)
# Get background
background = mog.getBackgroundImage()
return cv2.cvtColor(background, cv2.COLOR_BGR2RGB)
示例2: __init__
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import createBackgroundSubtractorMOG2 [as 别名]
def __init__(self, vid_file): # vid_file = 'videos/traffic.avi'
self.cnt_up = 0
self.cnt_down = 0
self.zone1 = (100, 200)
self.zone2 = (450, 100)
self.cap = cv2.VideoCapture(vid_file) # insane
# Capture the properties of VideoCapture to console
# for i in range(19):
# print(i, self.cap.get(i))
self.w = self.cap.get(3)
self.h = self.cap.get(4)
self.frameArea = self.h * self.w
self.areaTH = self.frameArea / 200
print('Area Threshold', self.areaTH)
# Input/Output Lines
self.line_up = int(2 * (self.h / 5))
self.line_down = int(3 * (self.h / 5))
self.up_limit = int(1 * (self.h / 5))
self.down_limit = int(4 * (self.h / 5))
self.line_down_color = (255, 0, 0)
self.line_up_color = (0, 0, 255)
self.pt1 = [0, self.line_down]
self.pt2 = [self.w, self.line_down]
self.pts_L1 = np.array([self.pt1, self.pt2], np.int32)
self.pts_L1 = self.pts_L1.reshape((-1, 1, 2))
self.pt3 = [0, self.line_up]
self.pt4 = [self.w, self.line_up]
self.pts_L2 = np.array([self.pt3, self.pt4], np.int32)
self.pts_L2 = self.pts_L2.reshape((-1, 1, 2))
self.pt5 = [0, self.up_limit]
self.pt6 = [self.w, self.up_limit]
self.pts_L3 = np.array([self.pt5, self.pt6], np.int32)
self.pts_L3 = self.pts_L3.reshape((-1, 1, 2))
self.pt7 = [0, self.down_limit]
self.pt8 = [self.w, self.down_limit]
self.pts_L4 = np.array([self.pt7, self.pt8], np.int32)
self.pts_L4 = self.pts_L4.reshape((-1, 1, 2))
# Create the background subtractor
self.fgbg = cv2.createBackgroundSubtractorMOG2()
self.kernelOp = np.ones((3, 3), np.uint8)
self.kernelOp2 = np.ones((5, 5), np.uint8)
self.kernelCl = np.ones((11, 11), np.uint8)
# Variables
self.font = cv2.FONT_HERSHEY_SIMPLEX
self.vehicles = []
self.max_p_age = 5
self.pid = 1
示例3: __init__
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import createBackgroundSubtractorMOG2 [as 别名]
def __init__(self):
"""Initialize variables used by Detectors class
Args:
None
Return:
None
"""
self.fgbg = cv2.createBackgroundSubtractorMOG2()
示例4: __init__
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import createBackgroundSubtractorMOG2 [as 别名]
def __init__(self, min_accuracy, min_blend_area, kernel_fill=20, dist_threshold=15000, history=400):
self.min_accuracy = max (min_accuracy, 0.7)
self.min_blend_area = min_blend_area
self.kernel_clean = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(4,4))
self.kernel_fill = np.ones((kernel_fill,kernel_fill),np.uint8)
self.dist_threshold = dist_threshold
self.history = history
# read https://docs.opencv.org/3.3.0/d2/d55/group__bgsegm.html#gae561c9701970d0e6b35ec12bae149814
try:
self.fgbg = cv2.bgsegm.createBackgroundSubtractorMOG(history=self.history, nmixtures=5, backgroundRatio=0.7, noiseSigma=0)
except AttributeError as error:
print ('It looks like your OpenCV version does not include bgsegm. Switching to createBackgroundSubtractorMOG2')
self.fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows=False, history=self.history)
#self.fgbg = cv2.bgsegm.createBackgroundSubtractorGMG(decisionThreshold=0.98, initializationFrames=10)
#self.fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows=False, history=self.history)
#self.fgbg=cv2.bgsegm.createBackgroundSubtractorGSOC(noiseRemovalThresholdFacBG=0.01, noiseRemovalThresholdFacFG=0.0001)
#self.fgbg=cv2.bgsegm.createBackgroundSubtractorCNT(minPixelStability = 5, useHistory = True, maxPixelStability = 5 *60,isParallel = True)
#self.fgbg=cv2.createBackgroundSubtractorKNN(detectShadows=False, history=self.history, dist2Threshold = self.dist_threshold)
#fgbg=cv2.bgsegm.createBackgroundSubtractorLSBP()
utils.success_print('Background subtraction initialized')
示例5: detected_frame
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import createBackgroundSubtractorMOG2 [as 别名]
def detected_frame(self, preprocessed_faced_covered_input_frame):
"""Function for removing background from input frame. """
if self.flag_handler.background_capture_required is True:
self._bg_model = cv2.createBackgroundSubtractorMOG2(0, self._bg_Sub_Threshold)
self.flag_handler.background_capture_required = False
if self._bg_model is not None:
fgmask = self._bg_model.apply(preprocessed_faced_covered_input_frame, learningRate=self._learning_Rate)
kernel = np.ones((3, 3), np.uint8)
fgmask = cv2.erode(fgmask, kernel, iterations=1)
res = cv2.bitwise_and(preprocessed_faced_covered_input_frame, preprocessed_faced_covered_input_frame,
mask=fgmask)
self._input_frame_with_hand = res[
0:int(
self._cap_region_y_end * preprocessed_faced_covered_input_frame.shape[0]),
int(self._cap_region_x_begin * preprocessed_faced_covered_input_frame.shape[
1]):
preprocessed_faced_covered_input_frame.shape[
1]] # clip the ROI
示例6: test_contour_extreme_point_tracking
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import createBackgroundSubtractorMOG2 [as 别名]
def test_contour_extreme_point_tracking(self):
"""Test for tracking extreme_points without optical flow (e.g until calibrated). """
# setup
test_path = utils.get_full_path('docs/material_for_testing/back_ground_removed_frame.jpg')
test_image = cv2.imread(test_path)
# todo: use mockito here to mock preprocessing elements
flags_handler = FlagsHandler()
detector = Detector(flags_handler)
extractor = Extractor(flags_handler)
# Background model preparations.
bg_model = cv2.createBackgroundSubtractorMOG2(0, 50)
cap = cv2.VideoCapture(0)
while flags_handler.quit_flag is False:
ret, frame = cap.read()
frame = cv2.flip(frame, 1)
# Remove background from input frame.
fgmask = bg_model.apply(frame, learningRate=0)
kernel = np.ones((3, 3), np.uint8)
fgmask = cv2.erode(fgmask, kernel, iterations=1)
res = cv2.bitwise_and(frame, frame, mask=fgmask)
# Clip frames ROI.
back_ground_removed_clipped = ImageTestTool.clip_roi(res,
{'cap_region_x_begin': 0.6, 'cap_region_y_end': 0.6})
if flags_handler.background_capture_required is True:
bg_model = cv2.createBackgroundSubtractorMOG2(0, 50)
flags_handler.background_capture_required = False
detector.input_frame_for_feature_extraction = back_ground_removed_clipped
extractor.extract = detector
image = extractor.get_drawn_extreme_contour_points()
cv2.imshow('test_contour_extreme_point_tracking', image)
flags_handler.keyboard_input = cv2.waitKey(1)
示例7: test_max_distance_between_top_ext_point_and_palm_center_point
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import createBackgroundSubtractorMOG2 [as 别名]
def test_max_distance_between_top_ext_point_and_palm_center_point(self):
"""Test if max distance is found correctly. """
# setup
# todo: use mockito here to mock preprocessing elements
flags_handler = FlagsHandler()
detector = Detector(flags_handler)
extractor = Extractor(flags_handler)
# Background model preparations.
bg_model = cv2.createBackgroundSubtractorMOG2(0, 50)
cap = cv2.VideoCapture(0)
while flags_handler.quit_flag is False:
ret, frame = cap.read()
frame = cv2.flip(frame, 1)
# Remove background from input frame.
fgmask = bg_model.apply(frame, learningRate=0)
kernel = np.ones((3, 3), np.uint8)
fgmask = cv2.erode(fgmask, kernel, iterations=1)
res = cv2.bitwise_and(frame, frame, mask=fgmask)
# Clip frames ROI.
back_ground_removed_clipped = ImageTestTool.clip_roi(res,
{'cap_region_x_begin': 0.6, 'cap_region_y_end': 0.6})
if flags_handler.background_capture_required is True:
bg_model = cv2.createBackgroundSubtractorMOG2(0, 50)
flags_handler.background_capture_required = False
detector.input_frame_for_feature_extraction = back_ground_removed_clipped
extractor.extract = detector
# run
image = extractor.get_drawn_extreme_contour_points()
cv2.line(image, extractor.palm_center_point, (extractor.ext_top[0], extractor.palm_center_point[
1] - extractor.max_distance_from_ext_top_point_to_palm_center), (255, 255, 255), thickness=2)
cv2.imshow('test_max_distance_between_top_ext_point_and_palm_center_point', image)
flags_handler.keyboard_input = cv2.waitKey(1)
示例8: test_palm_angle_calculation
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import createBackgroundSubtractorMOG2 [as 别名]
def test_palm_angle_calculation(self):
"""Test if angle is calculated correctly.
Usage:
1. press 'b': to calibrate back_ground_remover.
2. insert hand into frame, so that middle_finger is aligned with the Y axe.
3. rotate hand 15 degrees left. (degrees should go above 90).
4. rotate hand 15 degrees right. (degrees should go below 90).
5. press esc when done.
"""
# setup
# todo: use mockito here to mock preprocessing elements
flags_handler = FlagsHandler()
detector = Detector(flags_handler)
extractor = Extractor(flags_handler)
# Background model preparations.
bg_model = cv2.createBackgroundSubtractorMOG2(0, 50)
cap = cv2.VideoCapture(0)
while flags_handler.quit_flag is False:
ret, frame = cap.read()
frame = cv2.flip(frame, 1)
# Remove background from input frame.
fgmask = bg_model.apply(frame, learningRate=0)
kernel = np.ones((3, 3), np.uint8)
fgmask = cv2.erode(fgmask, kernel, iterations=1)
res = cv2.bitwise_and(frame, frame, mask=fgmask)
# Clip frames ROI.
back_ground_removed_clipped = ImageTestTool.clip_roi(res,
{'cap_region_x_begin': 0.6, 'cap_region_y_end': 0.6})
if flags_handler.background_capture_required is True:
bg_model = cv2.createBackgroundSubtractorMOG2(0, 50)
flags_handler.background_capture_required = False
detector.input_frame_for_feature_extraction = back_ground_removed_clipped
extractor.extract = detector
# run
image = extractor.get_drawn_extreme_contour_points()
cv2.imshow('test_contour_extreme_point_tracking', image)
print(extractor.palm_angle_in_degrees)
flags_handler.keyboard_input = cv2.waitKey(1)
示例9: test_5_second_calibration_time
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import createBackgroundSubtractorMOG2 [as 别名]
def test_5_second_calibration_time(self):
"""Test if 5 second calibration time works correctly according to flags_handler.
Usage:
1. press 'b': to calibrate back_ground_remover.
2. insert hand into frame, center palms_center (white dot) with axes crossing.
3. wait for #calibration_time (default 5 sec).
4. press esc
test: after calibration_time, center circle should be green.
"""
# setup
# todo: use mockito here to mock preprocessing elements
flags_handler = FlagsHandler()
detector = Detector(flags_handler)
extractor = Extractor(flags_handler)
# Background model preparations.
bg_model = cv2.createBackgroundSubtractorMOG2(0, 50)
cap = cv2.VideoCapture(0)
while flags_handler.quit_flag is False:
ret, frame = cap.read()
frame = cv2.flip(frame, 1)
# Remove background from input frame.
fgmask = bg_model.apply(frame, learningRate=0)
kernel = np.ones((3, 3), np.uint8)
fgmask = cv2.erode(fgmask, kernel, iterations=1)
res = cv2.bitwise_and(frame, frame, mask=fgmask)
# Clip frames ROI.
back_ground_removed_clipped = ImageTestTool.clip_roi(res,
{'cap_region_x_begin': 0.6, 'cap_region_y_end': 0.6})
if flags_handler.background_capture_required is True:
bg_model = cv2.createBackgroundSubtractorMOG2(0, 50)
flags_handler.background_capture_required = False
detector.input_frame_for_feature_extraction = back_ground_removed_clipped
extractor.extract = detector
# run
image = extractor.get_drawn_extreme_contour_points()
cv2.imshow('test_contour_extreme_point_tracking', image)
flags_handler.keyboard_input = cv2.waitKey(1)
示例10: test_detector_extract_and_track
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import createBackgroundSubtractorMOG2 [as 别名]
def test_detector_extract_and_track(self):
"""Test if Detector uses tracker object correctly. """
# setup
# Input from camera.
cv2.namedWindow('test_detector_extract_and_track')
cap = cv2.VideoCapture(0)
flags_handler = FlagsHandler()
detector = Detector(flags_handler)
extractor = Extractor(flags_handler)
bg_model = cv2.createBackgroundSubtractorMOG2(0, 50)
while flags_handler.quit_flag is False:
"""
Inside loop, update self._threshold according to flags_handler,
Pressing 'c': in order to toggle control (suppose to change contour's color between green and red)
Pressing 'l': to raise 'land' flag in flags_handler, in order to be able to break loop (with esc)
Pressing 'z': will make threshold thinner.
Pressing 'x': will make threshold thicker.
Pressing esc: break loop.
"""
ret, frame = cap.read()
frame = cv2.flip(frame, 1)
# Remove background from input frame.
fgmask = bg_model.apply(frame, learningRate=0)
kernel = np.ones((3, 3), np.uint8)
fgmask = cv2.erode(fgmask, kernel, iterations=1)
res = cv2.bitwise_and(frame, frame, mask=fgmask)
# Clip frames ROI.b
roi = {'cap_region_x_begin': 0.6, 'cap_region_y_end': 0.6}
back_ground_removed_clipped = ImageTestTool.clip_roi(res, roi)
if flags_handler.background_capture_required is True:
bg_model = cv2.createBackgroundSubtractorMOG2(0, 50)
flags_handler.background_capture_required = False
# Pipe:
detector.input_frame_for_feature_extraction = back_ground_removed_clipped
extractor.extract = detector
cv2.imshow('test_detector_extract_and_track', extractor.get_drawn_extreme_contour_points())
keyboard_input = cv2.waitKey(1)
flags_handler.keyboard_input = keyboard_input
# teardown
cap.release()
cv2.destroyAllWindows()
示例11: test_track
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import createBackgroundSubtractorMOG2 [as 别名]
def test_track(self):
"""Test if tracker object tracks correctly after given set of points to track, and a frame."""
# setup
cv2.namedWindow('test_track')
flags_handler = FlagsHandler()
tracker = None
bg_model = cv2.createBackgroundSubtractorMOG2(0, 50)
cap = cv2.VideoCapture(0)
while flags_handler.quit_flag is False:
ret, frame = cap.read()
frame = cv2.flip(frame, 1)
# Remove background from input frame.
fgmask = bg_model.apply(frame, learningRate=0)
kernel = np.ones((3, 3), np.uint8)
fgmask = cv2.erode(fgmask, kernel, iterations=1)
res = cv2.bitwise_and(frame, frame, mask=fgmask)
# Clip frames ROI.
back_ground_removed_clipped = ImageTestTool.clip_roi(res,
{'cap_region_x_begin': 0.6, 'cap_region_y_end': 0.6})
if flags_handler.background_capture_required is True:
bg_model = cv2.createBackgroundSubtractorMOG2(0, 50)
flags_handler.background_capture_required = False
max_area_contour = ImageTestTool.get_max_area_contour(back_ground_removed_clipped)
extLeft, extRight, extTop, extBot = ImageTestTool.get_contour_extreme_points(max_area_contour)
palm_center = ImageTestTool.get_center_of_mass(max_area_contour)
if tracker is None:
points = np.array([extTop, palm_center])
else:
points = tracker.points_to_track
tracker.track(points, back_ground_removed_clipped)
points = tracker.points_to_track
ImageTestTool.draw_tracking_points(back_ground_removed_clipped, points)
cv2.circle(back_ground_removed_clipped, palm_center, 8, (255, 255, 255), thickness=-1)
cv2.imshow('test_track', back_ground_removed_clipped)
keyboard_input = cv2.waitKey(1)
flags_handler.keyboard_input = keyboard_input
# run
if flags_handler.background_capture_required is True:
tracker = None
if keyboard_input == ord('t'):
tracker = Tracker(flags_handler, points, back_ground_removed_clipped)
# teardown
cap.release()
cv2.destroyAllWindows()
示例12: background_subtraction
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import createBackgroundSubtractorMOG2 [as 别名]
def background_subtraction(background_image, foreground_image):
"""Creates a binary image from a background subtraction of the foreground using cv2.BackgroundSubtractorMOG().
The binary image returned is a mask that should contain mostly foreground pixels.
The background image should be the same background as the foreground image except not containing the object
of interest.
Images must be of the same size and type.
If not, larger image will be taken and downsampled to smaller image size.
If they are of different types, an error will occur.
Inputs:
background_image = img object, RGB or binary/grayscale/single-channel
foreground_image = img object, RGB or binary/grayscale/single-channel
Returns:
fgmask = background subtracted foreground image (mask)
:param background_image: numpy.ndarray
:param foreground_image: numpy.ndarray
:return fgmask: numpy.ndarray
"""
params.device += 1
# Copying images to make sure not alter originals
bg_img = np.copy(background_image)
fg_img = np.copy(foreground_image)
# Checking if images need to be resized or error raised
if bg_img.shape != fg_img.shape:
# If both images are not 3 channel or single channel then raise error.
if len(bg_img.shape) != len(fg_img.shape):
fatal_error("Images must both be single-channel/grayscale/binary or RGB")
# Forcibly resizing largest image to smallest image
print("WARNING: Images are not of same size.\nResizing")
if bg_img.shape > fg_img.shape:
width, height = fg_img.shape[1], fg_img.shape[0]
bg_img = cv2.resize(bg_img, (width, height), interpolation=cv2.INTER_AREA)
else:
width, height = bg_img.shape[1], bg_img.shape[0]
fg_img = cv2.resize(fg_img, (width, height), interpolation=cv2.INTER_AREA)
bgsub = cv2.createBackgroundSubtractorMOG2()
# Applying the background image to the background subtractor first.
# Anything added after is subtracted from the previous iterations.
_ = bgsub.apply(bg_img)
# Applying the foreground image to the background subtractor (therefore removing the background)
fgmask = bgsub.apply(fg_img)
# Debug options
if params.debug == "print":
print_image(fgmask, os.path.join(params.debug_outdir, str(params.device) + "_background_subtraction.png"))
elif params.debug == "plot":
plot_image(fgmask, cmap="gray")
return fgmask