计算汽车OpenCV + Python问题
Tes*_*awy 36 python opencv numpy image-processing
我一直试图在越线时计算汽车并且它可以工作,但问题是它多次计算一辆车是荒谬的,因为它应该被计算一次
这是我正在使用的代码:
import cv2
import numpy as np
bgsMOG = cv2.BackgroundSubtractorMOG()
cap = cv2.VideoCapture("traffic.avi")
counter = 0
if cap:
while True:
ret, frame = cap.read()
if ret:
fgmask = bgsMOG.apply(frame, None, 0.01)
cv2.line(frame,(0,60),(160,60),(255,255,0),1)
# To find the countours of the Cars
contours, hierarchy = cv2.findContours(fgmask,
cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
try:
hierarchy = hierarchy[0]
except:
hierarchy = []
for contour, hier in zip(contours, hierarchy):
(x, y, w, h) = cv2.boundingRect(contour)
if w > 20 and h > 20:
cv2.rectangle(frame, (x,y), (x+w,y+h), (255, 0, 0), 1)
#To find centroid of the Car
x1 = w/2
y1 = h/2
cx = x+x1
cy = y+y1
## print "cy=", cy
## print "cx=", cx
centroid = (cx,cy)
## print "centoid=", centroid
# Draw the circle of Centroid
cv2.circle(frame,(int(cx),int(cy)),2,(0,0,255),-1)
# To make sure the Car crosses the line
## dy = cy-108
## print "dy", dy
if centroid > (27, 38) and centroid < (134, 108):
## if (cx <= 132)and(cx >= 20):
counter +=1
## print "counter=", counter
## if cy > 10 and cy < 160:
cv2.putText(frame, str(counter), (x,y-5),
cv2.FONT_HERSHEY_SIMPLEX,
0.5, (255, 0, 255), 2)
## cv2.namedWindow('Output',cv2.cv.CV_WINDOW_NORMAL)
cv2.imshow('Output', frame)
## cv2.imshow('FGMASK', fgmask)
key = cv2.waitKey(60)
if key == 27:
break
cap.release()
cv2.destroyAllWindows()
视频在我的github页面上@ https://github.com/Tes3awy/MatLab-Tutorials 名为traffic.avi,它也是Matlab库中的内置视频
每辆车一次计算的帮助吗？
编辑:视频的各个帧看起来如下:
Dan*_*šek 95
制备
为了理解正在发生的事情,并最终解决我们的问题,我们首先需要稍微改进一下脚本.
我已经添加了算法重要步骤的记录,稍微重构了代码,添加了掩码和处理过的图像的保存,添加了使用单个帧图像运行脚本的功能,以及其他一些修改.
这就是脚本在这一点上的样子:
import logging
import logging.handlers
import os
import time
import sys
import cv2
import numpy as np
from vehicle_counter import VehicleCounter
# ============================================================================
IMAGE_DIR = "images"
IMAGE_FILENAME_FORMAT = IMAGE_DIR + "/frame_%04d.png"
# Support either video file or individual frames
CAPTURE_FROM_VIDEO = False
if CAPTURE_FROM_VIDEO:
IMAGE_SOURCE = "traffic.avi" # Video file
else:
IMAGE_SOURCE = IMAGE_FILENAME_FORMAT # Image sequence
# Time to wait between frames, 0=forever
WAIT_TIME = 1 # 250 # ms
LOG_TO_FILE = True
# Colours for drawing on processed frames
DIVIDER_COLOUR = (255, 255, 0)
BOUNDING_BOX_COLOUR = (255, 0, 0)
CENTROID_COLOUR = (0, 0, 255)
# ============================================================================
def init_logging():
main_logger = logging.getLogger()
formatter = logging.Formatter(
fmt='%(asctime)s.%(msecs)03d %(levelname)-8s [%(name)s] %(message)s'
, datefmt='%Y-%m-%d %H:%M:%S')
handler_stream = logging.StreamHandler(sys.stdout)
handler_stream.setFormatter(formatter)
main_logger.addHandler(handler_stream)
if LOG_TO_FILE:
handler_file = logging.handlers.RotatingFileHandler("debug.log"
, maxBytes = 2**24
, backupCount = 10)
handler_file.setFormatter(formatter)
main_logger.addHandler(handler_file)
main_logger.setLevel(logging.DEBUG)
return main_logger
# ============================================================================
def save_frame(file_name_format, frame_number, frame, label_format):
file_name = file_name_format % frame_number
label = label_format % frame_number
log.debug("Saving %s as '%s'", label, file_name)
cv2.imwrite(file_name, frame)
# ============================================================================
def get_centroid(x, y, w, h):
x1 = int(w / 2)
y1 = int(h / 2)
cx = x + x1
cy = y + y1
return (cx, cy)
# ============================================================================
def detect_vehicles(fg_mask):
log = logging.getLogger("detect_vehicles")
MIN_CONTOUR_WIDTH = 21
MIN_CONTOUR_HEIGHT = 21
# Find the contours of any vehicles in the image
contours, hierarchy = cv2.findContours(fg_mask
, cv2.RETR_EXTERNAL
, cv2.CHAIN_APPROX_SIMPLE)
log.debug("Found %d vehicle contours.", len(contours))
matches = []
for (i, contour) in enumerate(contours):
(x, y, w, h) = cv2.boundingRect(contour)
contour_valid = (w >= MIN_CONTOUR_WIDTH) and (h >= MIN_CONTOUR_HEIGHT)
log.debug("Contour #%d: pos=(x=%d, y=%d) size=(w=%d, h=%d) valid=%s"
, i, x, y, w, h, contour_valid)
if not contour_valid:
continue
centroid = get_centroid(x, y, w, h)
matches.append(((x, y, w, h), centroid))
return matches
# ============================================================================
def filter_mask(fg_mask):
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
# Fill any small holes
closing = cv2.morphologyEx(fg_mask, cv2.MORPH_CLOSE, kernel)
# Remove noise
opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel)
# Dilate to merge adjacent blobs
dilation = cv2.dilate(opening, kernel, iterations = 2)
return dilation
# ============================================================================
def process_frame(frame_number, frame, bg_subtractor, car_counter):
log = logging.getLogger("process_frame")
# Create a copy of source frame to draw into
processed = frame.copy()
# Draw dividing line -- we count cars as they cross this line.
cv2.line(processed, (0, car_counter.divider), (frame.shape[1], car_counter.divider), DIVIDER_COLOUR, 1)
# Remove the background
fg_mask = bg_subtractor.apply(frame, None, 0.01)
fg_mask = filter_mask(fg_mask)
save_frame(IMAGE_DIR + "/mask_%04d.png"
, frame_number, fg_mask, "foreground mask for frame #%d")
matches = detect_vehicles(fg_mask)
log.debug("Found %d valid vehicle contours.", len(matches))
for (i, match) in enumerate(matches):
contour, centroid = match
log.debug("Valid vehicle contour #%d: centroid=%s, bounding_box=%s", i, centroid, contour)
x, y, w, h = contour
# Mark the bounding box and the centroid on the processed frame
# NB: Fixed the off-by one in the bottom right corner
cv2.rectangle(processed, (x, y), (x + w - 1, y + h - 1), BOUNDING_BOX_COLOUR, 1)
cv2.circle(processed, centroid, 2, CENTROID_COLOUR, -1)
log.debug("Updating vehicle count...")
car_counter.update_count(matches, processed)
return processed
# ============================================================================
def main():
log = logging.getLogger("main")
log.debug("Creating background subtractor...")
bg_subtractor = cv2.BackgroundSubtractorMOG()
log.debug("Pre-training the background subtractor...")
default_bg = cv2.imread(IMAGE_FILENAME_FORMAT % 119)
bg_subtractor.apply(default_bg, None, 1.0)
car_counter = None # Will be created after first frame is captured
# Set up image source
log.debug("Initializing video capture device #%s...", IMAGE_SOURCE)
cap = cv2.VideoCapture(IMAGE_SOURCE)
frame_width = cap.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)
frame_height = cap.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)
log.debug("Video capture frame size=(w=%d, h=%d)", frame_width, frame_height)
log.debug("Starting capture loop...")
frame_number = -1
while True:
frame_number += 1
log.debug("Capturing frame #%d...", frame_number)
ret, frame = cap.read()
if not ret:
log.error("Frame capture failed, stopping...")
break
log.debug("Got frame #%d: shape=%s", frame_number, frame.shape)
if car_counter is None:
# We do this here, so that we can initialize with actual frame size
log.debug("Creating vehicle counter...")
car_counter = VehicleCounter(frame.shape[:2], frame.shape[0] / 2)
# Archive raw frames from video to disk for later inspection/testing
if CAPTURE_FROM_VIDEO:
save_frame(IMAGE_FILENAME_FORMAT
, frame_number, frame, "source frame #%d")
log.debug("Processing frame #%d...", frame_number)
processed = process_frame(frame_number, frame, bg_subtractor, car_counter)
save_frame(IMAGE_DIR + "/processed_%04d.png"
, frame_number, processed, "processed frame #%d")
cv2.imshow('Source Image', frame)
cv2.imshow('Processed Image', processed)
log.debug("Frame #%d processed.", frame_number)
c = cv2.waitKey(WAIT_TIME)
if c == 27:
log.debug("ESC detected, stopping...")
break
log.debug("Closing video capture device...")
cap.release()
cv2.destroyAllWindows()
log.debug("Done.")
# ============================================================================
if __name__ == "__main__":
log = init_logging()
if not os.path.exists(IMAGE_DIR):
log.debug("Creating image directory `%s`...", IMAGE_DIR)
os.makedirs(IMAGE_DIR)
main()
该脚本负责处理图像流,并识别每个帧中的所有车辆 - 我matches在代码中引用它们.
计算检测到的车辆的任务被委托给班级VehicleCounter.随着我们的进步,我之所以选择将其作为一个班级,将会变得明显.我没有实施你的车辆计数算法,因为当我们深入研究这个算法时,它将不再有效.
文件vehicle_counter.py包含以下代码:
import logging
# ============================================================================
class VehicleCounter(object):
def __init__(self, shape, divider):
self.log = logging.getLogger("vehicle_counter")
self.height, self.width = shape
self.divider = divider
self.vehicle_count = 0
def update_count(self, matches, output_image = None):
self.log.debug("Updating count using %d matches...", len(matches))
# ============================================================================
最后,我编写了一个脚本,将所有生成的图像拼接在一起,因此更容易检查它们:
import cv2
import numpy as np
# ============================================================================
INPUT_WIDTH = 160
INPUT_HEIGHT = 120
OUTPUT_TILE_WIDTH = 10
OUTPUT_TILE_HEIGHT = 12
TILE_COUNT = OUTPUT_TILE_WIDTH * OUTPUT_TILE_HEIGHT
# ============================================================================
def stitch_images(input_format, output_filename):
output_shape = (INPUT_HEIGHT * OUTPUT_TILE_HEIGHT
, INPUT_WIDTH * OUTPUT_TILE_WIDTH
, 3)
output = np.zeros(output_shape, np.uint8)
for i in range(TILE_COUNT):
img = cv2.imread(input_format % i)
cv2.rectangle(img, (0, 0), (INPUT_WIDTH - 1, INPUT_HEIGHT - 1), (0, 0, 255), 1)
# Draw the frame number
cv2.putText(img, str(i), (2, 10)
, cv2.FONT_HERSHEY_PLAIN, 0.7, (255, 255, 255), 1)
x = i % OUTPUT_TILE_WIDTH * INPUT_WIDTH
y = i / OUTPUT_TILE_WIDTH * INPUT_HEIGHT
output[y:y+INPUT_HEIGHT, x:x+INPUT_WIDTH,:] = img
cv2.imwrite(output_filename, output)
# ============================================================================
stitch_images("images/frame_%04d.png", "stitched_frames.png")
stitch_images("images/mask_%04d.png", "stitched_masks.png")
stitch_images("images/processed_%04d.png", "stitched_processed.png")
分析
为了解决这个问题,我们应该对我们期望获得的结果有所了解.我们还应该在视频中标记所有不同的汽车,因此更容易谈论它们.
如果我们运行我们的脚本并将图像拼接在一起,我们会得到许多有用的文件来帮助我们分析问题:
- 运行的调试日志.
在检查这些问题后,一些问题变得明显:
- 前景蒙版往往很吵.我们应该做一些过滤(侵蚀/扩张？)来摆脱噪音和缩小差距.
- 有时我们会错过车辆(灰色车辆).
- 一些车辆在单帧中被检测到两次.
- 在车架的上部区域很少检测到车辆.
- 通常在连续帧中检测相同的车辆.我们需要找出一种在连续帧中跟踪同一车辆的方法,并且只计算一次.
解
1.预播种背景减法器
我们的视频很短,只有120帧.随着学习率的增加0.01,背景探测器将需要大部分视频才能稳定下来.
幸运的是,视频的最后一帧(帧号119)完全没有车辆,因此我们可以将它用作我们的初始背景图像.(在注释和评论中提到了获得合适图像的其他选项.)
要使用这个初始背景图像,我们只需加载它,并apply在背景减法器上加载学习因子1.0:
bg_subtractor = cv2.BackgroundSubtractorMOG()
default_bg = cv2.imread(IMAGE_FILENAME_FORMAT % 119)
bg_subtractor.apply(default_bg, None, 1.0)
当我们看到新的面具镶嵌时,我们可以看到我们获得的噪音更少,车辆检测在早期帧中效果更好.
2.清理前景面膜
A simple approach to improve our foreground mask is to apply a few morphological transformations.
def filter_mask(fg_mask):
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
# Fill any small holes
closing = cv2.morphologyEx(fg_mask, cv2.MORPH_CLOSE, kernel)
# Remove noise
opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel)
# Dilate to merge adjacent blobs
dilation = cv2.dilate(opening, kernel, iterations = 2)
return dilation
Inspecting the masks, processed frames and the log file generated with filtering, we can see that we now detect vehicles more reliably, and have mitigated the issue of different parts of one vehicle being detected as separate objects.
3. Tracking Vehicles Between Frames
At this point, we need to go through our log file, and collect all the centroid coordinates for each vehicle. This will allow us to plot and inspect the path each vehicle traces across the image, and develop an algorithm to do this automatically. To make this process easier, we can create a reduced log by grepping out the relevant entries.
The lists of centroid coordinates:
traces = {
'A': [(112, 36), (112, 45), (112, 52), (112, 54), (112, 63), (111, 73), (111, 86), (111, 91), (111, 97), (110, 105)]
, 'B': [(119, 37), (120, 42), (121, 54), (121, 55), (123, 64), (124, 74), (125, 87), (127, 94), (125, 100), (126, 108)]
, 'C': [(93, 23), (91, 27), (89, 31), (87, 36), (85, 42), (82, 49), (79, 59), (74, 71), (70, 82), (62, 86), (61, 92), (55, 101)]
, 'D': [(118, 30), (124, 83), (125, 90), (116, 101), (122, 100)]
, 'E': [(77, 27), (75, 30), (73, 33), (70, 37), (67, 42), (63, 47), (59, 53), (55, 59), (49, 67), (43, 75), (36, 85), (27, 92), (24, 97), (20, 102)]
, 'F': [(119, 30), (120, 34), (120, 39), (122, 59), (123, 60), (124, 70), (125, 82), (127, 91), (126, 97), (128, 104)]
, 'G': [(88, 37), (87, 41), (85, 48), (82, 55), (79, 63), (76, 74), (72, 87), (67, 92), (65, 98), (60, 106)]
, 'H': [(124, 35), (123, 40), (125, 45), (127, 59), (126, 59), (128, 67), (130, 78), (132, 88), (134, 93), (135, 99), (135, 107)]
, 'I': [(98, 26), (97, 30), (96, 34), (94, 40), (92, 47), (90, 55), (87, 64), (84, 77), (79, 87), (74, 93), (73, 102)]
, 'J': [(123, 60), (125, 63), (125, 81), (127, 93), (126, 98), (125, 100)]
}
Individual vehicle traces plotted on the background:
Combined enlarged image of all the vehicle traces:
Vectors
In order to analyze the movement, we need to work with vectors (i.e. the distance and direction moved). The following diagram shows how the angles correspond to movement of vehicles in the image.
We can use the following function to calculate the vector between two points:
def get_vector(a, b):
"""Calculate vector (distance, angle in degrees) from point a to point b.
Angle ranges from -180 to 180 degrees.
Vector with angle 0 points straight down on the image.
Values increase in clockwise direction.
"""
dx = float(b[0] - a[0])
dy = float(b[1] - a[1])
distance = math.sqrt(dx**2 + dy**2)
if dy > 0:
angle = math.degrees(math.atan(-dx/dy))
elif dy == 0:
if dx < 0:
angle = 90.0
elif dx > 0:
angle = -90.0
else:
angle = 0.0
else:
if dx < 0:
angle = 180 - math.degrees(math.atan(dx/dy))
elif dx > 0:
angle = -180 - math.degrees(math.atan(dx/dy))
else:
angle = 180.0
return distance, angle
Categorization
One way we can look for patterns that could be used to categorize the movements as valid/invalid is to make a scatter plot (angle vs. distance):
- Green points represent valid movement, that we determined using the lists of points for each vehicle.
- Red points represent invalid movement - vectors between points in adjacent traffic lanes.
- I plotted two blue curves, which we can use to separate the two types of movements. Any point that lies below either curve can be considered as valid. The curves are:
distance = -0.008 * angle**2 + 0.4 * angle + 25.0distance = 10.0
We can use the following function to categorize the movement vectors:
def is_valid_vector(a):
distance, angle = a
threshold_distance = max(10.0, -0.008 * angle**2 + 0.4 * angle + 25.0)
return (distance <= threshold_distance)
NB: There is one outlier, which is occurs due to our loosing track of vehicle D in frames 43..48.
Algorithm
We will use class Vehicle to store information about each tracked vehicle:
- Some kind of identifier
- List of positions, most recent at front
- Last-seen counter -- number of frames since we've last seen this vehicle
- Flag to mark whether the vehicle was counted or not
Class VehicleCounter will store a list of currently tracked vehicles and keep track of the total count. On each frame, we will use the list of bounding boxes and positions of identified vehicles (candidate list) to update the state of VehicleCounter:
- Update currently tracked
Vehicles:- For each vehicle
- If there is any valid match for given vehicle, update vehicle position and reset its last-seen counter. Remove the match from the candidate list.
- Otherwise, increase the last-seen counter for that vehicle.
- For each vehicle
- Create new
Vehicles for any remaining matches - Update vehicle count
- For each vehicle
- If the vehicle is past divider and has not been counted yet, update the total count and mark the vehicle as counted
- For each vehicle
- Remove vehicles that are no longer visible
- For each vehicle
- If the last-seen counter exceeds threshold, remove the vehicle
- For each vehicle
4. Solution
We can reuse the main script with the final version of vehicle_counter.py, containing the implementation of our counting algorithm:
import logging
import math
import cv2
import numpy as np
# ============================================================================
CAR_COLOURS = [ (0,0,255), (0,106,255), (0,216,255), (0,255,182), (0,255,76)
, (144,255,0), (255,255,0), (255,148,0), (255,0,178), (220,0,255) ]
# ============================================================================
class Vehicle(object):
def __init__(self, id, position):
self.id = id
self.positions = [position]
self.frames_since_seen = 0
self.counted = False
@property
def last_position(self):
return self.positions[-1]
def add_position(self, new_position):
self.positions.append(new_position)
self.frames_since_seen = 0
def draw(self, output_image):
car_colour = CAR_COLOURS[self.id % len(CAR_COLOURS)]
for point in self.positions:
cv2.circle(output_image, point, 2, car_colour, -1)
cv2.polylines(output_image, [np.int32(self.positions)]
, False, car_colour, 1)
# ============================================================================
class VehicleCounter(object):
def __init__(self, shape, divider):
self.log = logging.getLogger("vehicle_counter")
self.height, self.width = shape
self.divider = divider
self.vehicles = []
self.next_vehicle_id = 0
self.vehicle_count = 0
self.max_unseen_frames = 7
@staticmethod
def get_vector(a, b):
"""Calculate vector (distance, angle in degrees) from point a to point b.
Angle ranges from -180 to 180 degrees.
Vector with angle 0 points straight down on the image.
Values increase in clockwise direction.
"""
dx = float(b[0] - a[0])
dy = float(b[1] - a[1])
distance = math.sqrt(dx**2 + dy**2)
if dy > 0:
angle = math.degrees(math.atan(-dx/dy))
elif dy == 0:
if dx < 0:
angle = 90.0
elif dx > 0:
angle = -90.0
else:
angle = 0.0
else:
if dx < 0:
angle = 180 - math.degrees(math.atan(dx/dy))
elif dx > 0:
angle = -180 - math.degrees(math.atan(dx/dy))
else:
angle = 180.0
return distance, angle
@staticmethod
def is_valid_vector(a):
distance, angle = a
threshold_distance = max(10.0, -0.008 * angle**2 + 0.4 * angle + 25.0)
return (distance <= threshold_distance)
def update_vehicle(self, vehicle, matches):
# Find if any of the matches fits this vehicle
for i, match in enumerate(matches):
contour, centroid = match
vector = self.get_vector(vehicle.last_position, centroid)
if self.is_valid_vector(vector):
vehicle.add_position(centroid)
self.log.debug("Added match (%d, %d) to vehicle #%d. vector=(%0.2f,%0.2f)"
, centroid[0], centroid[1], vehicle.id, vector[0], vector[1])
return i
# No matches fit...
vehicle.frames_since_seen += 1
self.log.debug("No match for vehicle #%d. frames_since_seen=%d"
, vehicle.id, vehicle.frames_since_seen)
return None
def update_count(self, matches, output_image = None):
self.log.debug("Updating count using %d matches...", len(matches))
# First update all the existing vehicles
for vehicle in self.vehicles:
i = self.update_vehicle(vehicle, matches)
if i is not None:
del matches[i]
# Add new vehicles based on the remaining matches
for match in matches:
contour, centroid = match
new_vehicle = Vehicle(self.next_vehicle_id, centroid)
self.next_vehicle_id += 1
self.vehicles.append(new_vehicle)
self.log.debug("Created new vehicle #%d from match (%d, %d)."
, new_vehicle.id, centroid[0], centroid[1])
# Count any uncounted vehicles that are past the divider
for vehicle in self.vehicles:
if not vehicle.counted and (vehicle.last_position[1] > self.divider):
self.vehicle_count += 1
vehicle.counted = True
self.log.debug("Counted vehicle #%d (total count=%d)."
, vehicle.id, self.vehicle_count)
# Optionally draw the vehicles on an image
if output_image is not None:
for vehicle in self.vehicles:
vehicle.draw(output_image)
cv2.putText(output_image, ("%02d" % self.vehicle_count), (142, 10)
, cv2.FONT_HERSHEY_PLAIN, 0.7, (127, 255, 255), 1)
# Remove vehicles that have not been seen long enough
removed = [ v.id for v in self.vehicles
if v.frames_since_seen >= self.max_unseen_frames ]
self.vehicles[:] = [ v for v in self.vehicles
if not v.frames_since_seen >= self.max_unseen_frames ]
for id in removed:
self.log.debug("Removed vehicle #%d.", id)
self.log.debug("Count updated, tracking %d vehicles.", len(self.vehicles))
# ============================================================================
The program now draws the historical paths of all currently tracked vehicles into the output image, along with the vehicle count. Each vehicle is assigned 1 of 10 colours.
Notice that vehicle D ends up being tracked twice, however it is counted only once, since we lose track of it before crossing the divider. Ideas on how to resolve this are mentioned in the appendix.
Based on the last processed frame generated by the script
the total vehicle count is 10. This is a correct result.
More details can be found in the output the script generated:
- Full debug log
- Filtered out vehicle counter log
- A mosaic of the processed frames:
A. Potential Improvements
- Refactor, add unit tests.
- Improve filtering/preprocessing of the foreground mask
- Multiple iterations of filtering, fill holes using
cv2.drawContourswithCV_FILLED? - Watershed Algorithm?
- Multiple iterations of filtering, fill holes using
- Improve categorization of movement vectors
- Create a predictor to estimate initial movement angle when vehicles are created (and only one position is known)... in order to be able to
- Use change in direction rather than direction alone (I think this would cluster the angles of valid motion vectors close to zero).
- Improve vehicle tracking
- Predict position for frames where vehicle is not seen.
B. Notes
- 似乎不可能直接从
BackgroundSubtractorMOGPython中提取当前的背景图像(至少在OpenCV 2.4.x中),但有一种方法可以通过一些工作来完成. - 正如Henrik所建议的那样,我们可以使用中值混合获得对背景的良好估计.
- @DanMašek非常感谢超级骗子的伟大伟大的伟大工作 (2认同)
- 神奇的答案！研究透彻，雄辩地写作。我发现您的回答对我的项目非常有帮助！非常感谢 (2认同)