def back_projection_demo(target):
sample = target[430:442, 305:317, :3] # 建筑墙面色调块
roi_hsv = cv.cvtColor(sample, cv.COLOR_BGR2HSV)
target_hsv = cv.cvtColor(target, cv.COLOR_BGR2HSV)
cv.imshow("sample", sample)
cv.imshow("target", target)
roihist = cv.calcHist([roi_hsv], [0, 1], None, [8, 8],
[0, 180, 0, 256]) # [180, 256] binsize小则查找粗糙
cv.normalize(roihist, roihist, 0, 255, cv.NORM_MINMAX) # 归一化
dst = cv.calcBackProject([target_hsv], [0, 1], roihist, [0, 180, 0, 256],
1)
cv.imshow("backproject", dst)
def hist_extract(image, handHist):
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
dst = cv2.calcBackProject([hsv], [0, 1], handHist, [0, 180, 0, 256], 1)
disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (21, 21))
cv2.filter2D(dst, -1, disc, dst)
# dst is now a probability map
# Use binary thresholding to create a map of 0s and 1s
# 1 means the pixel is part of the hand and 0 means not
ret, thresh = cv2.threshold(dst, 150, 255, cv2.THRESH_BINARY)
kernel = np.ones((5, 5), np.uint8)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=8)
#thresh = cv2.merge((thresh, thresh, thresh))
return thresh
def back_projection_demo():#反向投影
roi = cv.imread("C:/Users/32936/Desktop/2/qiuyi.jpg")#搜索目标
target = cv.imread("C:/Users/32936/Desktop/2/maidi.jpg")#待搜索图片
hsv = cv.cvtColor(roi,cv.COLOR_BGR2HSV)
hsvt = cv.cvtColor(target,cv.COLOR_BGR2HSV)
roihist = cv.calcHist([hsv],[0,1],None,[32,32],[0,180,0,256])#获取搜索目标直方图
cv.normalize(roihist,roihist,0,255,cv.NORM_MINMAX)#直方图归一化
mask = cv.calcBackProject([hsvt],[0,1],roihist,[0,180,0,256],3)#直方图反向投影
dst = cv.bitwise_and(target,target,mask=mask)
cv.imshow("result",dst)
cv.imshow("target",target)
cv.imshow("image",roi)
cv.imshow("mask",mask)
# set up the ROI for tracking
roi = frame[y:y + h, x:x + w]
hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)),
np.array((180., 255., 255.)))
roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180])
cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)
# setup the termination criteria, either 10 iteration or move by atleast 1 pt
term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1)
cv2.imshow('roi', roi)
while (1):
ret, frame = cap.read()
if ret == True:
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1)
# apply meanshift to get the new location
ret, track_window = cv2.meanShift(dst, track_window, term_crit)
# Draw it on image
x, y, w, h = track_window
final_image = cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0),
3)
cv2.imshow(' final_image', final_image)
cv2.imshow('dst', dst)
k = cv2.waitKey(30) & 0xFF
if k == 27:
break
else:
break
# OpenCV provides an inbuilt function cv2.calcBackProject(). Its parameters are almost same as the cv2.calcHist() function. One of its parameter is histogram which is histogram of the object and we have to find it. Also, the object histogram should be normalized before passing on to the backproject function. It returns the probability image. Then we convolve the image with a disc kernel and apply threshold. Below is my code and output :
from cv2 import cv2
import numpy as np
img = cv2.imread('resource/messi.jpg')
roi = img.copy()[222:278, 2:77]
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
hist_roi = cv2.calcHist([hsv_roi], [0, 1], None, [180, 256], [0, 180, 0, 256])
cv2.normalize(hist_roi, hist_roi, 0, 255, cv2.NORM_MINMAX)
backproject = cv2.calcBackProject([hsv_img], [0, 1], hist_roi,
[0, 180, 0, 256], 1)
# Now convolute with circular disc
disckernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
circular = backproject.copy()
cv2.filter2D(backproject, -1, disckernel, circular)
# threshold and binary AND
ret, thresh = cv2.threshold(circular, 50, 255, 0)
thresh = cv2.merge((thresh, thresh, thresh))
res = cv2.bitwise_and(img, thresh.copy())
cv2.imshow('origin', img)
cv2.imshow('roi', roi)
cv2.imshow('back', backproject)
cv2.imshow('circular', circular)
def start_tracking(self):
# Iterate until the user presses the Esc key
while True:
# Capture the frame from webcam
_, self.frame = self.cap.read()
# Resize the input frame
self.frame = cv.resize(self.frame,
None,
fx=self.scaling_factor,
fy=self.scaling_factor,
interpolation=cv.INTER_AREA)
# Create a copy of the frame
vis = self.frame.copy()
# Convert the frame to HSV colorspace
hsv = cv.cvtColor(self.frame, cv.COLOR_BGR2HSV)
# Create the mask based on predefined thresholds
mask = cv.inRange(hsv, np.array((0., 60., 32.)),
np.array((180., 255., 255.)))
# Check if the user has selected the region
if self.selection:
# Extract the coordinates of the selected rectangle
x0, y0, x1, y1 = self.selection
# Extract the tracking window
self.track_window = (x0, y0, x1 - x0, y1 - y0)
# Extract the regions of interest
hsv_roi = hsv[y0:y1, x0:x1]
mask_roi = mask[y0:y1, x0:x1]
# Compute the histogram of the region of
# interest in the HSV image using the mask
hist = cv.calcHist([hsv_roi], [0], mask_roi, [16], [0, 180])
# Normalize and reshape the histogram
cv.normalize(hist, hist, 0, 255, cv.NORM_MINMAX)
self.hist = hist.reshape(-1)
# Extract the region of interest from the frame
vis_roi = vis[y0:y1, x0:x1]
# Compute the image negative (for display only)
cv.bitwise_not(vis_roi, vis_roi)
vis[mask == 0] = 0
# Check if the system in the "tracking" mode
if self.tracking_state == 1:
# Reset the selection variable
self.selection = None
# Compute the histogram back projection
hsv_backproj = cv.calcBackProject([hsv], [0], self.hist,
[0, 180], 1)
# Compute bitwise AND between histogram
# backprojection and the mask
hsv_backproj &= mask
# Define termination criteria for the tracker
term_crit = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10,
1)
# Apply CAMShift on 'hsv_backproj'
track_box, self.track_window = cv.CamShift(
hsv_backproj, self.track_window, term_crit)
# Draw an ellipse around the object
cv.ellipse(vis, track_box, (0, 255, 0), 2)
# Show the output live video
cv.imshow('Object Tracker', vis)
# Stop if the user hits the 'Esc' key
c = cv.waitKey(5)
if c == 27:
break
# Close all the windows
cv.destroyAllWindows()
# OpenCV provides an built-in function cv2.calcBackProject(). Its parameters are almost same as the cv2.calcHist() function. One of its parameter is histogram which is histogram of the object and we have to find it. Also, the object histogram should be normalized before passing on to the backproject function. It returns the probability image. Then we convolve the image with a disc kernel and apply threshold.
roi = cv2.imread(r'pictures\roi.a.2x.jpg')
hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
target = cv2.imread(r'pictures\a.2x.jpg')
hsvt = cv2.cvtColor(target, cv2.COLOR_BGR2HSV)
target_rgb = cv2.cvtColor(target, cv2.COLOR_BGR2RGB)
# calculating object histogram
roihist = cv2.calcHist([hsv], [0, 1], None, [180, 256], [0, 180, 0, 256])
# normalize histogram and apply backprojection
cv2.normalize(roihist, roihist, 0, 255, cv2.NORM_MINMAX)
dst = cv2.calcBackProject([hsvt], [0, 1], roihist, [0, 180, 0, 256], 1)
# Now convolute with circular disc
disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
cv2.filter2D(dst, -1, disc, dst)
# threshold and binary AND
ret, thresh = cv2.threshold(dst, 50, 255, 0)
thresh = cv2.merge((thresh, thresh, thresh))
res = cv2.bitwise_and(target_rgb, thresh)
# res = np.vstack((target,thresh,res))
# cv2.imwrite('res.jpg',res)
plt.subplot(311), plt.imshow(target_rgb)
plt.subplot(312), plt.imshow(thresh)
plt.subplot(313), plt.imshow(res)
lower_bound = np.array([15, 100, 100])
upper_bound = np.array([35, 255, 255])
pts = []
#reg for recatangle X= 400 to 640 ,Y= 0 to 200
#pts [i][0]=x,pts[i][1]=y
while True:
ret, frame = cap.read()
frame = cv2.flip(frame, 1)
temp_frame = frame
flag = 0
hsv_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv_frame, lower_bound, upper_bound)
roi_hist = cv2.calcHist([hsv_frame], [0], mask, [180], [0, 180])
roi_norm_hist = cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)
termination = (cv2.TERM_CRITERIA_EPS | cv2.TermCriteria_COUNT, 40, 10)
dst = cv2.calcBackProject([hsv_frame], [0], roi_norm_hist, [0, 180], 1)
ret, track_window = cv2.CamShift(dst, track_window, termination)
x, y, w, h = track_window
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
center = int((2 * x + w) / 2), int((2 * y + h) / 2)
pts.append(center)
if center[0] in range(500, 640) and center[1] in range(0, 150):
cv2.rectangle(temp_frame, (500, 0), (640, 150), (0, 255, 0), -1)
cv2.putText(temp_frame, "ERASED", (510, 70), cv2.FONT_HERSHEY_PLAIN, 2,
(255, 0, 0), 2)
temp_frame = cv2.resize(temp_frame, (1024, 768),
interpolation=cv2.INTER_LANCZOS4)
cv2.imshow("tracking", temp_frame)
flag = 1
pts = []
for i in range(1, len(pts)):