这篇文章主要为大家展示了python如何实现图像外边界跟踪，内容简而易懂，希望大家可以学习一下，学习完之后肯定会有收获的，下面让小编带大家一起来看看吧。
share一些python实现的code
#!/usr/bin/env python
#coding=utf-8
import cv2
img = cv2.imread("trace_border2.bmp")
[img_h, img_w, img_channel] = img.shape
trace = []
start_x = 0
start_y = 0
gray = img[:,:,1]
for h in range(img_h):
for w in range(img_w):
if (gray[h,w] > 128):
gray[h,w] = 255
else:
gray[h,w] = 0
#python 跳出多重循环
#https://www.cnblogs.com/xiaojiayu/p/5195316.html
class getoutofloop(Exception): pass
try:
for h in range(img_h - 2):
for w in range(img_w - 2):
if gray[h,w] == 0:
start_x = w
start_y = h
raise getoutofloop
except getoutofloop:
pass
print("Start Point (%d %d)"%(start_x, start_y))
trace.append([start_x, start_y])
# 8邻域 顺时针方向搜索
neighbor = [[-1,-1],[0,-1],[1,-1],[1,0],[1,1],[0,1],[-1,1],[-1,0]]
neighbor_len = len(neighbor)
#先从当前点的左上方开始，
# 如果左上方也是黑点(边界点)：
# 搜索方向逆时针旋转90 i-=2
# 否则：
# 搜索方向顺时针旋转45 i+=1
i = 0
cur_x = start_x + neighbor[i][0]
cur_y = start_y + neighbor[i][1]
is_contour_point = 0
try:
while not ((cur_x == start_x) and (cur_y == start_y)):
is_contour_point = 0
while is_contour_point == 0:
#neighbor_x = cur_x +
if gray[cur_y, cur_x] == 0:
is_contour_point = 1
trace.append([cur_x, cur_y])
i -= 2
if i < 0:
i += neighbor_len
else:
i += 1
if i >= neighbor_len:
i -= neighbor_len
#print(i)
cur_x = cur_x + neighbor[i][0]
cur_y = cur_y + neighbor[i][1]
except:
print("throw error")
for i in range(len(trace)-1):
cv2.line(img,(trace[i][0],trace[i][1]), (trace[i+1][0], trace[i+1][1]),(0,0,255),3)
cv2.imshow("img", img)
cv2.waitKey(10)
cv2.rectangle(img,(start_x, start_y),(start_x + 20, start_y + 20),(255,0,0),2)
cv2.imshow("img", img)
cv2.waitKey(0)
cv2.destroyWindow("img")补充知识：python实现目标跟踪（opencv）
1.单目标跟踪
import cv2
import sys
(major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')
print(major_ver, minor_ver, subminor_ver)
if __name__ == '__main__':
# 创建跟踪器
tracker_type = 'MIL'
tracker = cv2.TrackerMIL_create()
# 读入视频
video = cv2.VideoCapture("./data/1.mp4")
# 读入第一帧
ok, frame = video.read()
if not ok:
print('Cannot read video file')
sys.exit()
# 定义一个bounding box
bbox = (287, 23, 86, 320)
bbox = cv2.selectROI(frame, False)
# 用第一帧初始化
ok = tracker.init(frame, bbox)
while True:
ok, frame = video.read()
if not ok:
break
# Start timer
timer = cv2.getTickCount()
# Update tracker
ok, bbox = tracker.update(frame)
# Cakculate FPS
fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer)
# Draw bonding box
if ok:
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:
cv2.putText(frame, "Tracking failed detected", (100, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
# 展示tracker类型
cv2.putText(frame, tracker_type+"Tracker", (100, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)
# 展示FPS
cv2.putText(frame, "FPS:"+str(fps), (100, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)
# Result
cv2.imshow("Tracking", frame)
# Exit
k = cv2.waitKey(1) & 0xff
if k ==27 : break2.多目标跟踪
使用GOTURN作为跟踪器时，须将goturn.caffemodel和goturn.prototxt放到工作目录才能运行，解决问题链接https://stackoverflow.com/questions/48802603/getting-deep-learning-tracker-goturn-to-run-opencv-python
import cv2
import sys
(major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')
print(major_ver, minor_ver, subminor_ver)
if __name__ == '__main__':
# 创建跟踪器
# 'BOOSTING', 'MIL','KCF', 'TLD', 'MEDIANFLOW', 'GOTURN', 'MOSSE'
tracker_type = 'MIL'
tracker = cv2.MultiTracker_create()
# 创建窗口
cv2.namedWindow("Tracking")
# 读入视频
video = cv2.VideoCapture("./data/1.mp4")
# 读入第一帧
ok, frame = video.read()
if not ok:
print('Cannot read video file')
sys.exit()
# 定义一个bounding box
box1 = cv2.selectROI("Tracking", frame)
box2 = cv2.selectROI("Tracking", frame)
box3 = cv2.selectROI("Tracking", frame)
# 用第一帧初始化
ok = tracker.add(cv2.TrackerMIL_create(), frame, box1)
ok1 = tracker.add(cv2.TrackerMIL_create(), frame, box2)
ok2 = tracker.add(cv2.TrackerMIL_create(), frame, box3)
while True:
ok, frame = video.read()
if not ok:
break
# Start timer
timer = cv2.getTickCount()
# Update tracker
ok, boxes = tracker.update(frame)
print(ok, boxes)
# Cakculate FPS
fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer)
for box in boxes:
# Draw bonding box
if ok:
p1 = (int(box[0]), int(box[1]))
p2 = (int(box[0] + box[2]), int(box[1] + box[3]))
cv2.rectangle(frame, p1, p2, (255, 0, 0), 2, 1)
else:
cv2.putText(frame, "Tracking failed detected", (100, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255),2)
# 展示tracker类型
cv2.putText(frame, tracker_type+"Tracker", (100, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)
# 展示FPS
cv2.putText(frame, "FPS:"+str(fps), (100, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)
# Result
cv2.imshow("Tracking", frame)
# Exit
k = cv2.waitKey(1) & 0xff
if k ==27 : break以上就是关于python如何实现图像外边界跟踪的内容，如果你们有学习到知识或者技能，可以把它分享出去让更多的人看到。
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