我对opencv和python有一定的问题...
我收到了 4 张链锯图片和一个包含相同链锯的视频。我的任务是使用 opencv 在视频上的电锯周围绘制一个矩形。我试图这样做：
import numpy as np
import inspect
import cv2
def show_img(img, bw=False):
fig = plt.figure(figsize=(13,13))
ax = fig.gca()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.imshow(img, cmap='Greys_r' if bw else None)
plt.show()
img1 = cv2.imread("saw1.jpg")
img2 = cv2.imread("saw2.jpg")
img3 = cv2.imread("saw3.jpg")
img4 = cv2.imread("saw4.jpg")
gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
gray3 = cv2.cvtColor(img3, cv2.COLOR_BGR2GRAY)
gray4 = cv2.cvtColor(img4, cv2.COLOR_BGR2GRAY)
orb = cv2.ORB_create(nfeatures=1000)
kp1, des1 = orb.detectAndCompute(gray1, None)
kp2, des2 = orb.detectAndCompute(gray2, None)
kp3, des3 = orb.detectAndCompute(gray3, None)
kp4, des4 = orb.detectAndCompute(gray4, None)
matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
cap = cv2.VideoCapture('sawmovie.mp4')
while(cap.isOpened()):
ret, frame = cap.read()
if ret == True:
frameGray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
kp5, des5 = orb.detectAndCompute(frameGray, None)
matches = matcher.match(des1, des5)
matches = sorted(matches, key = lambda x:x.distance)
good_matches = matches[:10]
list_kp1 = []
list_kp2 = []
for mat in good_matches:
# Get the matching keypoints for each of the images
img1_idx = mat.queryIdx
img2_idx = mat.trainIdx
(x1, y1) = kp1[img1_idx].pt
(x2, y2) = kp5[img2_idx].pt
list_kp1.append((x1, y1))
list_kp2.append((x2, y2))
x_values = []
y_values = []
for kp in list_kp2:
x_values.append(kp[0])
for kp in list_kp2:
y_values.append(kp[1])
x_max = int(max(x_values))
x_min = int(min(x_values))
y_max = int(max(y_values))
y_min = int(min(y_values))
starting_point = (x_max,y_max)
ending_point = (x_min,y_min)
thickness = 2
color = (255,0,0)
final_image = cv2.rectangle(frame, starting_point, ending_point, color, thickness)
cv2.imshow('Frame', final_image)
if cv2.waitKey(25) & 0xFF == ord('q'):
break
else:
break
cap.release()
cv2.destroyAllWindows()
我试图这样做，但矩形位于随机位置。我被卡住了，不能再继续了。
示例图像和视频中的一帧： https://drive.google.com/drive/u/0/folders/1lD97uXttSUUUc2R76nXsbQVxNfONKnp8
我想获得的是使用这些示例图像在视频的每一帧上围绕锯子绘制一个矩形。
答案 0 :(得分：0)
真的很抱歉。我周末被叫去上班，看来我今天不会下班了。我会发布我现在得到的蹩脚代码。
我在两个文件中运行它。一个将所有匹配的点保存到一个文本文件中，另一个读取所有这些点并在它周围画一个框。特征点检测器需要永远运行，这就是为什么我将它保存到一个文件并只是重播该文件。
匹配器.py
import cv2
import numpy as np
import time
# sample images
samples = [];
for a in range(1,5):
samples.append(cv2.imread("saw" + str(a) + ".jpg"));
# test video
cap = cv2.VideoCapture("sawmovie.mp4");
# make detector and matcher
sift = cv2.SIFT_create();
orb = cv2.ORB_create();
bfm = cv2.BFMatcher();
# text file to save feature points (so we don't have to redo the slow part every time)
file = open("matched_points.txt", 'w');
# skip empty frames
frame = 0;
skip = 0;
for a in range(skip):
_,_ = cap.read();
frame += 1;
# go until video is finished
while True:
# get frame
ret, test = cap.read();
if not ret:
break;
# count
print("Frame: " + str(frame));
# create a fresh mask
mask = np.zeros(test.shape[:2], np.uint8);
# do keypoint matching with each sample
for sample in samples:
# get features
kp1, des1 = sift.detectAndCompute(sample,None);
kp2, des2 = sift.detectAndCompute(test,None);
# match
matches = bfm.knnMatch(des1, des2, k=2);
# lowe's ratio test
good = []
for a,b in matches:
if a.distance < 0.75*b.distance:
good.append(a);
# pull position of matches
points = [];
for m in good:
test_index = m.trainIdx;
x, y = kp2[test_index].pt;
points.append((int(x), int(y)));
# add to log
for point in points:
x, y = point;
out_str = "";
out_str += str(frame) + " ";
out_str += str(x) + " ";
out_str += str(y) + "\n";
file.write(out_str);
# show
cv2.imshow("Test", test);
cv2.imshow("Mask", mask);
key = cv2.waitKey(1);
if key == ord('q'):
break;
# increment frame counter
frame += 1;
file.close();
播放.py
import cv2
import numpy as np
import time
# rescale
def rescale(img, scale):
h, w = img.shape[:2];
h = int(h*scale);
w = int(w*scale);
return cv2.resize(img, (w,h));
# test video
cap = cv2.VideoCapture("sawmovie.mp4");
# text file to save feature points (so we don't have to redo the slow part every time)
file = open("matched_points.txt", 'r');
points = []; # [frame, x, y]
for line in file:
frame, x, y = [int(a) for a in line.split(' ')];
points.append([frame, x, y]);
file.close();
# group by frame
fps = [];
last_frame = -1;
group = [];
for point in points:
# unpack
frame, x, y = point;
if last_frame == -1:
last_frame = frame;
# check for new group
if last_frame != frame:
fps.append(group[:]);
group = [];
last_frame = frame;
# add to group
group.append((x,y));
# make videowriter
res = (1920, 1080);
four_cc = cv2.VideoWriter_fourcc(*'DIVX'); # this is windows-specific
writer = cv2.VideoWriter("marked.avi", four_cc, 20, res);
# skip empty frames
frame = 0;
skip = 0;
for a in range(skip):
_,_ = cap.read();
frame += 1;
# go until video is finished
while True:
# get frame
ret, test = cap.read();
if not ret:
break;
# count
print("Frame: " + str(frame));
# create a fresh mask
mask = np.zeros(test.shape[:2], np.uint8);
# grab points
points = fps[frame - skip];
# draw points
# for point in points:
# cv2.circle(test, point, 50, (0,0,255), -1);
# grow points to try and link up points that are closer than "dist"
dist = 40;
for point in points:
cv2.circle(mask, point, dist, 255, -1);
# get contours and find biggest by area
contours, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE);
biggest = None;
biggest_area = -1;
for con in contours:
area = cv2.contourArea(con);
if area > biggest_area:
biggest = con;
biggest_area = area;
# bounding box
x,y,w,h = cv2.boundingRect(biggest);
test = cv2.rectangle(test,(x,y),(x+w,y+h),(0,255,0),4);
# rescale to fit screen
# test = rescale(test, 0.5);
# print(test.shape);
# save
writer.write(test);
# show
cv2.imshow("Test", test);
# cv2.imshow("Mask", mask);
key = cv2.waitKey(1);
if key == ord('q'):
break;
# increment frame counter
frame += 1;
writer.release();