问题描述
我想将线性视频制作成一幅长全景图，要么直接制作成一幅长全景图，要么分解成堆叠在一起的许多较小的全景图。视频看起来像这样。最好的方法是什么？我曾尝试一次缝合几帧，但我没有缝合。是否有值得研究的特定方法？视频时长 60 秒，可以以任何 fps 分帧。 这是我目前的代码
import cv2
import numpy as np
import glob
import imutils
# DEFINE THE HELPER FUNCTIONS
def draw_matches(img1,keypoints1,img2,keypoints2,matches):
r,c = img1.shape[:2]
r1,c1 = img2.shape[:2]
# Create a blank image with the size of the first image + second image
output_img = np.zeros((max([r,r1]),c + c1,3),dtype='uint8')
output_img[:r,:c,:] = np.dstack([img1])
output_img[:r1,c:c + c1,:] = np.dstack([img2])
# Go over all of the matching points and extract them
for match in matches:
img1_idx = match.queryIdx
img2_idx = match.trainIdx
(x1,y1) = keypoints1[img1_idx].pt
(x2,y2) = keypoints2[img2_idx].pt
# Draw circles on the keypoints
cv2.circle(output_img,(int(x1),int(y1)),4,(0,255,255),1)
cv2.circle(output_img,(int(x2) + c,int(y2)),1)
# Connect the same keypoints
cv2.line(output_img,1)
return output_img
def warpImages(img1,H):
rows1,cols1 = img1.shape[:2]
rows2,cols2 = img2.shape[:2]
list_of_points_1 = np.float32([[0,0],[0,rows1],[cols1,0]]).reshape(-1,1,2)
temp_points = np.float32([[0,rows2],[cols2,2)
# When we have established a homography we need to warp perspective
# Change field of view
list_of_points_2 = cv2.perspectiveTransform(temp_points,H)
list_of_points = np.concatenate((list_of_points_1,list_of_points_2),axis=0)
[x_min,y_min] = np.int32(list_of_points.min(axis=0).ravel() - 0.5)
[x_max,y_max] = np.int32(list_of_points.max(axis=0).ravel() + 0.5)
translation_dist = [-x_min,-y_min]
H_translation = np.array([[1,translation_dist[0]],translation_dist[1]],1]])
output_img = cv2.warpPerspective(img2,H_translation.dot(H),(x_max - x_min,y_max - y_min))
output_img[translation_dist[1]:rows1 + translation_dist[1],translation_dist[0]:cols1 + translation_dist[0]] = img1
# print(output_img)
return output_img
# End of Funcion definitions
# Main program begins here
# Define input and output paths
input_path = "/Users/akshayacharya/Desktop/Panorama/Bazinga/Test images for final/Highfps2fps/*.jpg"
# Define whatever variables necessary
input_img = glob.glob(input_path)
img_path = sorted(input_img)
for i in range(0,len(img_path)):
img = cv2.imread(img_path[i])
img = cv2.resize(img,(400,300))
cv2.imwrite(img_path[i],img)
tmp = img_path[0]
flag = True
pano = []
i = 1
count = 0
indices = []
k = 1
while i < len(img_path):
indices.append(i)
print(i)
count += 1
if flag:
img1 = cv2.imread(tmp,cv2.COLOR_BGR2GRAY)
img2 = cv2.imread(img_path[i],cv2.COLOR_BGR2GRAY)
flag = False
img1 = cv2.resize(img1,0),fx=1,fy=1)
img2 = cv2.imread(img_path[i],cv2.COLOR_BGR2GRAY)
img2 = cv2.resize(img2,fy=1)
orb = cv2.ORB_create(nfeatures=2000)
keypoints1,descriptors1 = orb.detectAndCompute(img1,None)
keypoints2,descriptors2 = orb.detectAndCompute(img2,None)
# Create a BFMatcher object.
# It will find all of the matching keypoints on two images
bf = cv2.BFMatcher_create(cv2.NORM_HAMMING)
# Find matching points
matches = bf.knnMatch(descriptors1,descriptors2,k=2)
all_matches = []
for m,n in matches:
all_matches.append(m)
# Finding the best matches
good = []
for m,n in matches:
if m.distance < 0.9 * n.distance:
good.append(m)
MIN_MATCH_COUNT = 15
if len(good) > MIN_MATCH_COUNT:
# Convert keypoints to an argument for findHomography
src_pts = np.float32([keypoints1[m.queryIdx].pt for m in good]).reshape(-1,2)
dst_pts = np.float32([keypoints2[m.trainIdx].pt for m in good]).reshape(-1,2)
# Establish a homography
M,_ = cv2.findHomography(src_pts,dst_pts,cv2.RANSAC,5.0)
result = warpImages(img2,img1,M)
img1 = result
i += 1
if count % 8 == 0:
i += 12
count = 0
"""stitched = img1
print(np.shape(stitched))
stitched = cv2.copyMakeBorder(stitched,10,cv2.BORDER_CONSTANT,0))
gray = cv2.cvtColor(stitched,cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray,cv2.THRESH_BINARY)[1]
cnts = cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
c = max(cnts,key=cv2.contourArea)
mask = np.zeros(thresh.shape,dtype="uint8")
(x,y,w,h) = cv2.boundingRect(c)
cv2.rectangle(mask,(x,y),(x + w,y + h),-1)
minRect = mask.copy()
sub = mask.copy()
while cv2.countNonZero(sub) > 0:
minRect = cv2.erode(minRect,None)
sub = cv2.subtract(minRect,thresh)
cnts = cv2.findContours(minRect.copy(),key=cv2.contourArea)
(x,h) = cv2.boundingRect(c)
stitched = stitched[y:y + h,x:x + w]
"""
#pano.append(stitched)
result = cv2.resize(result,(1080,720))
#cv2.imwrite(f"Test images for final/Highfps2fps/temp_pano/frame{k}.jpg",stitched)
k += 1
try:
img1 = cv2.imread(img_path[i])
i = i + 1
img1 = cv2.resize(img1,300))
cv2.imshow("Stitch",result)
cv2.waitKey(0)
indices = []
print(np.shape(img1))
except:
continue
if len(indices) == 8:
indices = [0]
j= 100000
if indices[0] != 0:
i = 0
print(indices)
j = indices[i]
temp = img_path[j]
if j == (len(img_path) - 1):
img_1 = cv2.imread(temp)
i = 1
flag1 = True
while i < len(indices):
if flag1:
img_1 = cv2.imread(temp,cv2.COLOR_BGR2GRAY)
j = indices[i]
img_2 = cv2.imread(img_path[j],cv2.COLOR_BGR2GRAY)
flag1 = False
img_1 = cv2.resize(img1,fy=1)
img_2 = cv2.imread(img_path[i],cv2.COLOR_BGR2GRAY)
img_2 = cv2.resize(img2,descriptors1 = orb.detectAndCompute(img_1,descriptors2 = orb.detectAndCompute(img_2,None)
bf = cv2.BFMatcher_create(cv2.NORM_HAMMING)
matches = bf.knnMatch(descriptors1,n in matches:
if m.distance < 0.9 * n.distance:
good.append(m)
MIN_MATCH_COUNT = 10
if len(good) > MIN_MATCH_COUNT:
# Convert keypoints to an argument for findHomography
src_pts = np.float32([keypoints1[m.queryIdx].pt for m in good]).reshape(-1,5.0)
result1 = warpImages(img_2,img_1,M)
img_1 = result1
i += 1
if j != 100000:
img_1 = cv2.resize(img_1,300))
#cv2.imwrite(f"Test images for final/Highfps2fps/temp_pano/frame{k}.jpg",img_1)
cv2.imshow("Last pano",img_1)
cv2.waitKey(0)
# stacking all the tiny panoramas
input_path = "/Users/akshayacharya/Desktop/Panorama/Bazinga/Test images for final/Highfps2fps/temp_pano/*.jpg"
output_path = "/Users/akshayacharya/Desktop/Panorama/Bazinga/Output/New/pano10.jpg"
list_images = glob.glob(input_path)
list_sorted = sorted(list_images)
images = []
for image in list_sorted:
img = cv2.imread(image)
img = cv2.resize(img,(1280,720))
# cv2.imshow(f"{image}",img)
images.append(img)
final_image = cv2.hconcat(images)
#final_image = cv2.resize(final_image,(2000,1500))
cv2.imshow("Acceptable",final_image)
cv2.waitKey(0)
#cv2.imwrite(output_path,final_image)
解决方法
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