如何解决如何从多个图像创建一个长的线性全景
我想拍摄一个线性视频,然后将其拆分为帧,然后拼接帧以获得非常长的全景图。但是,我无法正确处理。我改用了一种新方法,一次拍摄大约 5 帧,然后拼接它们以获得更小的全景图。我保存了所有这些小全景图,然后尝试缝合它们以获得大的全景图。但是,我并没有清楚地看到最终的全景图。
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
def trim(frame):
#crop top
if not np.sum(frame[0]):
return trim(frame[1:])
#crop top
if not np.sum(frame[-1]):
return trim(frame[:-2])
#crop top
if not np.sum(frame[:,0]):
return trim(frame[:,1:])
#crop top
if not np.sum(frame[:,-1]):
return trim(frame[:,:-2])
return frame
# 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"
output_path = "Output/o5.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,(300,300))
cv2.imwrite(img_path[i],img)
tmp = img_path[0]
flag = True
pano = []
j = 1
for i in range(1,len(img_path)):
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)
# cv2.imshow('1',cv2.drawKeypoints(img1,None,(255,255)))
# cv2.imshow('2',cv2.drawKeypoints(img2,255)))
# cv2.waitKey(0)
# 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)
# print("Descriptor of the first keypoint: ")
# print(descriptors1[0])
# print(type(matches))
all_matches = []
for m,n in matches:
all_matches.append(m)
img3 = draw_matches(img1,all_matches[:])
# v2.imshow('Matches',img3)
# cv2.waitKey(0)
# Finding the best matches
good = []
for m,n in matches:
if m.distance < 0.9 * n.distance:
good.append(m)
# cv2.imshow('Final1',[keypoints1[m.queryIdx] for m in good],255)))
# cv2.imshow('Final2',[keypoints2[m.queryIdx] for m in good],255)))
# cv2.waitKey(0)
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
if i%5 == 0:
stitched = img1
print(np.shape(stitched))
stitched = cv2.copyMakeBorder(stitched,10,cv2.BORDER_CONSTANT,0))
# convert the stitched image to grayscale and threshold it
# such that all pixels greater than zero are set to 255
# (foreground) while all others remain 0 (background)
gray = cv2.cvtColor(stitched,cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray,cv2.THRESH_BINARY)[1]
# find all external contours in the threshold image then find
# the *largest* contour which will be the contour/outline of
# the stitched image
cnts = cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
c = max(cnts,key=cv2.contourArea)
# allocate memory for the mask which will contain the
# rectangular bounding Box of the stitched image region
mask = np.zeros(thresh.shape,dtype="uint8")
(x,y,w,h) = cv2.boundingRect(c)
cv2.rectangle(mask,(x,y),(x + w,y + h),-1)
# create two copies of the mask: one to serve as our actual
# minimum rectangular region and another to serve as a counter
# for how many pixels need to be removed to form the minimum
# rectangular region
minRect = mask.copy()
sub = mask.copy()
# keep looping until there are no non-zero pixels left in the
# subtracted image
while cv2.countNonZero(sub) > 0:
# erode the minimum rectangular mask and then subtract
# the thresholded image from the minimum rectangular mask
# so we can count if there are any non-zero pixels left
minRect = cv2.erode(minRect,None)
sub = cv2.subtract(minRect,thresh)
# find contours in the minimum rectangular mask and then
# extract the bounding Box (x,y)-coordinates
cnts = cv2.findContours(minRect.copy(),key=cv2.contourArea)
(x,h) = cv2.boundingRect(c)
# use the bounding Box coordinates to extract the our final
# stitched image
stitched = stitched[y:y + h,x:x + w]
# cv2.imwrite("cropped.jpg",stitched)
# writeStatus = cv2.imwrite(output_path,stitched)
# if writeStatus is True:
# print("image written")
# else:
# print("problem") # or raise exception,handle problem,etc.
#stitched = cv2.resize(stitched,(1080,720))
pano.append(stitched)
#stitched = cv2.resize(stitched,(np.shape(stitched)[0],1500))
#titched1 = trim(stitched)
cv2.imshow("Stitch",stitched)
cv2.waitKey(0)
cv2.imwrite(f"Test images for final/Highfps2fps/temp_pano/frame{j}.jpg",stitched)
j += 1
try:
img1 = cv2.imread(img_path[i+1])
i= i+1
img1 = cv2.resize(img1,(400,400))
print(np.shape(img1))
except:
continue
#cv2.imshow("Stitch",stitched)
#cv2.waitKey(0)
final_input = "/Users/akshayacharya/Desktop/Panorama/Bazinga/Test images for final/Highfps2fps/temp_pano/*.jpg"
input_image = glob.glob(final_input)
image_path = sorted(input_image)
for i in range(0,len(image_path)):
img = cv2.imread(image_path[i])
img = cv2.resize(img,720))
cv2.imwrite(image_path[i],img)
temp = image_path[0]
flag = True
for i in range(1,len(image_path)):
if flag:
img1 = cv2.imread(temp,255)))
# cv2.waitKey(0)
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,M)
img1 = result
cv2.imshow("Comeon",img1)
cv2.waitKey(0)
cv2.imwrite('final.jpg',img1)
最终的输出是这样的 Final 为什么即使在较小的全景图中似乎有很好的重叠,它也不能正确拼接
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