Later version having issues impelementing your code

[martinchez]

could you update your code for later vesions of python am interested in some ideas here

[martinchez]

my email is martingogle12@gmail.com

[adityajain07]

Hi Martin,

Can you please specify the python version and also post the error snapshot?

[martinchez]

[martinchez]

this is the code `import cv2 import numpy as np from matplotlib import pyplot as plt import glob import time import os

import math

FilepathFirst = os.path.abspath("/Users/uSER/Downloads/Documents/DJI_0122.JPG") FilepathFirst1 = os.path.abspath("/Users/uSER/Downloads/Documents/DJI_0124.JPG") FilepathAll = os.path.abspath("/Users/uSER/Downloads/Documents/*.JPG") img1 = cv2.imread(FilepathFirst)

img2 = cv2.imread(FilepathFirst1)

tic = time.process_time() test = []

def warpImages(img1, img2, H): rows1, cols1 = img1.shape[:2] rows2, cols2 = img2.shape[:2]

list_of_points_1 = np.float32([[0,0], [0,rows1], [cols1,rows1], [cols1,0]]).reshape(-1,1,2)
temp_points = np.float32([[0,0], [0,rows2], [cols2,rows2], [cols2,0]]).reshape(-1,1,2)
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, 0, translation_dist[0]], [0, 1, translation_dist[1]], [0,0,1]])

print ("Min and Max:", x_min, y_min, x_max, y_max)
print ("Translation Distance:", translation_dist  )  

output_img = cv2.warpPerspective(img2, H_translation.dot(H), (x_max-x_min, y_max-y_min))
FrameSize = output_img.shape
NewImage = img2.shape
output_img[translation_dist[1]:rows1+translation_dist[1], translation_dist[0]:cols1+translation_dist[0]] = img1

OriginR = int(list_of_points_2[0][0][1])
OriginC = int(list_of_points_2[0][0][0])

# if the origin of projected image is out of bounds, then mapping to ()
if OriginR < 0:
    OriginR = 0
if OriginC < 0:
    OriginC = 0

# Clipping the new image, if it's size is more than the frame    
if NewImage[0] > FrameSize[0]-OriginR:
    img2 = img2[0:FrameSize[0]-OriginR,:]

if NewImage[1] > FrameSize[1]-OriginC:
    img2 = img2[:,0:FrameSize[1]-OriginC]    

print ("Image 2 Magic size:", img2.shape)
output_img[OriginR:NewImage[0]+OriginR, OriginC:NewImage[1]+OriginC] = img2    

return output_img

images = sorted(glob.glob(FilepathAll)) # for reading images n = 10000; # no of features to extract

def giveMosaic(FirstImage, no):

EList = []      # this stores the average reprojection error
ImgList = []          # No of images stitched
Matches = []    # this stores the number of good matches at every stage
i = 1

heightM,widthM = FirstImage.shape[:2]
FirstImage = cv2.resize(FirstImage, (widthM / 4, heightM / 4))
RecMosaic = FirstImage

for name in images[1:]:

    print (name)
    image = cv2.imread(name) 

    sift = cv2.SIFT(no)

    ######## Resize them (they are too big)
    # Get their dimensions        
    height,width = image.shape[:2]

print heightM, widthM, height, width

    image = cv2.resize(image, (width / 4, height / 4))
    ###########################

    # Find the features
    kp1= des1 = sift.detectAndCompute(RecMosaic,None)   # kp are the keypoints, des are the descriptors
    kp2= des2 = sift.detectAndCompute(image,None)

    ########### FLANN Matcher  ##########      
    FLANN_INDEX_KDTREE = 0
    index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
    search_params = dict(checks = 50)
    flann = cv2.FlannBasedMatcher(index_params, search_params)
    matches = flann.knnMatch(des1,des2,k=2)
    #############

    # store all the good matches as per Lowe's ratio test.
    good = []
    allPoints = []
    for m,n in matches:
        if m.distance < 0.7*n.distance:
            good.append(m)

        allPoints.append(m)

    Matches.append(len(good))
    print ("Good_Matches:", len(good))
    ##################################

    #### Finding the homography #########
    src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
    dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)

    all_src_pts = np.float32([ kp1[m.queryIdx].pt for m in allPoints ]).reshape(-1,1,2)
    all_dst_pts = np.float32([ kp2[m.trainIdx].pt for m in allPoints ]).reshape(-1,1,2)

    M, mask = cv2.findHomography(dst_pts, src_pts, cv2.RANSAC,6.0)
    ###################################

    #### Finding the euclidean distance error ####
    list1 = np.array(src_pts)    
    list2 = np.array(dst_pts)
    list2 = np.reshape(list2, (len(list2), 2))
    ones = np.ones(len(list1))    
    TestPoints = np.transpose(np.reshape(list1, (len(list1), 2)))
    print ("Length:", np.shape(TestPoints), np.shape(ones))
    TestPointsHom = np.vstack((TestPoints, ones))  
    print ("Homogenous Points:", np.shape(TestPointsHom))

    projectedPointsH = np.matmul(M, TestPointsHom)  # projecting the points in test image to collage image using homography matrix    
    projectedPointsNH = np.transpose(np.array([np.true_divide(projectedPointsH[0,:], projectedPointsH[2,:]), np.true_divide(projectedPointsH[1,:], projectedPointsH[2,:])]))

    print ("list2 shape:", np.shape(list2))
    print ("NH Points shape:", np.shape(projectedPointsNH))
    print ("Raw Error Vector:", np.shape(np.linalg.norm(projectedPointsNH-list2, axis=1)))
    Error = int(np.sum(np.linalg.norm(projectedPointsNH-list2, axis=1)))
    print ("Total Error:", Error)
    AvgError = np.divide(np.array(Error), np.array(len(list1)))
    print ("Average Error:", AvgError)

    ##################       

    i+=1

    RecMosaic = warpImages(RecMosaic, image, M)
    cv2.imwrite("FinalMosaicTemp.jpg", RecMosaic)
    print (i)

    EList.append(AvgError)
    ImgList.append(i)

    if i==40:
        break

cv2.imwrite("FinalMosaic.jpg", RecMosaic)
return EList, ImgList, Matches

ErrorList,ImgNumbers,GoodMatches = giveMosaic(img1, 10000) toc = time.process_time() print(toc-tic) ` Bellow is the error that am getting

[martinchez]

https://github.com/martinchez/Mosaicking-DJI-Aerial-imagery-using-python this is the repository to the images that am using if you could help

[deepzsenu]

You have to downgrade your cv2 version and the script is written in python 2.x so you have to change the code also. hope this will help you.

[deepzsenu]

cv2 version 3.4.2.17 cv2 contrub 3.4.2.17

[martinchez]

how do i donwgrade

[martinchez]

How do I change the code.

On Mon, Dec 14, 2020, 17:19 Deepak Kumar Saxena notifications@github.com wrote:

You have to downgrade your cv2 version and the script is written in python 2.x so you have to change the code also. hope this will help you.

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/adityajain07/Drone-Images-Mosaicing/issues/1#issuecomment-744470983, or unsubscribe https://github.com/notifications/unsubscribe-auth/AHTTBCA2XTSBE2R45OYHIADSUYNHLANCNFSM4UOQKYNQ .

[deepzsenu]

!pip install opencv-python==3.4.2.17 !pip install opencv-contrib-python==3.4.2.17

[martinchez]

am getting this error

[martinchez]

3.4.9.33, 3.4.10.35, 3.4.10.37, 3.4.11.39, 3.4.11.41, 3.4.11.43, 3.4.11.45, 4.1.2.30, 4.2.0.32, 4.2.0.34, 4.3.0.36, 4.3.0.3
8, 4.4.0.40, 4.4.0.42, 4.4.0.44, 4.4.0.46)
ERROR: No matching distribution found for opencv-python==3.4.2.17```

[deepzsenu]

please go through my repo of multiple image-stitching and stitching a pair of images here is the link https://github.com/deepzsenu/Image-stitching-multiple-and-a-pair-of-images-