How to create a long linear panorama from over 30 images using open source software from CUI or from code

Hi, I want to create a panorama from a linear video by splitting it into frames and then stitching them. However, As i was not getting output, I changed my approach. I created smaller panoramas from 5 to 6 frames and now I want to stitvch these resultant panoramas to geta long panorama but it isnt giving exact output. CAn someone guide me?

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)), 4, (0, 255, 255), 1)

        # Connect the same keypoints
        cv2.line(output_img, (int(x1), int(y1)), (int(x2) + c, int(y2)), (0, 255, 255), 1)

    return output_img


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)

    # 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, 0, translation_dist[0]], [0, 1, translation_dist[1]], [0, 0, 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, (0,0), fx=1, fy=1)
    img2 = cv2.imread(img_path[i], cv2.COLOR_BGR2GRAY)
    img2 = cv2.resize(img2, (0,0), fx=1, 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, keypoints1, None, (255, 0, 255)))
    # cv2.imshow('2',cv2.drawKeypoints(img2, keypoints2, None, (255,255, 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, keypoints1, img2, keypoints2, 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',cv2.drawKeypoints(img1, [keypoints1[m.queryIdx] for m in good], None, (255, 0, 255)))
    # cv2.imshow('Final2',cv2.drawKeypoints(img2, [keypoints2[m.queryIdx] for m in good], None, (255, 0, 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, 1, 2)
        dst_pts = np.float32([keypoints2[m.trainIdx].pt for m in good]).reshape(-1, 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, 10, 10, 10,
                                      cv2.BORDER_CONSTANT, (0, 0, 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, 0, 255, 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), 255, -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(), cv2.RETR_EXTERNAL,
                                cv2.CHAIN_APPROX_SIMPLE)
        cnts = imutils.grab_contours(cnts)
        c = max(cnts, key=cv2.contourArea)
        (x, y, w, 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,(1080,720))
    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, cv2.COLOR_BGR2GRAY)
        img2 = cv2.imread(img_path[i], cv2.COLOR_BGR2GRAY)
        flag = False
    img1 = cv2.resize(img1, (0,0), fx=1, fy=1)
    img2 = cv2.imread(img_path[i], cv2.COLOR_BGR2GRAY)
    img2 = cv2.resize(img2, (0,0), fx=1, 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, keypoints1, None, (255, 0, 255)))
    # cv2.imshow('2',cv2.drawKeypoints(img2, keypoints2, None, (255,255, 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, keypoints1, img2, keypoints2, 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',cv2.drawKeypoints(img1, [keypoints1[m.queryIdx] for m in good], None, (255, 0, 255)))
    # cv2.imshow('Final2',cv2.drawKeypoints(img2, [keypoints2[m.queryIdx] for m in good], None, (255, 0, 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, 1, 2)
        dst_pts = np.float32([keypoints2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)

        # Establish a homography
        M, _ = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)

        result = warpImages(img2, img1, M)
        img1 = result

cv2.imshow("Comeon", img1)
cv2.waitKey(0)
cv2.imwrite('final.jpg',img1)

final This is the final image

These are the smaller panoramas. AS you can see, the final image is not even close to the required output

If that could work it could do wonders for drone mapping.

1 Like

You could try doing a slit scan with ffmpeg https://cameronbrowning.com/content/creating-slit-scan-images-from-quicktime-movies-with-ffmpeg-on-mac-os-x/

Slit scan of your nightmares. :sweat_smile:

From the OP, the requirement seems to be to stitch photos from drones. A web search for “stitch drone photos software” produces large numbers of possible systems.