A simple orthomosaic program for nadir drone videos:
import cv2
import numpy as np
import os
def extract_frames(video_path, output_dir, seconds_interval=1):
"""
Extract frames every N seconds.
"""
if not os.path.exists(output_dir):
os.makedirs(output_dir)
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
print("Error: Could not open video.")
return []
fps = cap.get(cv2.CAP_PROP_FPS)
if fps <= 0:
fps = 30
frame_interval = int(fps * seconds_interval)
print(
f"Using frame interval: "
f"{frame_interval} frames ({seconds_interval} seconds)"
)
frame_paths = []
frame_count = 0
saved_count = 0
while True:
ret, frame = cap.read()
if not ret:
break
if frame_count % frame_interval == 0:
path = os.path.join(
output_dir,
f"frame_{saved_count:04d}.jpg"
)
cv2.imwrite(path, frame)
frame_paths.append(path)
saved_count += 1
frame_count += 1
cap.release()
print(f"Extracted {saved_count} frames.")
return frame_paths
def compute_affine_transforms(frame_paths):
orb = cv2.ORB_create(nfeatures=5000)
bf = cv2.BFMatcher(cv2.NORM_HAMMING)
transforms = [np.eye(3, dtype=np.float64)]
prev_img = cv2.imread(frame_paths[0])
prev_gray = cv2.cvtColor(prev_img, cv2.COLOR_BGR2GRAY)
cumulative_T = np.eye(3, dtype=np.float64)
for i in range(1, len(frame_paths)):
print(
f"Computing affine transform "
f"{i}/{len(frame_paths)-1}"
)
curr_img = cv2.imread(frame_paths[i])
curr_gray = cv2.cvtColor(curr_img, cv2.COLOR_BGR2GRAY)
kp1, des1 = orb.detectAndCompute(prev_gray, None)
kp2, des2 = orb.detectAndCompute(curr_gray, None)
if des1 is None or des2 is None:
print("Skipping frame: descriptors missing")
transforms.append(cumulative_T.copy())
prev_gray = curr_gray
continue
matches = bf.knnMatch(des1, des2, k=2)
good = []
for m, n in matches:
if m.distance < 0.75 * n.distance:
good.append(m)
print(f"Good matches: {len(good)}")
if len(good) < 200:
print(
f"Rejecting frame "
f"(only {len(good)} matches)"
)
transforms.append(cumulative_T.copy())
prev_gray = curr_gray
continue
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)
A, inliers = cv2.estimateAffinePartial2D(
dst_pts,
src_pts,
method=cv2.RANSAC,
ransacReprojThreshold=3
)
if A is None:
print("Skipping frame: affine failed")
transforms.append(cumulative_T.copy())
prev_gray = curr_gray
continue
T = np.eye(3, dtype=np.float64)
T[:2, :] = A
cumulative_T = cumulative_T @ T
transforms.append(cumulative_T.copy())
prev_gray = curr_gray
return transforms
def compute_canvas_size(frame_paths, homographies):
first = cv2.imread(frame_paths[0])
h, w = first.shape[:2]
all_corners = []
corners = np.float32([
[0, 0],
[w, 0],
[w, h],
[0, h]
]).reshape(-1, 1, 2)
for H in homographies:
transformed = cv2.perspectiveTransform(
corners,
H
)
all_corners.append(transformed)
all_corners = np.concatenate(all_corners, axis=0)
min_x = np.min(all_corners[:, 0, 0])
max_x = np.max(all_corners[:, 0, 0])
min_y = np.min(all_corners[:, 0, 1])
max_y = np.max(all_corners[:, 0, 1])
width = int(np.ceil(max_x - min_x))
height = int(np.ceil(max_y - min_y))
print("\nMosaic bounds:")
print(f"min_x={min_x:.0f}")
print(f"max_x={max_x:.0f}")
print(f"min_y={min_y:.0f}")
print(f"max_y={max_y:.0f}")
print(f"Canvas size = {width} x {height}")
translation = np.array([
[1, 0, -min_x],
[0, 1, -min_y],
[0, 0, 1]
])
return width, height, translation
def build_mosaic(
frame_paths,
homographies,
canvas_width,
canvas_height,
translation
):
mosaic = np.zeros(
(
canvas_height,
canvas_width,
3
),
dtype=np.uint8
)
for idx, frame_path in enumerate(frame_paths):
print(
f"Warping frame "
f"{idx+1}/{len(frame_paths)}"
)
img = cv2.imread(frame_path)
H_global = translation @ homographies[idx]
warped = cv2.warpPerspective(
img,
H_global,
(
canvas_width,
canvas_height
)
)
mask = np.any(warped > 0, axis=2)
mosaic[mask] = warped[mask]
return mosaic
def stitch_frames(frame_paths):
if len(frame_paths) == 0:
return None
print("\nComputing cumulative homographies...")
transforms = compute_affine_transforms(frame_paths)
print("\nComputing canvas size...")
width, height, translation = compute_canvas_size(
frame_paths,
transforms
)
print("\nBuilding mosaic...")
mosaic = build_mosaic(
frame_paths,
transforms,
width,
height,
translation
)
return mosaic
def main():
VIDEO_PATH = "mainindexedvideo.mp4"
FRAMES_DIR = "extracted_frames"
OUTPUT_MAP = "orthomosaic.jpg"
frame_paths = extract_frames(
VIDEO_PATH,
FRAMES_DIR,
seconds_interval=1
)
if not frame_paths:
return
mosaic = stitch_frames(frame_paths)
if mosaic is not None:
cv2.imwrite(
OUTPUT_MAP,
mosaic
)
print(
f"\nOrthomosaic saved as "
f"{OUTPUT_MAP}"
)
if __name__ == "__main__":
main()
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