diff options
Diffstat (limited to 'FocusStack.py')
| -rw-r--r-- | FocusStack.py | 20 |
1 files changed, 16 insertions, 4 deletions
diff --git a/FocusStack.py b/FocusStack.py index d33f36d..506ef56 100644 --- a/FocusStack.py +++ b/FocusStack.py @@ -56,7 +56,10 @@ def findHomography(image_1_kp, image_2_kp, matches): # # def align_images(images): - use_sift = False + + # SIFT generally produces better results, but it is not FOSS, so chose the feature detector + # that suits the needs of your project. ORB does OK + use_sift = True outimages = [] @@ -96,16 +99,25 @@ def align_images(images): newimage = cv2.warpPerspective(images[i], hom, (images[i].shape[1], images[i].shape[0]), flags=cv2.INTER_LINEAR) outimages.append(newimage) + # If you find that there's a large amount of ghosting, it may be because one or more of the input + # images gets misaligned. Outputting the aligned images may help diagnose that. + # cv2.imwrite("aligned{}.png".format(i), newimage) return outimages # -# Do a lapacian or other filter +# Compute the gradient map of the image def doLap(image): - kernel_size = 9 # YOU SHOULD TUNE THIS VALUE TO SUIT YOUR NEEDS - blurred = cv2.GaussianBlur(image, (kernel_size,kernel_size), 0) + + # YOU SHOULD TUNE THESE VALUES TO SUIT YOUR NEEDS + kernel_size = 5 # Size of the laplacian window + blur_size = 5 # How big of a kernal to use for the gaussian blur + # Generally, keeping these two values the same or very close works well + # Also, odd numbers, please... + + blurred = cv2.GaussianBlur(image, (blur_size,blur_size), 0) return cv2.Laplacian(blurred, cv2.CV_64F, ksize=kernel_size) # |