L'idea di base e' quella di cercare le differenze tra le due immagini sottostanti
Non e' immediatamente visibile ma ci sono dei sassi che si sono spostati nella zona a coordinata 2600,1600 circa. Per questa prova e' stato impiegato l'algoritmo di Structural Similarity di SkImage
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| Foto 1 |
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| Foto2 |
Questa e' l'elaborazione (bianco minima differenza)
Il risultato finale e' la vegetazione con il suo movimento ha completamente obliterato il segnale relativo allo spostamento dei sassi
from skimage.metrics import structural_similarity
import cv2
import numpy as np
# Load images
before = cv2.imread('20241114.jpg')
after = cv2.imread('20241115.jpg')
# Convert images to grayscale
before_gray = cv2.cvtColor(before, cv2.COLOR_BGR2GRAY)
after_gray = cv2.cvtColor(after, cv2.COLOR_BGR2GRAY)
# Compute SSIM between the two images
(score, diff) = structural_similarity(before_gray, after_gray, full=True)
print("Image Similarity: {:.4f}%".format(score * 100))
# The diff image contains the actual image differences between the two images
# and is represented as a floating point data type in the range [0,1]
# so we must convert the array to 8-bit unsigned integers in the range
# [0,255] before we can use it with OpenCV
diff = (diff * 255).astype("uint8")
diff_box = cv2.merge([diff, diff, diff])
# Threshold the difference image, followed by finding contours to
# obtain the regions of the two input images that differ
thresh = cv2.threshold(diff, 20, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
mask = np.zeros(before.shape, dtype='uint8')
filled_after = after.copy()
for c in contours:
area = cv2.contourArea(c)
if area > 40:
x,y,w,h = cv2.boundingRect(c)
cv2.rectangle(before, (x, y), (x + w, y + h), (36,255,12), 2)
cv2.rectangle(after, (x, y), (x + w, y + h), (36,255,12), 2)
cv2.rectangle(diff_box, (x, y), (x + w, y + h), (36,255,12), 2)
cv2.drawContours(mask, [c], 0, (255,255,255), -1)
cv2.drawContours(filled_after, [c], 0, (0,255,0), -1)
cv2.imshow('20241114', before)
cv2.imshow('20241115', after)
cv2.imshow('diff', diff)
cv2.imshow('diff_box', diff_box)
cv2.imshow('mask', mask)
cv2.imshow('filled after', filled_after)
cv2.waitKey()
