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 ValueError: could not broadcast input array from shape (75) into shape (25) route2sabya Unladen Swallow Posts: 1 Threads: 1 Joined: Mar 2019 Reputation: 0 Likes received: 0 #1 Mar-14-2019, 01:14 PM (This post was last modified: Mar-14-2019, 01:15 PM by route2sabya. Edited 2 times in total.) I am a Python beginner. I am trying to follow the tutorial posted here about change detection for satellite images. I am receiving a value error. Can anyone guide me towards resolving the issue. ```import cv2 import numpy as np from sklearn.cluster import KMeans from sklearn.decomposition import PCA from collections import Counter from scipy.misc import imread, imresize, imsave def find_vector_set(diff_image, new_size): i = 0 j = 0 vector_set = np.zeros(((int(new_size[0]) * int(new_size[1])) // 25, 25)) while i < vector_set.shape[0]: while j < new_size[0]: k = 0 while k < new_size[1]: block = diff_image[j:j+5, k:k+5] feature = block.ravel() vector_set[i, :] = feature k = k + 5 j = j + 5 i = i + 1 print ('vector_set shape', vector_set.shape) mean_vec = np.mean(vector_set, axis = 0) vector_set = vector_set - mean_vec return vector_set, mean_vec def find_FVS(EVS, diff_image, mean_vec, new): i = 2 feature_vector_set = [] while i < new[0] - 2: j = 2 while j < new[1] - 2: block = diff_image[i-2:i+3, j-2:j+3] feature = block.flatten() feature_vector_set.append(feature) j = j+1 i = i+1 FVS = np.dot(feature_vector_set, EVS) FVS = FVS - mean_vec print ("feature vector space size", FVS.shape) return FVS def clustering(FVS, components, new): kmeans = KMeans(components, verbose = 0) kmeans.fit(FVS) output = kmeans.predict(FVS) count = Counter(output) least_index = min(count, key = count.get) change_map = np.reshape(output,(new[0] - 4, new[1] - 4)) return least_index, change_map def find_PCAKmeans(imagepath1, imagepath2): print ('Operating') image1 = imread(imagepath1) image2 = imread(imagepath2) new_size = np.asarray(image1.shape) // 25 image1 = imresize(image1, (new_size)).astype(np.int16) image2 = imresize(image2, (new_size)).astype(np.int16) diff_image = abs(image1 - image2) imsave('diff.jpg', diff_image) print ('Both images resized to ',new_size) vector_set, mean_vec = find_vector_set(diff_image, new_size) pca = PCA() pca.fit(vector_set) EVS = pca.components_ FVS = find_FVS(EVS, diff_image, mean_vec, new_size) print ('computing k means') components = 3 least_index, change_map = clustering(FVS, components, new_size) change_map[change_map == least_index] = 255 change_map[change_map != 255] = 0 change_map = change_map.astype(np.uint8) kernel = np.asarray(((0,0,1,0,0), (0,1,1,1,0), (1,1,1,1,1), (0,1,1,1,0), (0,0,1,0,0)), dtype=np.uint8) cleanChangeMap = cv2.erode(change_map,kernel) imsave("changemap.jpg", change_map) imsave("cleanchangemap.jpg", cleanChangeMap) if __name__ == "__main__": a = 'Argentina_01131994_md.jpg' b = 'Argentina_01202014_md.jpg' find_PCAKmeans(a,b) ```OUTPUT : File "D:/Numer8/change_detection4.py", line 26, in find_vector_set vector_set[i, :] = feature ValueError: could not broadcast input array from shape (75) into shape (25) « Next Oldest | Next Newest »

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