ValueError: need at least one array to concatenate

Publish date: 2023-12-19

I am having issues with

ValueError: need at least one array to concatenate

Below is the whole error message.

 Training mode Traceback (most recent call last): File "bcf.py", line 342, in <module> bcf.train() File "bcf.py", line 321, in train self._learn_codebook() File "bcf.py", line 142, in _learn_codebook feats_sc = np.concatenate(feats_sc, axis=1).transpose() ValueError: need at least one array to concatenate 

Below is the area of the problem.

 def _learn_codebook(self): MAX_CFS = 800 # max number of contour fragments per image; if above, sample randomly CLUSTERING_CENTERS = 1500 feats_sc = [] for image in self.data.values(): feats = image['cfs'] feat_sc = feats[1] if feat_sc.shape[1] > MAX_CFS: # Sample MAX_CFS from contour fragments rand_indices = np.random.permutation(feat_sc.shape[1]) feat_sc = feat_sc[:, rand_indices[:MAX_CFS]] feats_sc.append(feat_sc) feats_sc = np.concatenate(feats_sc, axis=1).transpose() print("Running KMeans...") self.kmeans = sklearn.cluster.KMeans(min(CLUSTERING_CENTERS, feats_sc.shape[0]), n_jobs=-1, algorithm='elkan').fit(feats_sc) print("Saving codebook...") self._save_kmeans(self.kmeans) return self.kmeans 

Below is the complete CLASS

class BCF(): def __init__(self): self.DATA_DIR = "/Users/minniemouse/TRAIN/bcf-master5/data/cuauv/" self.PERC_TRAINING_PER_CLASS = 0.5 self.CODEBOOK_FILE = "codebook.data" self.CLASSIFIER_FILE = "classifier" self.LABEL_TO_CLASS_MAPPING_FILE = "labels_to_classes.data" self.classes = defaultdict(list) self.data = defaultdict(dict) self.counter = defaultdict(int) self.kmeans = None self.clf = None self.label_to_class_mapping = None def _load_classes(self): for dir_name, subdir_list, file_list in os.walk(self.DATA_DIR): if subdir_list: continue for f in sorted(file_list, key=hash): self.classes[dir_name.split('/')[-1]].append(os.path.join(dir_name, f)) def _load_training(self): for cls in self.classes: images = self.classes[cls] for image in images[:int(len(images) * self.PERC_TRAINING_PER_CLASS)]: image_id = self._get_image_identifier(cls) self.data[image_id]['image'] = cv2.imread(image, cv2.IMREAD_GRAYSCALE) if self.data[image_id]['image'] is None: print("Failed to load " + image) def _load_testing(self): for cls in self.classes: images = self.classes[cls] for image in images[int(len(images) * self.PERC_TRAINING_PER_CLASS):]: image_id = self._get_image_identifier(cls) self.data[image_id]['image'] = cv2.imread(image, cv2.IMREAD_GRAYSCALE) if self.data[image_id]['image'] is None: print("Failed to load " + image) def _load_single(self, image): # Load single image data self.data.clear() image_id = self._get_image_identifier(None) self.data[image_id]['image'] = image def _save_label_to_class_mapping(self): self.label_to_class_mapping = {hash(cls): cls for cls in self.classes} with open(self.LABEL_TO_CLASS_MAPPING_FILE, 'wb') as out_file: pickle.dump(self.label_to_class_mapping, out_file, -1) def _load_label_to_class_mapping(self): if self.label_to_class_mapping is None: with open(self.LABEL_TO_CLASS_MAPPING_FILE, 'rb') as in_file: self.label_to_class_mapping = pickle.load(in_file) return self.label_to_class_mapping def _normalize_shapes(self): for (cls, idx) in self.data.keys(): image = self.data[(cls, idx)]['image'] # Remove void space y, x = np.where(image > 50) max_y = y.max() min_y = y.min() max_x = x.max() min_x = x.min() trimmed = image[min_y:max_y, min_x:max_x] > 50 trimmed = trimmed.astype('uint8') trimmed[trimmed > 0] = 255 self.data[(cls, idx)]['normalized_image'] = trimmed def _extract_cf(self): for (cls, idx) in self.data.keys(): image = self.data[(cls, idx)]['normalized_image'] images,contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) contour = sorted(contours, key=len)[-1] mat = np.zeros(image.shape, np.int8) cv2.drawContours(mat, [contour], -1, (255, 255, 255)) #self.show(mat) MAX_CURVATURE = 1.5 N_CONTSAMP = 50 N_PNTSAMP = 10 C = None for pnt in contour: if C is None: C = np.array([[pnt[0][0], pnt[0][1]]]) else: C = np.append(C, [[pnt[0][0], pnt[0][1]]], axis=0) cfs = self._extr_raw_points(C, MAX_CURVATURE, N_CONTSAMP, N_PNTSAMP) tmp = mat.copy() for cf in cfs: for pnt in cf: cv2.circle(tmp, (pnt[0], pnt[1]), 2, (255, 0, 0)) #self.show(tmp) num_cfs = len(cfs) print("Extracted %s points" % (num_cfs)) feat_sc = np.zeros((300, num_cfs)) xy = np.zeros((num_cfs, 2)) for i in range(num_cfs): cf = cfs[i] sc, _, _, _ = shape_context(cf) # shape context is 60x5 (60 bins at 5 reference points) sc = sc.flatten(order='F') sc /= np.sum(sc) # normalize feat_sc[:, i] = sc # shape context descriptor sc for each cf is 300x1 # save a point at the midpoint of the contour fragment xy[i, 0:2] = cf[np.round(len(cf) / 2. - 1).astype('int32'), :] sz = image.shape self.data[(cls, idx)]['cfs'] = (cfs, feat_sc, xy, sz) def _learn_codebook(self): MAX_CFS = 800 # max number of contour fragments per image; if above, sample randomly CLUSTERING_CENTERS = 1500 feats_sc = [] for image in self.data.values(): feats = image['cfs'] feat_sc = feats[1] if feat_sc.shape[1] > MAX_CFS: # Sample MAX_CFS from contour fragments rand_indices = np.random.permutation(feat_sc.shape[1]) feat_sc = feat_sc[:, rand_indices[:MAX_CFS]] feats_sc.append(feat_sc) feats_sc = np.concatenate(feats_sc, axis=1).transpose() print("Running KMeans...") self.kmeans = sklearn.cluster.KMeans(min(CLUSTERING_CENTERS, feats_sc.shape[0]), n_jobs=-1, algorithm='elkan').fit(feats_sc) print("Saving codebook...") self._save_kmeans(self.kmeans) return self.kmeans 

I have read through the various posts on ValueError already described, but I am not having much luck on figuring it out. I have now attached the CLASS and full error message information.

Please, can someone point out what I am missing?

Thank you

2 Answers

the problem comes from the lenght of your array. Check if your array/list is longer than to 0 print(len(feats_sc)).

Don't forget to checkout the documentation numpy.concatenate — NumPy v1.16 Manual

The problem seems to be in np.concatenate where it expects an array of arrays and it's not receiving that.

Refer: Scipy docs

numpy.concatenate((a1, a2, ...), axis=0, out=None) 

Join a sequence of arrays along an existing axis.

Parameters:
a1, a2, … : sequence of array_like The arrays must have the same shape, except in the dimension corresponding to axis (the first, by default).

axis : int, optional The axis along which the arrays will be joined. If axis is None, arrays are flattened before use. Default is 0.

out : ndarray, optional If provided, the destination to place the result. The shape must be correct, matching that of what concatenate would have returned if no out argument were specified.

Returns: res : ndarray The concatenated array.

In your case, check what feats_sc contains.

You can debug using pdb

python -m pdb <your-code>.py (pdb) b fullpath/to/your-code.py:line-number-to-break (pdb) c 

• c will continue until break point in encountered
• n will move to next line
• b is to set break point
• q is to quit

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