numpy/core/ undefined symbol: cblas_sgemm – Raspberry Pi

While working on a Raspberry Pi image that had been used prior by an electrical engineer to setup all of the dependencies for the hardware, there was an error when trying to upgrade to use Tensorflow. Tensorflow was needed to run a model trained with Cognitive Services: Custom Vision Service. The error was when the script imported Numpy. That caused the following error:

numpy/core/ undefined symbol: cblas_sgemm

To remedy this, all of the installations of Numpy had to be uninstalled. The following commands were run:

  • apt-get remove python-numpy
  • apt-get remove python3-numpy
  • pip3 uninstall numpy

After all three of those commands complete, Numpy was reinstalled using the package provided for raspian:

apt-get install python3-numpy

Multiple TensorFlow Graphs from Cognitive Services – Custom Vision Service

For one project, there was a need for multiple models within the same Python application. These models were trained using the Cognitive Services: Custom Vision Service. There are two steps to using an exported model:

  1. Prepare the image
  2. Classify the image

Prepare an image for prediction

from PIL import Image
import numpy as np
import cv2
def convert_to_opencv(image):
# RGB -> BGR conversion is performed as well.
image = image.convert('RGB')
r,g,b = np.array(image).T
opencv_image = np.array([b,g,r]).transpose()
return opencv_image
def crop_center(img,cropx,cropy):
h, w = img.shape[:2]
startx = w//2(cropx//2)
starty = h//2(cropy//2)
return img[starty:starty+cropy, startx:startx+cropx]
def resize_down_to_1600_max_dim(image):
h, w = image.shape[:2]
if (h < 1600 and w < 1600):
return image
new_size = (1600 * w // h, 1600) if (h > w) else (1600, 1600 * h // w)
return cv2.resize(image, new_size, interpolation = cv2.INTER_LINEAR)
def resize_to_256_square(image):
h, w = image.shape[:2]
return cv2.resize(image, (256, 256), interpolation = cv2.INTER_LINEAR)
def update_orientation(image):
exif_orientation_tag = 0x0112
if hasattr(image, '_getexif'):
exif = image._getexif()
if (exif != None and exif_orientation_tag in exif):
orientation = exif.get(exif_orientation_tag, 1)
# orientation is 1 based, shift to zero based and flip/transpose based on 0-based values
orientation -= 1
if orientation >= 4:
image = image.transpose(Image.TRANSPOSE)
if orientation == 2 or orientation == 3 or orientation == 6 or orientation == 7:
image = image.transpose(Image.FLIP_TOP_BOTTOM)
if orientation == 1 or orientation == 2 or orientation == 5 or orientation == 6:
image = image.transpose(Image.FLIP_LEFT_RIGHT)
return image
def prepare_image(image):
# Update orientation based on EXIF tags, if the file has orientation info.
image = update_orientation(image)
# Convert to OpenCV format
image = convert_to_opencv(image)
# If the image has either w or h greater than 1600 we resize it down respecting
# aspect ratio such that the largest dimension is 1600
image = resize_down_to_1600_max_dim(image)
# We next get the largest center square
h, w = image.shape[:2]
min_dim = min(w,h)
max_square_image = crop_center(image, min_dim, min_dim)
# Resize that square down to 256×256
augmented_image = resize_to_256_square(max_square_image)
augmented_image = crop_center(augmented_image, 244, network_input_size)
return augmented_image

Classify the image

To run multiple models in Python was fairly simple. Simply call tf.reset_default_graph() after saving the loaded session into memory.

import tensorflow as tf
import numpy as np
# The category name and probability percentage
class CategoryScore:
def __init__(self, category, probability: float):
self.category = category
self.probability = probability
# The categorizer handles running tensorflow models
class Categorizer:
def __init__(self, model_file_path: str, map: []): = map
self.graph = tf.Graph()
self.graph_def = self.graph.as_graph_def()
with tf.gfile.GFile(model_file_path, 'rb') as f:
tf.import_graph_def(self.graph_def, name='')
output_layer = 'loss:0'
self.input_node = 'Placeholder:0'
self.sess = tf.Session()
self.prob_tensor = self.sess.graph.get_tensor_by_name(output_layer)
def score(self, image):
predictions, =, {self.input_node: [image]})
label_index = 0
scores = []
for p in predictions:
category_score = CategoryScore([label_index],np.float64(np.round(p, 8)))
label_index += 1
return scores

After the CustomVisionCategorizer is create, just call score and it will score with the labels in the map.