aima-python/tests/test_perception4e.py at master · dopecoder/aima-python · GitHub

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import random

import pytest

from perception4e import *

from PIL import Image

import numpy as np

import os

random.seed("aima-python")

def test_array_normalization():

assert list(array_normalization([1, 2, 3, 4, 5], 0, 1)) == [0, 0.25, 0.5, 0.75, 1]

assert list(array_normalization([1, 2, 3, 4, 5], 1, 2)) == [1, 1.25, 1.5, 1.75, 2]

def test_sum_squared_difference():

image = Image.open(os.path.abspath("./images/broxrevised.png"))

arr = np.asarray(image)

arr1 = arr[10:500, :514]

arr2 = arr[10:500, 514:1028]

assert sum_squared_difference(arr1, arr1)[1] == 0

assert sum_squared_difference(arr1, arr1)[0] == (0, 0)

assert sum_squared_difference(arr1, arr2)[1] > 200000

def test_gen_gray_scale_picture():

assert list(gen_gray_scale_picture(size=3, level=3)[0]) == [0, 125, 250]

assert list(gen_gray_scale_picture(size=3, level=3)[1]) == [125, 125, 250]

assert list(gen_gray_scale_picture(size=3, level=3)[2]) == [250, 250, 250]

assert list(gen_gray_scale_picture(2, level=2)[0]) == [0, 250]

assert list(gen_gray_scale_picture(2, level=2)[1]) == [250, 250]

def test_generate_edge_weight():

assert generate_edge_weight(gray_scale_image, (0, 0), (2, 2)) == 5

assert generate_edge_weight(gray_scale_image, (1, 0), (0, 1)) == 255

def test_graph_bfs():

graph = Graph(gray_scale_image)

assert graph.bfs((1, 1), (0, 0), []) == False

parents = []

assert graph.bfs((0, 0), (2, 2), parents)

assert len(parents) == 8

def test_graph_min_cut():

image = gen_gray_scale_picture(size=3, level=2)

graph = Graph(image)

assert len(graph.min_cut((0, 0), (2, 2))) == 4

image = gen_gray_scale_picture(size=10, level=2)

graph = Graph(image)

assert len(graph.min_cut((0, 0), (9, 9))) == 10

def test_gen_discs():

discs = gen_discs(100, 2)

assert len(discs) == 2

assert len(discs[1]) == len(discs[0]) == 8

def test_simple_convnet():

train, val, test = load_MINST(1000, 100, 10)

model = simple_convnet()

model.fit(train[0], train[1], validation_data=(val[0], val[1]), epochs=5, verbose=2, batch_size=32)

scores = model.evaluate(test[0], test[1], verbose=1)

assert scores[1] > 0.2

def test_ROIPoolingLayer():

# Create feature map input

feature_maps_shape = (200, 100, 1)

feature_map = np.ones(feature_maps_shape, dtype='float32')

feature_map[200 - 1, 100 - 3, 0] = 50

roiss = np.asarray([[0.5, 0.2, 0.7, 0.4], [0.0, 0.0, 1.0, 1.0]])

assert pool_rois(feature_map, roiss, 3, 7)[0].tolist() == [[1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1],

[1, 1, 1, 1, 1, 1, 1]]

assert pool_rois(feature_map, roiss, 3, 7)[1].tolist() == [[1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1],

[1, 1, 1, 1, 1, 1, 50]]

if __name__ == '__main__':

pytest.main()

aima-python/tests/test_perception4e.py at master · dopecoder/aima-python · GitHub

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