aima-python/perception4e.py at master · aaronsgithub/aima-python · GitHub

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"""Perception (Chapter 24)"""

import cv2

import keras

import matplotlib.pyplot as plt

import numpy as np

import scipy.signal

from keras.datasets import mnist

from keras.layers import Dense, Activation, Flatten, InputLayer, Conv2D, MaxPooling2D

from keras.models import Sequential

from utils4e import gaussian_kernel_2D

# ____________________________________________________

# 24.3 Early Image Processing Operators

# 24.3.1 Edge Detection

def array_normalization(array, range_min, range_max):

"""Normalize an array in the range of (range_min, range_max)"""

if not isinstance(array, np.ndarray):

array = np.asarray(array)

array = array - np.min(array)

array = array * (range_max - range_min) / np.max(array) + range_min

return array

def gradient_edge_detector(image):

"""

Image edge detection by calculating gradients in the image

:param image: numpy ndarray or an iterable object

:return: numpy ndarray, representing a gray scale image

"""

if not isinstance(image, np.ndarray):

image = np.asarray(image)

# gradient filters of x and y direction edges

x_filter, y_filter = np.array([[1, -1]]), np.array([[1], [-1]])

# convolution between filter and image to get edges

y_edges = scipy.signal.convolve2d(image, x_filter, 'same')

x_edges = scipy.signal.convolve2d(image, y_filter, 'same')

edges = array_normalization(x_edges + y_edges, 0, 255)

return edges

def gaussian_derivative_edge_detector(image):

"""Image edge detector using derivative of gaussian kernels"""

if not isinstance(image, np.ndarray):

image = np.asarray(image)

gaussian_filter = gaussian_kernel_2D()

# init derivative of gaussian filters

x_filter = scipy.signal.convolve2d(gaussian_filter, np.asarray([[1, -1]]), 'same')

y_filter = scipy.signal.convolve2d(gaussian_filter, np.asarray([[1], [-1]]), 'same')

# extract edges using convolution

y_edges = scipy.signal.convolve2d(image, x_filter, 'same')

x_edges = scipy.signal.convolve2d(image, y_filter, 'same')

edges = array_normalization(x_edges + y_edges, 0, 255)

return edges

def laplacian_edge_detector(image):

"""Extract image edge with laplacian filter"""

if not isinstance(image, np.ndarray):

image = np.asarray(image)

# init laplacian filter

laplacian_kernel = np.asarray([[0, -1, 0], [-1, 4, -1], [0, -1, 0]])

# extract edges with convolution

edges = scipy.signal.convolve2d(image, laplacian_kernel, 'same')

edges = array_normalization(edges, 0, 255)

return edges

def show_edges(edges):

""" helper function to show edges picture"""

plt.imshow(edges, cmap='gray', vmin=0, vmax=255)

plt.axis('off')

plt.show()

# __________________________________________________

# 24.3.3 Optical flow

def sum_squared_difference(pic1, pic2):

"""SSD of two frames"""

pic1 = np.asarray(pic1)

pic2 = np.asarray(pic2)

assert pic1.shape == pic2.shape

min_ssd = np.inf

min_dxy = (np.inf, np.inf)

# consider picture shift from -30 to 30

for Dx in range(-30, 31):

for Dy in range(-30, 31):

# shift the image

shifted_pic = np.roll(pic2, Dx, axis=0)

shifted_pic = np.roll(shifted_pic, Dy, axis=1)

# calculate the difference

diff = np.sum((pic1 - shifted_pic) ** 2)

if diff < min_ssd:

min_dxy = (Dx, Dy)

min_ssd = diff

return min_dxy, min_ssd

# ____________________________________________________

# segmentation

def gen_gray_scale_picture(size, level=3):

"""

Generate a picture with different gray scale levels

:param size: size of generated picture

:param level: the number of level of gray scales in the picture,

range (0, 255) are equally divided by number of levels

:return image in numpy ndarray type

"""

assert level > 0

# init an empty image

image = np.zeros((size, size))

if level == 1:

return image

# draw a square on the left upper corner of the image

for x in range(size):

for y in range(size):

image[x, y] += (250 // (level - 1)) * (max(x, y) * level // size)

return image

gray_scale_image = gen_gray_scale_picture(3)

def probability_contour_detection(image, discs, threshold=0):

"""

Detect edges/contours by applying a set of discs to an image

:param image: an image in type of numpy ndarray

:param discs: a set of discs/filters to apply to pixels of image

:param threshold: threshold to tell whether the pixel at (x, y) is on an edge

:return image showing edges in numpy ndarray type

"""

# init an empty output image

res = np.zeros(image.shape)

step = discs[0].shape[0]

for x_i in range(0, image.shape[0] - step + 1, 1):

for y_i in range(0, image.shape[1] - step + 1, 1):

diff = []

# apply each pair of discs and calculate the difference

for d in range(0, len(discs), 2):

disc1, disc2 = discs[d], discs[d + 1]

# crop the region of interest

region = image[x_i: x_i + step, y_i: y_i + step]

diff.append(np.sum(np.multiply(region, disc1)) - np.sum(np.multiply(region, disc2)))

if max(diff) > threshold:

# change color of the center of region

res[x_i + step // 2, y_i + step // 2] = 255

return res

def group_contour_detection(image, cluster_num=2):

"""

Detecting contours in an image with k-means clustering

:param image: an image in numpy ndarray type

:param cluster_num: number of clusters in k-means

"""

img = image

Z = np.float32(img)

criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)

K = cluster_num

# use kmeans in opencv-python

ret, label, center = cv2.kmeans(Z, K, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)

center = np.uint8(center)

res = center[label.flatten()]

res2 = res.reshape(img.shape)

# show the image

# cv2.imshow('res2', res2)

# cv2.waitKey(0)

# cv2.destroyAllWindows()

return res2

def image_to_graph(image):

"""

Convert an image to an graph in adjacent matrix form

"""

graph_dict = {}

for x in range(image.shape[0]):

for y in range(image.shape[1]):

graph_dict[(x, y)] = [(x + 1, y) if x + 1 < image.shape[0] else None,

(x, y + 1) if y + 1 < image.shape[1] else None]

return graph_dict

def generate_edge_weight(image, v1, v2):

"""

Find edge weight between two vertices in an image

:param image: image in numpy ndarray type

:param v1, v2: verticles in the image in form of (x index, y index)

"""

diff = abs(image[v1[0], v1[1]] - image[v2[0], v2[1]])

return 255 - diff

class Graph:

"""Graph in adjacent matrix to represent an image"""

def __init__(self, image):

"""image: ndarray"""

self.graph = image_to_graph(image)

# number of columns and rows

self.ROW = len(self.graph)

self.COL = 2

self.image = image

# dictionary to save the maximum flow of each edge

self.flow = {}

# initialize the flow

for s in self.graph:

self.flow[s] = {}

for t in self.graph[s]:

if t:

self.flow[s][t] = generate_edge_weight(image, s, t)

def bfs(self, s, t, parent):

"""Breadth first search to tell whether there is an edge between source and sink

parent: a list to save the path between s and t"""

# queue to save the current searching frontier

queue = [s]

visited = []

while queue:

u = queue.pop(0)

for node in self.graph[u]:

# only select edge with positive flow

if node not in visited and node and self.flow[u][node] > 0:

queue.append(node)

visited.append(node)

parent.append((u, node))

return True if t in visited else False

def min_cut(self, source, sink):

"""Find the minimum cut of the graph between source and sink"""

parent = []

max_flow = 0

while self.bfs(source, sink, parent):

path_flow = np.inf

# find the minimum flow of s-t path

for s, t in parent:

path_flow = min(path_flow, self.flow[s][t])

max_flow += path_flow

# update all edges between source and sink

for s in self.flow:

for t in self.flow[s]:

if t[0] <= sink[0] and t[1] <= sink[1]:

self.flow[s][t] -= path_flow

parent = []

res = []

for i in self.flow:

for j in self.flow[i]:

if self.flow[i][j] == 0 and generate_edge_weight(self.image, i, j) > 0:

res.append((i, j))

return res

def gen_discs(init_scale, scales=1):

"""

Generate a collection of disc pairs by splitting an round discs with different angles

:param init_scale: the initial size of each half discs

:param scales: scale number of each type of half discs, the scale size will be doubled each time

:return: the collection of generated discs: [discs of scale1, discs of scale2...]

"""

discs = []

for m in range(scales):

scale = init_scale * (m + 1)

disc = []

# make the full empty dist

white = np.zeros((scale, scale))

center = (scale - 1) / 2

for i in range(scale):

for j in range(scale):

if (i - center) ** 2 + (j - center) ** 2 <= (center ** 2):

white[i, j] = 255

# generate lower half and upper half

lower_half = np.copy(white)

lower_half[:(scale - 1) // 2, :] = 0

upper_half = lower_half[::-1, ::-1]

# generate left half and right half

disc += [lower_half, upper_half, np.transpose(lower_half), np.transpose(upper_half)]

# generate upper-left, lower-right, upper-right, lower-left half discs

disc += [np.tril(white, 0), np.triu(white, 0), np.flip(np.tril(white, 0), axis=0),

np.flip(np.triu(white, 0), axis=0)]

discs.append(disc)

return discs

# __________________________________________________

# 24.4 Classifying Images

def load_MINST(train_size, val_size, test_size):

"""Load MINST dataset from keras"""

(x_train, y_train), (x_test, y_test) = mnist.load_data()

total_size = len(x_train)

if train_size + val_size > total_size:

train_size = total_size - val_size

x_train = x_train.reshape(x_train.shape[0], 1, 28, 28)

x_test = x_test.reshape(x_test.shape[0], 1, 28, 28)

x_train = x_train.astype('float32')

x_train /= 255

test_x = x_test.astype('float32')

test_x /= 255

y_train = keras.utils.to_categorical(y_train, 10)

y_test = keras.utils.to_categorical(y_test, 10)

return ((x_train[:train_size], y_train[:train_size]),

(x_train[train_size:train_size + val_size], y_train[train_size:train_size + val_size]),

(x_test[:test_size], y_test[:test_size]))

def simple_convnet(size=3, num_classes=10):

"""

Simple convolutional network for digit recognition

:param size: number of convolution layers

:param num_classes: number of output classes

:return a convolution network in keras model type

"""

model = Sequential()

# add input layer for images of size (28, 28)

model.add(InputLayer(input_shape=(1, 28, 28)))

# add convolution layers and max pooling layers

for _ in range(size):

model.add(Conv2D(32, (2, 2), padding='same', kernel_initializer='random_uniform'))

model.add(MaxPooling2D(padding='same'))

# add flatten layer and output layers

model.add(Flatten())

model.add(Dense(num_classes))

model.add(Activation('softmax'))

# compile model

model.compile(loss='categorical_crossentropy',

metrics=['accuracy'])

print(model.summary())

return model

def train_model(model):

"""Train the simple convolution network"""

# load dataset

(train_x, train_y), (val_x, val_y), (test_x, test_y) = load_MINST(1000, 100, 100)

model.fit(train_x, train_y, validation_data=(val_x, val_y), epochs=5, verbose=2, batch_size=32)

scores = model.evaluate(test_x, test_y, verbose=1)

print(scores)

return model

# _____________________________________________________

# 24.5 DETECTING OBJECTS

def selective_search(image):

"""

Selective search for object detection

:param image: str, the path of image or image in ndarray type with 3 channels

:return list of bounding boxes, each element is in form of [x_min, y_min, x_max, y_max]

"""

if not image:

im = cv2.imread("./images/stapler1-test.png")

elif isinstance(image, str):

im = cv2.imread(image)

else:

im = np.stack(image * 3, axis=-1)

# use opencv python to extract bounding box with selective search

ss = cv2.ximgproc.segmentation.createSelectiveSearchSegmentation()

ss.setBaseImage(im)

ss.switchToSelectiveSearchQuality()

rects = ss.process()

# show bounding boxes with the input image

image_out = im.copy()

for rect in rects[:100]:

print(rect)

x, y, w, h = rect

cv2.rectangle(image_out, (x, y), (x + w, y + h), (0, 255, 0), 1, cv2.LINE_AA)

cv2.imshow("Output", image_out)

cv2.waitKey(0)

return rects

# faster RCNN

def pool_rois(feature_map, rois, pooled_height, pooled_width):

"""

Applies ROI pooling for a single image and various ROIs

:param feature_map: ndarray, in shape of (width, height, channel)

:param rois: list of roi

:param pooled_height: height of pooled area

:param pooled_width: width of pooled area

:return list of pooled features

"""

def curried_pool_roi(roi):

return pool_roi(feature_map, roi, pooled_height, pooled_width)

pooled_areas = list(map(curried_pool_roi, rois))

return pooled_areas

def pool_roi(feature_map, roi, pooled_height, pooled_width):

"""

Applies a single ROI pooling to a single image

:param feature_map: ndarray, in shape of (width, height, channel)

:param roi: region of interest, in form of [x_min_ratio, y_min_ratio, x_max_ratio, y_max_ratio]

:return feature of pooling output, in shape of (pooled_width, pooled_height)

"""

# Compute the region of interest

feature_map_height = int(feature_map.shape[0])

feature_map_width = int(feature_map.shape[1])

h_start = int(feature_map_height * roi[0])

w_start = int(feature_map_width * roi[1])

h_end = int(feature_map_height * roi[2])

w_end = int(feature_map_width * roi[3])

region = feature_map[h_start:h_end, w_start:w_end, :]

# Divide the region into non overlapping areas

region_height = h_end - h_start

region_width = w_end - w_start

h_step = region_height // pooled_height

w_step = region_width // pooled_width

areas = [[(

i * h_step,

j * w_step,

(i + 1) * h_step if i + 1 < pooled_height else region_height,

(j + 1) * w_step if j + 1 < pooled_width else region_width)

for j in range(pooled_width)]

for i in range(pooled_height)]

# take the maximum of each area and stack the result

def pool_area(x):

return np.max(region[x[0]:x[2], x[1]:x[3], :])

pooled_features = np.stack([[pool_area(x) for x in row] for row in areas])

return pooled_features

# faster rcnn demo can be installed and shown in jupyter notebook

# def faster_rcnn_demo(directory):

# """

# show the demo of rcnn, the model is from

# @inproceedings{renNIPS15fasterrcnn,

# Author = {Shaoqing Ren and Kaiming He and Ross Girshick and Jian Sun},

# Title = {Faster {R-CNN}: Towards Real-Time Object Detection

# with Region Proposal Networks},

# Booktitle = {Advances in Neural Information Processing Systems ({NIPS})},

# Year = {2015}}

# :param directory: the directory where the faster rcnn model is installed

# """

# os.chdir(directory + '/lib')

# # make file

# os.system("make clean")

# os.system("make")

# # run demo

# os.chdir(directory)

# os.system("./tools/demo.py")

# return 0

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