def predictor():
for img in glob('/home/linuxsagar/tempTest/*'):
#List to hold the feature of new Image
_new_features = []
#Read the test image
_custom_image = Image(img)
#Extract features
_newHhfe = hhfe.extract(_custom_image)
_newEhfe = ehfe.extract(_custom_image)
_newHaarfe = haarfe.extract(_custom_image)
#List for the graph
hue_data = []
haar_data = []
edge_data = []
hue_fields = hhfe.getFieldNames()
for i in range(0, hhfe.getNumFields()):
hue_data.append(str(hue_fields[i]) + ' = ' + str(_newHhfe[i]))
edge_fields = ehfe.getFieldNames()
for i in range(0, hhfe.getNumFields()):
edge_data.append(str(edge_fields[i]) + ' = ' + str(_newEhfe[i]))
haar_fields = haarfe.getFieldNames()
for i in range(0, len(haar_fields)):
haar_data.append(str(haar_fields[i]) + ' = ' + str(_newHaarfe[i]))
#Concatinate all feature to one
_new_features.append(np.concatenate([_newHhfe, _newEhfe, _newHaarfe]))
probability = np.amax(classifier.predict_proba(_new_features))
#Call method to generate histogram image
make_histogram(_custom_image.histogram(), _newHhfe, _newEhfe,
_newHaarfe, dataDir)
#The Final result to view
result = [
"The given input is classified as: {}.".format(
classifier.predict(_new_features)[0]),
"Probability of prediction: {:.1%}".format((probability))
]
return [result, hue_data, edge_data, haar_data]
def classify(im, K, show=False):
""" classify - classify an image using k-means algorithm.
Arguments:
* im - an image (ndarray) or a filename.
* K - number of classes.
* show = False - if True, display the image and the classes.
Return:
* imClasses - the classes of the image (ndarray, 2D).
See also:
* kMeans - K-means algorithm.
"""
a = Image(im)
a = a.histogram(255)
# Load image
if type(im) is str:
im = plt.imread(im)
# Reshape and apply k-mean
values = im.reshape((im.shape[0] * im.shape[1], -1))
centers, classes = kMeans(values, K)
imClasses = classes.reshape((im.shape[0], im.shape[1]))
## imClasses.save("imClassesH.png")
## imClassesHl=image("imClassesH.png")
## imClassHisto=imClassesH1.histogram(255)
# Display
if show:
cmap = 'gray' if im.ndim < 3 else None
plt.subplot(131), plt.imshow(im, cmap=cmap), plt.title('Imagen Gray')
plt.subplot(132), plt.imshow(imClasses), plt.title('Segmentacion por Clases')
pylab.subplot(133), pylab.plot(a), pylab.draw(), pylab.pause(0.0001), pylab.title('Histograma Img Gray')
plt.show()
# The end
return imClasses
from SimpleCV import Image
# change this to process multiple images
img = Image("../images/beach.jpeg")
histogram = img.histogram()
print histogram.getFieldNames
* X - center of the classes, row by row (ndarray, 2D).
See also:
* kMeans - k-means classification.
* nearestNeighbor - nearest neighbor classification.
"""
# Check dimensions
if Y.ndim != 2:
raise Exception('data matrix must be a 2D ndarray')
if C.ndim != 1:
raise Exception('classes matrix must be a 1D ndarray')
if Y.shape[0] != C.shape[0]:
raise Exception('Y and C matrix must have the same number of rows')
# Create an empty array
K = C.max() + 1
X = np.ndarray((K, Y.shape[1]), Y.dtype)
# Compute barycenters
for k in range(K):
X[k,:] = Y[C == k,:].mean(0)
# Return them
return X
a = Image("hola5Gray.png")
b = a.histogram(255)
classify("hola5Gray.png", 2, show=True)
def getHistogram(imageFile):
im = Image(imageFile)
#im = im.toRGB()
im = im.toHSV()
return im.histogram(64)
from SimpleCV import Image
from SimpleCV.Shell import plot
img = Image('ex21a.jpg') # Open ex21b.jpg and ex21c.jpg too :)
# Generate the histogram with 256 bins, one for each color
histogram = img.histogram(256)
# Show how many elements are in the list
len(histogram)
# Graphically display the histogram
plot(histogram)
#!/usr/bin/python
from SimpleCV import Camera, Display, Image
import matplotlib.pyplot as plt
import time
cam = Camera()
img = cam.getImage().save("img.jpg")
img = Image("img.jpg")
img.show()
imgGray = img.grayscale().save("imgGray.jpg")
imgGray = Image("imgGray.jpg")
imgGray.show()
hist = imgGray.histogram(255)
(red, green, blue) = img.splitChannels(False)
red_histogram = red.histogram(255)
green_histogram = green.histogram(255)
blue_histogram = blue.histogram(255)
plt.figure(1)
plt.subplot(411)
plt.plot(hist)
plt.subplot(412)
plt.plot(red_histogram)
plt.subplot(413)
plt.plot(green_histogram)
plt.subplot(414)
plt.plot(blue_histogram)
plt.show()
print("Ingresar parametro para binarizar: ")
