I'm trying to implement the code showed here http://answers.opencv.org/answers/1051/revisions/ to do face recognition. The code is as follows:
!/usr/bin/env python
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Copyright (c) 2012, Philipp Wagner <bytefish[at]gmx[dot]de>.
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import os import sys import cv2 import numpy as np
def normalize(X, low, high, dtype=None):
"""Normalizes a given array in X to a value between low and high."""
X = np.asarray(X)
minX, maxX = np.min(X), np.max(X)
# normalize to [0...1].
X = X - float(minX)
X = X / float((maxX - minX))
# scale to [low...high].
X = X * (high-low)
X = X + low
if dtype is None:
return np.asarray(X)
return np.asarray(X, dtype=dtype)
def read_images(path, sz=None): """Reads the images in a given folder, resizes images on the fly if size is given.
Args:
path: Path to a folder with subfolders representing the subjects (persons).
sz: A tuple with the size Resizes
Returns:
A list [X,y]
X: The images, which is a Python list of numpy arrays.
y: The corresponding labels (the unique number of the subject, person) in a Python list.
"""
c = 0
X,y = [], []
for dirname, dirnames, filenames in os.walk(path):
for subdirname in dirnames:
subject_path = os.path.join(dirname, subdirname)
for filename in os.listdir(subject_path):
try:
im = cv2.imread(os.path.join(subject_path, filename), cv2.IMREAD_GRAYSCALE)
# resize to given size (if given)
if (sz is not None):
im = cv2.resize(im, sz)
X.append(np.asarray(im, dtype=np.uint8))
y.append(c)
except IOError, (errno, strerror):
print "I/O error({0}): {1}".format(errno, strerror)
except:
print "Unexpected error:", sys.exc_info()[0]
raise
c = c+1
return [X,y]
if __name__ == "__main__": # This is where we write the images, if an output_dir is given # in command line: out_dir = resultado # You'll need at least a path to your image data, please see # the tutorial coming with this source code on how to prepare # your image data: if len(sys.argv) < 2: print "USAGE: facerec_demo.py </images> [</resultado>]" sys.exit() # Now read in the image data. This must be a valid path! [X,y] = read_images(sys.argv[1]) # A line like: # [X,y] = read_images(path=sys.argv[1],sz=(70,70)) # # Will resize all images to 70x70 pixels. This is necessary for some # algorithms (Eigenfaces, Fisherfaces), so apply it if the size of # your images differ. # # Convert labels to 32bit integers. This is a workaround for 64bit machines, # because the labels will truncated else. This will be fixed in code as # soon as possible, so Python users don't need to know about this. # Thanks to Leo Dirac for reporting: y = np.asarray(y, dtype=np.int32) # If a out_dir is given, set it: if len(sys.argv) == 3: out_dir = sys.argv[2] # Create the Eigenfaces model. We are going to use the default # parameters for this simple example, please read the documentation # for thresholding: model = cv2.createFisherFaceRecognizer() # Read # Learn the model. Remember our function returns Python lists, # so we use np.asarray to turn them into NumPy lists to make # the OpenCV wrapper happy: model.train(np.asarray(X), np.asarray(y)) # We now get a prediction from the model! In reality you # should always use unseen images for testing your model. # But so many people were confused, when I sliced an image # off in the C++ version, so I am just using an image we # have trained with. # # model.predict is going to return the predicted label and # the associated confidence: [p_label, p_confidence] = model.predict(np.asarray(X[0])) # Print it: print "Predicted label = %d (confidence=%.2f)" % (p_label, p_confidence) # Cool! Finally we'll plot the Eigenfaces, because that's # what most people read in the papers are keen to see. # # Just like in C++ you have access to all model internal # data, because the cv::FaceRecognizer is a cv::Algorithm. # # You can see the available parameters with getParams(): print model.getParams() # Now let's get some data: mean = model.getMat("mean") eigenvectors = model.getMat("eigenvectors") cv2.imwrite("test.png", X[0]) # We'll save the mean, by first normalizing it: mean_norm = normalize(mean, 0, 255, dtype=np.uint8) mean_resized = mean_norm.reshape(X[0].shape) if out_dir is None: cv2.imshow("mean", mean_resized) else: cv2.imwrite("%s/mean.png" % (out_dir), mean_resized) # Turn the first (at most) 16 eigenvectors into grayscale # images. You could also use cv::normalize here, but sticking # to NumPy is much easier for now. # Note: eigenvectors are stored by column: for i in xrange(len(np.unique(y))-1): eigenvector_i = eigenvectors[:,i].reshape(X[0].shape) eigenvector_i_norm = normalize(eigenvector_i, 0, 255, dtype=np.uint8) eigenvector_i_colormap = cv2.applyColorMap(eigenvector_i_norm, cv2.COLORMAP_JET) # Show or save the images: if out_dir is None: cv2.imshow("%s/fisherface_%d" % (out_dir,i), eigenvector_i_colormap) else: cv2.imwrite("%s/fisherface_%d.png" % (out_dir,i), eigenvector_i_colormap) # Show the images: if out_dir is None: cv2.waitKey(0)
I have two folders, images and resultado, inside images I have folders person1 and person2, inside them I have pictures. first question: can the type of my image data be .jpg? second question: in this line [X,y] = read_images(path=sys.argv[1],sz=(70,70)) what is sys.argv[1]? Should I replace it by "images" because my data is there? When I try to run the program I find the erro: File "facerec_demo.py", line 103 [X,y] = read_images(path=sys.argv[1],sz=(70,70)) ^ IndentationError: unexpected indent
third question: I want my program take a image and compare with the data. Do this line do that?cv2.imwrite("test.png", X[0]).
