def testRestart(self):
fitness = FitnessNapsack()
ilog = pv.ImageLog()
restart_dir = ilog.dir
#fitness.solve()
alg = ga.GeneticAlgorithm(fitness,[ga.GARanking(100)],n_processes=1)
result = alg.optimize(max_iter=500,ilog=ilog)
time.sleep(2)
#print "Restarting..."
ilog = pv.ImageLog()
alg = ga.GeneticAlgorithm(fitness,[ga.GARanking(100)],n_processes=1)
result = alg.optimize(max_iter=1000,ilog=ilog,restart_dir=restart_dir)
def test01_hsHist(self):
'''Test hue saturation histogram.'''
ilog = pv.ImageLog()
im = pv.Image(
os.path.join(pv.__path__[0], "data", "misc", "baboon.jpg"))
mask = np.zeros((512, 512), dtype=np.bool)
mask[150:200, 128:300] = True
m = pv.Image(1.0 * mask)
ilog(im)
ilog(m)
hist = hsHist(im, mask=m)
print hist
print dir(hist)
#print dir(hist.bins),hist.bins.channels
#for i in range(32):
# for j in range(30):
# print i,j,cv.QueryHistValue_2D(hist,j,i)
hist.rescaleMax(255)
print hist.asMatrix()
#print cv.SetHistBinRanges
back = hist.backProject(im)
ilog(back)
ilog.show()
def testBugsVideo(self):
ilog = pv.ImageLog()
#ilog = None
video = pv.FFMPEGVideo(BUGS_VIDEO)
i = 0
for frame in video:
if ilog != None:
print "Processing Frame:", i
#if frame != None:
ilog(frame, format='jpg')
i += 1
if ilog != None:
ilog.show()
tmp4.annotateLabel(pv.Point(10, 10), "ScaleTest: use_orig=True")
tmp4.annotateLabel(pv.Point(20, 20), "This image should be sharp.")
ilog.log(tmp4)
# Now remove the reverence to the im instance. The weak references within
# tmp1 do not hold onto the original data so now there is no choice but to
# use the scaled down image.
del im
tmp5 = aff_big.transformImage(tmp1, use_orig=True)
tmp5.annotateLabel(pv.Point(10, 10), "ScaleTest: use_orig=True")
tmp5.annotateLabel(pv.Point(20, 20), "This image should be blurry")
tmp5.annotateLabel(pv.Point(20, 30), "because the original has be")
tmp5.annotateLabel(pv.Point(20, 40), "removed from memory.")
ilog.log(tmp5)
# Weak references are used to prevent python from hanging onto larger images
# when the user deletes there references or they go out of scope. To
# use the original images further down the image pipeline make sure that
# they are expressly kept around. This feature is designed as a convenience
# so that the user does not have to keep track of many affine transforms and
# images, and can accumulate transforms as if each one goes all the way back
# to the original image if it is still available. It also does not interfere
# memory management if the user decides to drop references to large images.
if __name__ == '__main__':
ilog = pv.ImageLog()
Example1(ilog)
ilog.show()
# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import pyvision as pv
from pyvision.edge.canny import canny # An interface to the OpenCV Canny.
'''
This code is from part 1 of the PyVision Quick Start Guide.
'''
if __name__ == '__main__':
# (1) Load an image from a file.
im = pv.Image(pv.__path__[0] + "/data/nonface/NONFACE_16.jpg")
# (2) Rescale the image
im = pv.AffineScale(0.5, (320, 240)).transformImage(im)
# (3) Run the canny function to locate the edges.
edge_im1 = canny(im)
# (4) Run the canny function with different defaults.
edge_im2 = canny(im, threshold1=100, threshold2=250)
# (5) Save the results to a log.
ilog = pv.ImageLog("../..")
ilog.log(im, label="Source")
ilog.log(edge_im1, label="Canny1")
ilog.log(edge_im2, label="Canny2")
# (6) Display the results.
ilog.show()
# If an integer is passed to the sample option then subselect the image names.
if options.sample != None:
image_names = random.sample(image_names,options.sample)
# Open the file to use as output.
f = open(args[1],'wb')
csv_file = csv.writer(f)
headers = ['image_name','detect_number','detect_x','detect_y','detect_width','detect_height','eye1_x','eye1_y','eye2_x','eye2_y']
csv_file.writerow(headers)
# Create an image log if this is being saved to a file.
ilog = None
if options.log_dir != None:
print("Creating Image Log...")
ilog = pv.ImageLog(options.log_dir)
# For each image run face and eye detection
face_detect = CascadeDetector(image_scale=1.3*options.scale)
locate_eyes = FilterEyeLocator()#locator_filename)
c = 0
for pathname in image_names:
c += 1
im = pv.Image(pathname)
scale = options.log_scale
log_im = pv.AffineScale(scale,(int(scale*im.width),int(scale*im.height))).transformImage(im)
