def main(image_filename, cascade_filename, res_filename, use_multiscale = False): image = imageio.read(image_filename) haar_classifier = parse_haar.parse_haar_xml(cascade_filename) print str(haar_classifier) # parameters scale_factor = 1.2 min_size = (40, 40) # process image detected_faces = [] if use_multiscale: detected_faces = detect_faces_multiscale(image, haar_classifier, scale_factor, min_size) else: detected_faces = detect_faces(image, haar_classifier) res = visualisation.draw_faces(image, detected_faces) imageio.write(res_filename, res, 3)
def run_detector(block_size, implementation, image_filename, cascade_filename, res_filename_prefix):
from blip.simulator import interpreter
from blip.support import imageio
import violajones.reference
# first load the cascade
cascade = violajones.parse_haar.parse_haar_xml(cascade_filename)
print cascade
image = imageio.read(image_filename)
if not image: raise Exception('image %s not found or not supported'%image_filename)
print 'XXX histogram equalisation is not implemented yet, use violajones impl'
print ' before executing simulator'
image = violajones.reference.equalizeHist(image)
im_size = len(image[0]), len(image)
pe_dim = [s//b for s,b in zip(im_size, block_size)]
args = {'haar_classifier':cascade, 'pe_dim':pe_dim}
# now execute the codegen
code = Code()
code.set_generator(implementation, block_size, args)
#print '# instructions: %i'%(code.instr_size())
sim = interpreter.Interpreter(code, image, block_size, 4)
sim.run()
detections_pixmap = sim.gen_output_image(1) # result is saved in first buffer
# convert the number of rejections in the stages to detections
detections = convert_pixelmap_to_detections(detections_pixmap, cascade.size)
print 'detections:', detections
detections_im = visualisation.draw_faces(image, detections)
imageio.write(res_filename_prefix + '_pixmap.png', detections_pixmap, 1)
imageio.write(res_filename_prefix + '.png', detections_im, 3)
