def run_test(image, cascade):
block_size = (64, 64)
print 'XXX histogram equalisation is not implemented yet, use violajones impl'
print ' before executing simulator'
image = reference.equalizeHist(image)
args = {'haar_classifier':cascade}
# now execute the codegen
code = Code()
code.set_generator(gen_code.gen_detect_faces_opt, block_size, args)
#print '# instructions: %i'%(code.instr_size())
sim = 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 = gen_code.convert_pixelmap_to_detections(detections_pixmap, cascade.size)
return detections
def run_test(codegen_function, image, cascade, block_size):
print 'running %s'%codegen_function.__name__
print 'XXX histogram equalisation is not implemented yet, use violajones impl'
print ' before executing simulator'
image = reference.equalizeHist(image)
width, height = block_size
pe_dim = (len(image[0])//width, len(image)//height)
args = {'haar_classifier': cascade, 'pe_dim':pe_dim}
# now execute the codegen
code = Code()
code.set_generator(optimiser_wrapper(codegen_function), block_size, args)
sim = 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 = gen_code.convert_pixelmap_to_detections(detections_pixmap, cascade.size)
return detections
