def faceDetection(args, FDDB_list, FDDB_results_file):
runner = Runner(args.vitisrundir)
inTensors = runner.get_input_tensors()
outTensors = runner.get_output_tensors()
batch_sz = 1
fpgaBlobs = []
for io in [inTensors, outTensors]:
blobs = []
for t in io:
shape = (batch_sz, ) + tuple([t.dims[i]
for i in range(t.ndims)][1:])
blobs.append(np.empty((shape), dtype=np.float32, order='C'))
fpgaBlobs.append(blobs)
for i, line in enumerate(FDDB_list):
FDDB_results_file.write('%s\n' % line.strip())
image_name = args.fddbPath + line.strip() + '.jpg'
image_ori = cv2.imread(image_name, cv2.IMREAD_COLOR)
rects = detect(runner, fpgaBlobs, image_ori)
FDDB_results_file.write('%d\n' % len(rects))
for rect in rects:
FDDB_results_file.write('%d %d %d %d %f\n' %
(rect[0], rect[1], rect[2] - rect[0],
rect[3] - rect[1], rect[4]))
FDDB_results_file.close()
def faceDetection(vitis_rundir,outpath, rsz_h, rsz_w, path):
runner = Runner(vitis_rundir)
inTensors = runner.get_input_tensors()
outTensors = runner.get_output_tensors()
batch_sz = 1
fpgaBlobs= []
for io in [inTensors, outTensors]:
blobs = []
for t in io:
shape = (batch_sz,) + tuple([t.dims[i] for i in range(t.ndims)][1:])
blobs.append(np.empty((shape), dtype=np.float32, order='C'))
fpgaBlobs.append(blobs)
dirName = outpath
if not os.path.exists(dirName):
os.mkdir(dirName)
output_Img_path = dirName
#os.chdir(path)
res=[]
for fn in sorted(glob.glob(path+ '/*.jpg'), key=os.path.getsize):
filename = fn[fn.rfind('/')+1:]
src_img=cv2.imread(fn)
input_img=cv2.resize(src_img,(rsz_w, rsz_h))
face_rects=detect(runner, fpgaBlobs, input_img)
dst_img=input_img.copy()
if len(face_rects) != 0:
for face_rect in face_rects:
res.append("{} {} {} {} {}".format(fn, face_rect[0],face_rect[1],face_rect[2],face_rect[3]))
print ("{} {} {} {} {}".format(fn, face_rect[0],face_rect[1],face_rect[2],face_rect[3]))
cv2.rectangle(dst_img,(face_rect[0],face_rect[1]),(face_rect[2],face_rect[3]),(0,255,0),2)
cv2.imwrite(output_Img_path+filename,dst_img)
def run(rundir, chanIdx, q, args):
xspub = xstream.Publisher()
xssub = xstream.Subscribe(chanIdx2Str(chanIdx))
runner = Runner(rundir)
inTensors = runner.get_input_tensors()
outTensors = runner.get_output_tensors()
q.put(1) # ready for work
fpgaBlobs = None
fcOutput = None
labels = xdnn_io.get_labels(args['labels'])
xdnnCPUOp = xdnn.XDNNCPUOp("%s/weights.h5" % rundir)
while True:
try:
payload = xssub.get()
if not payload:
break
(meta, buf) = payload
if fpgaBlobs == None:
# allocate buffers
fpgaBlobs = []
batchsz = meta['shape'][0] # inTensors[0].dims[0]
for io in [inTensors, outTensors]:
blobs = []
for t in io:
shape = (batchsz,) + tuple([t.dims[i] for i in range(t.ndims)][1:])
blobs.append(np.empty((shape), dtype=np.float32, order='C'))
fpgaBlobs.append(blobs)
fcOutput = np.empty((batchsz, args['outsz'],), dtype=np.float32, order='C')
fpgaInput = fpgaBlobs[0][0]
assert(tuple(meta['shape']) == fpgaInput.shape)
data = np.frombuffer(buf, dtype=np.float32).reshape(fpgaInput.shape)
np.copyto(fpgaInput, data)
jid = runner.execute_async(fpgaBlobs[0], fpgaBlobs[1])
runner.wait(jid)
xdnnCPUOp.computeFC(fpgaBlobs[1][0], fcOutput)
softmaxOut = xdnnCPUOp.computeSoftmax(fcOutput)
xdnn_io.printClassification(softmaxOut, meta['images'], labels)
sys.stdout.flush()
if meta['id'] % 1000 == 0:
print("Recvd query %d" % meta['id'])
sys.stdout.flush()
del data
del buf
del payload
xspub.send(meta['from'], "success")
except Exception as e:
logging.error("Worker exception " + str(e))
def main():
args = xdnn_io.processCommandLine()
runner = Runner(args['vitis_rundir'])
inTensors = runner.get_input_tensors()
outTensors = runner.get_output_tensors()
batch_sz = args['batch_sz']
if batch_sz == -1:
# use Runner's suggested batch size
batch_sz = inTensors[0].dims[0]
if args['golden']:
goldenMap = xdnn_io.getGoldenMap(args['golden'])
top5Count = 0
top1Count = 0
fpgaBlobs = []
for io in [inTensors, outTensors]:
blobs = []
for t in io:
shape = (batch_sz,) + tuple([t.dims[i] for i in range(t.ndims)][1:])
blobs.append(np.empty((shape), dtype=np.float32, order='C'))
fpgaBlobs.append(blobs)
img_paths = xdnn_io.getFilePaths(args['images'])
labels = xdnn_io.get_labels(args['labels'])
xdnnCPUOp = xdnn.XDNNCPUOp("%s/weights.h5" % args['vitis_rundir'])
fcOutput = np.empty((batch_sz, args['outsz'],), dtype=np.float32, order='C')
fpgaInput = fpgaBlobs[0][0]
for i in range(0, len(img_paths), batch_sz):
pl = []
# fill tensor input data from image file
for j, p in enumerate(img_paths[i:i + batch_sz]):
img, _ = xdnn_io.loadImageBlobFromFile(p,
args['img_raw_scale'], args['img_mean'], args['img_input_scale'],
fpgaInput.shape[2], fpgaInput.shape[3])
pl.append(p)
np.copyto(fpgaInput[j], img)
jid = runner.execute_async(fpgaBlobs[0], fpgaBlobs[1])
runner.wait(jid)
xdnnCPUOp.computeFC(fpgaBlobs[1][0], fcOutput)
softmaxOut = xdnnCPUOp.computeSoftmax(fcOutput)
if args['golden']:
for j,p in enumerate(img_paths[i:i + batch_sz]):
top1Count += xdnn_io.isTopK(softmaxOut[j], goldenMap, p, labels, 1)
top5Count += xdnn_io.isTopK(softmaxOut[j], goldenMap, p, labels, 5)
else:
xdnn_io.printClassification(softmaxOut, pl, labels)
if args['golden']:
print ( ("\nAverage accuracy (n=%d) Top-1: %.1f%%, Top-5: %.1f%%\n") % (len(img_paths), float(top1Count)/float(len(img_paths))*100., float(top5Count)/float(len(img_paths))*100.) )
def __init__(self, rundir, nFPGA, nDispatchers, batchsz=-1):
# update meta.json with nFPGA
meta = {}
with open("%s/meta.json" % rundir) as f:
meta = json.load(f)
meta['num_fpga'] = nFPGA
if 'publish_id' in meta:
del meta['publish_id']
if 'subscribe_id' in meta:
del meta['subscribe_id']
with open("%s/meta.json" % rundir, "w") as f:
json.dump(meta, f)
# acquire FPGA
runner = Runner(rundir)
inTensors = runner.get_input_tensors()
outTensors = runner.get_output_tensors()
inshape = [inTensors[0].dims[i] for i in range(inTensors[0].ndims)]
outshape = [outTensors[0].dims[i] for i in range(outTensors[0].ndims)]
if batchsz != -1:
inshape[0] = batchsz # update batch size
outshape[0] = batchsz # update batch size
Dispatcher.runner = runner
Dispatcher.inTensors = inTensors
Dispatcher.outTensors = outTensors
Dispatcher.inshape = inshape
Dispatcher.outshape = outshape
self.q = Queue(maxsize=nDispatchers * 4)
self.workers = []
for i in range(nDispatchers):
sys.stdout.flush()
worker = threading.Thread(target=self._run,
args=(
self.q,
inshape,
outshape,
))
self.workers.append(worker)
worker.start()
def faceDetection(vitis_rundir, outpath, rsz_h, rsz_w, path):
runner = Runner(vitis_rundir)
inTensors = runner.get_input_tensors()
outTensors = runner.get_output_tensors()
batch_sz = 1
fpgaBlobs = []
for io in [inTensors, outTensors]:
blobs = []
for t in io:
shape = (batch_sz, ) + tuple([t.dims[i]
for i in range(t.ndims)][1:])
blobs.append(np.empty((shape), dtype=np.float32, order='C'))
fpgaBlobs.append(blobs)
dirName = outpath
if not os.path.exists(dirName):
os.mkdir(dirName)
output_Img_path = dirName
#os.chdir(path)
fp = open(output_Img_path + "/output.txt", 'w')
for fn in sorted(glob.glob(path + '/*.jpg'), key=os.path.getsize):
filename = fn[fn.rfind('/') + 1:]
image_ori = cv2.imread(fn, cv2.IMREAD_COLOR)
face_rects = detect(runner, fpgaBlobs, image_ori)
res = []
if len(face_rects) != 0:
for face_rect in face_rects:
res.append("{} {} {} {} {}".format(filename, face_rect[0],
face_rect[1], face_rect[2],
face_rect[3]))
print("{} {} {} {} {}".format(fn, face_rect[0], face_rect[1],
face_rect[2], face_rect[3]))
cv2.rectangle(image_ori, (face_rect[0], face_rect[1]),
(face_rect[2], face_rect[3]), (0, 255, 0), 2)
cv2.imwrite(output_Img_path + filename, image_ori)
for faces in res:
fp.write(faces + '\n')
# else:
#res.append("{} {} {} {} {}".format(fn, 0,0,0,0))
fp.close()
def run(rundir, chanIdx, q, args):
xspub = xstream.Publisher()
xssub = xstream.Subscribe(chanIdx2Str(chanIdx))
runner = Runner(rundir)
inTensors = runner.get_input_tensors()
outTensors = runner.get_output_tensors()
q.put(1) # ready for work
fpgaBlobs = None
labels = xdnn_io.get_labels(args['labels'])
if args['yolo_version'] == 'v2': yolo_postproc = yolo.yolov2_postproc
elif args['yolo_version'] == 'v3': yolo_postproc = yolo.yolov3_postproc
else:
assert args['yolo_version'] in (
'v2', 'v3'), "--yolo_version should be <v2|v3>"
biases = bias_selector(args)
if (args['visualize']): colors = generate_colors(len(labels))
while True:
try:
payload = xssub.get()
if not payload:
break
(meta, buf) = payload
if fpgaBlobs == None:
# allocate buffers
fpgaBlobs = []
batchsz = meta['shape'][0] # inTensors[0].dims[0]
for io in [inTensors, outTensors]:
blobs = []
for t in io:
shape = (batchsz, ) + tuple(
[t.dims[i] for i in range(t.ndims)][1:])
blobs.append(
np.empty((shape), dtype=np.float32, order='C'))
fpgaBlobs.append(blobs)
fcOutput = np.empty((
batchsz,
args['outsz'],
),
dtype=np.float32,
order='C')
fpgaInput = fpgaBlobs[0][0]
assert (tuple(meta['shape']) == fpgaInput.shape)
data = np.frombuffer(buf,
dtype=np.float32).reshape(fpgaInput.shape)
np.copyto(fpgaInput, data)
jid = runner.execute_async(fpgaBlobs[0], fpgaBlobs[1])
runner.wait(jid)
boxes = yolo_postproc(fpgaBlobs[1],
args,
meta['image_shapes'],
biases=biases)
if (not args['profile']):
for i in range(min(batchsz, len(meta['image_shapes']))):
print("Detected {} boxes in {}".format(
len(boxes[i]), meta['images'][i]),
flush=True)
# Save the result
if (args['results_dir']):
for i in range(min(batchsz, len(meta['image_shapes']))):
fname = meta['images'][i]
filename = os.path.splitext(os.path.basename(fname))[0]
out_file_txt = os.path.join(args['results_dir'],
filename + '.txt')
print("Saving {} boxes to {}".format(
len(boxes[i]), out_file_txt))
sys.stdout.flush()
saveDetectionDarknetStyle(out_file_txt, boxes[i],
meta['image_shapes'][i])
if (args['visualize']):
out_file_png = os.path.join(
args['results_dir'], filename + '.png')
print("Saving result to {}".format(out_file_png))
sys.stdout.flush()
draw_boxes(fname, boxes[i], labels, colors,
out_file_png)
if meta['id'] % 1000 == 0:
print("Recvd query %d" % meta['id'])
sys.stdout.flush()
del data
del buf
del payload
xspub.send(meta['from'], "success")
except Exception as e:
logging.error("Worker exception " + str(e))
def main():
parser = xdnn_io.default_parser_args()
parser = yolo_parser_args(parser)
args = parser.parse_args()
args = xdnn_io.make_dict_args(args)
# Setup the environment
img_paths = xdnn_io.getFilePaths(args['images'])
if (args['golden'] or args['visualize']):
assert args['labels'], "Provide --labels to compute mAP."
assert args[
'results_dir'], "For accuracy measurements, provide --results_dir to save the detections."
labels = xdnn_io.get_labels(args['labels'])
colors = generate_colors(len(labels))
if args['yolo_version'] == 'v2': yolo_postproc = yolo.yolov2_postproc
elif args['yolo_version'] == 'v3': yolo_postproc = yolo.yolov3_postproc
runner = Runner(args['vitis_rundir'])
# Setup the blobs
inTensors = runner.get_input_tensors()
outTensors = runner.get_output_tensors()
batch_sz = args['batch_sz']
if batch_sz == -1:
batch_sz = inTensors[0].dims[0]
fpgaBlobs = []
for io in [inTensors, outTensors]:
blobs = []
for t in io:
shape = (batch_sz, ) + tuple([t.dims[i]
for i in range(t.ndims)][1:])
blobs.append(np.empty((shape), dtype=np.float32, order='C'))
fpgaBlobs.append(blobs)
fpgaInput = fpgaBlobs[0][0]
# Setup the YOLO config
net_h, net_w = fpgaInput.shape[-2:]
args['net_h'] = net_h
args['net_w'] = net_w
biases = bias_selector(args)
# Setup profiling env
prep_time = 0
exec_time = 0
post_time = 0
# Start the execution
for i in range(0, len(img_paths), batch_sz):
pl = []
img_shapes = []
# Prep images
t1 = timeit.default_timer()
for j, p in enumerate(img_paths[i:i + batch_sz]):
fpgaInput[j, ...], img_shape = xdnn_io.loadYoloImageBlobFromFile(
p, net_h, net_w)
pl.append(p)
img_shapes.append(img_shape)
t2 = timeit.default_timer()
# Execute
jid = runner.execute_async(fpgaBlobs[0], fpgaBlobs[1])
runner.wait(jid)
# Post Proc
t3 = timeit.default_timer()
boxes = yolo_postproc(fpgaBlobs[1], args, img_shapes, biases=biases)
t4 = timeit.default_timer()
prep_time += (t2 - t1)
exec_time += (t3 - t2)
post_time += (t4 - t3)
for i in range(min(batch_sz, len(img_shapes))):
print("Detected {} boxes in {}".format(len(boxes[i]), pl[i]))
# Save the result
if (args['results_dir']):
for i in range(min(batch_sz, len(img_shapes))):
filename = os.path.splitext(os.path.basename(pl[i]))[0]
out_file_txt = os.path.join(args['results_dir'],
filename + '.txt')
print("Saving {} boxes to {}".format(len(boxes[i]),
out_file_txt))
sys.stdout.flush()
saveDetectionDarknetStyle(out_file_txt, boxes[i],
img_shapes[i])
if (args['visualize']):
out_file_png = os.path.join(args['results_dir'],
filename + '.png')
print("Saving result to {}".format(out_file_png))
sys.stdout.flush()
draw_boxes(pl[i], boxes[i], labels, colors, out_file_png)
# Profiling results
if (args['profile']):
print("\nAverage Latency in ms:")
print(" Image Prep: {0:3f}".format(prep_time * 1000.0 /
len(img_paths)))
print(" Exec: {0:3f}".format(exec_time * 1000.0 / len(img_paths)))
print(" Post Proc: {0:3f}".format(post_time * 1000.0 /
len(img_paths)))
sys.stdout.flush()
# mAP calculation
if (args['golden']):
print()
print("Computing mAP score : ")
print("Class names are : {} ".format(labels))
mAP = calc_detector_mAP(args['results_dir'], args['golden'],
len(labels), labels, args['prob_threshold'],
args['mapiouthresh'], args['points'])
sys.stdout.flush()