def infer():
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
if not os.path.exists('output'):
os.mkdir('output')
model = YOLOv3(is_train=False)
model.build_model()
outputs = model.get_pred()
input_size = cfg.input_size
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
# yapf: disable
if cfg.weights:
def if_exist(var):
return os.path.exists(os.path.join(cfg.weights, var.name))
fluid.io.load_vars(exe, cfg.weights, predicate=if_exist)
# yapf: enable
# you can save inference model by following code
# fluid.io.save_inference_model("./output/yolov3",
# feeded_var_names=['image', 'im_shape'],
# target_vars=outputs,
# executor=exe)
feeder = fluid.DataFeeder(place=place, feed_list=model.feeds())
fetch_list = [outputs]
image_names = []
if cfg.image_name is not None:
image_names.append(cfg.image_name)
else:
for image_name in os.listdir(cfg.image_path):
if image_name.split('.')[-1] in ['jpg', 'png']:
image_names.append(image_name)
for image_name in image_names:
infer_reader = reader.infer(input_size,
os.path.join(cfg.image_path, image_name))
label_names, _ = reader.get_label_infos()
data = next(infer_reader())
im_shape = data[0][2]
outputs = exe.run(fetch_list=[v.name for v in fetch_list],
feed=feeder.feed(data),
return_numpy=False,
use_program_cache=True)
bboxes = np.array(outputs[0])
if bboxes.shape[1] != 6:
print("No object found in {}".format(image_name))
continue
labels = bboxes[:, 0].astype('int32')
scores = bboxes[:, 1].astype('float32')
boxes = bboxes[:, 2:].astype('float32')
path = os.path.join(cfg.image_path, image_name)
box_utils.draw_boxes_on_image(path, boxes, scores, labels, label_names,
cfg.draw_thresh)
def infer():
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
if not os.path.exists('output'):
os.mkdir('output')
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
with fluid.dygraph.guard(place):
model = YOLOv3(3, is_train=False)
input_size = cfg.input_size
# yapf: disable
if cfg.weights:
restore, _ = fluid.load_dygraph(cfg.weights)
model.set_dict(restore)
# yapf: enable
# you can save inference model by following code
# fluid.io.save_inference_model("./output/yolov3",
# feeded_var_names=['image', 'im_shape'],
# target_vars=outputs,
# executor=exe)
image_names = []
if cfg.image_name is not None:
image_names.append(cfg.image_name)
else:
for image_name in os.listdir(cfg.image_path):
if image_name.split('.')[-1] in ['jpg', 'png']:
image_names.append(image_name)
for image_name in image_names:
infer_reader = reader.infer(
input_size, os.path.join(cfg.image_path, image_name))
label_names, _ = reader.get_label_infos()
data = next(infer_reader())
img_data = np.array([x[0] for x in data]).astype('float32')
img = to_variable(img_data)
im_shape_data = np.array([x[2] for x in data]).astype('int32')
im_shape = to_variable(im_shape_data)
outputs = model(img, None, None, None, None, im_shape)
bboxes = outputs.numpy()
if bboxes.shape[1] != 6:
print("No object found in {}".format(image_name))
continue
labels = bboxes[:, 0].astype('int32')
scores = bboxes[:, 1].astype('float32')
boxes = bboxes[:, 2:].astype('float32')
path = os.path.join(cfg.image_path, image_name)
box_utils.draw_boxes_on_image(path, boxes, scores, labels,
label_names, cfg.draw_thresh)
def infer():
if not os.path.exists('output'):
os.mkdir('output')
model = YOLOv3(is_train=False)
model.build_model()
outputs = model.get_pred()
input_size = cfg.input_size
#place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
place = fluid.CPUPlace()
exe = fluid.Executor(place)
# yapf: disable
if cfg.weights:
print('cfg.weights = ',cfg.weights)
def if_exist(var):
return os.path.exists(os.path.join(cfg.weights, var.name))
fluid.io.load_vars(exe, cfg.weights, predicate=if_exist)
# yapf: enable
feeder = fluid.DataFeeder(place=place, feed_list=model.feeds())
fetch_list = [outputs]
image_names = []
if cfg.image_name is not None:
image_names.append(cfg.image_name)
print('image_names = ', image_names)
else:
for image_name in os.listdir(cfg.image_path):
if image_name.split('.')[-1] in ['jpg', 'png']:
image_names.append(image_name)
for image_name in image_names:
infer_reader = reader.infer(input_size,
os.path.join(cfg.image_path, image_name))
label_names, _ = reader.get_label_infos()
data = next(infer_reader())
im_shape = data[0][2]
outputs = exe.run(fetch_list=[v.name for v in fetch_list],
feed=feeder.feed(data),
return_numpy=False)
bboxes = np.array(outputs[0])
if bboxes.shape[1] != 6:
print("No object found in {}".format(image_name))
continue
labels = bboxes[:, 0].astype('int32')
scores = bboxes[:, 1].astype('float32')
boxes = bboxes[:, 2:].astype('float32')
print(labels)
print(scores)
print(len(boxes))
path = os.path.join(cfg.image_path, image_name)
box_utils.draw_boxes_on_image(path, boxes, scores, labels, label_names,
cfg.draw_thresh)
def eval():
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
if '2014' in cfg.dataset:
test_list = 'annotations/instances_val2014.json'
elif '2017' in cfg.dataset:
test_list = 'annotations/instances_val2017.json'
if cfg.debug:
if not os.path.exists('output'):
os.mkdir('output')
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
with fluid.dygraph.guard(place):
model = YOLOv3(3,is_train=False)
# yapf: disable
if cfg.weights:
restore, _ = fluid.load_dygraph(cfg.weights)
model.set_dict(restore)
model.eval()
input_size = cfg.input_size
# batch_size for test must be 1
test_reader = reader.test(input_size, 1)
label_names, label_ids = reader.get_label_infos()
if cfg.debug:
print("Load in labels {} with ids {}".format(label_names, label_ids))
def get_pred_result(boxes, scores, labels, im_id):
result = []
for box, score, label in zip(boxes, scores, labels):
x1, y1, x2, y2 = box
w = x2 - x1 + 1
h = y2 - y1 + 1
bbox = [x1, y1, w, h]
res = {
'image_id': int(im_id),
'category_id': label_ids[int(label)],
'bbox': list(map(float, bbox)),
'score': float(score)
}
result.append(res)
return result
dts_res = []
total_time = 0
for iter_id, data in enumerate(test_reader()):
start_time = time.time()
img_data = np.array([x[0] for x in data]).astype('float32')
img = to_variable(img_data)
im_id_data = np.array([x[1] for x in data]).astype('int32')
im_id = to_variable(im_id_data)
im_shape_data = np.array([x[2] for x in data]).astype('int32')
im_shape = to_variable(im_shape_data)
batch_outputs = model(img, None, None, None, im_id, im_shape)
nmsed_boxes = batch_outputs.numpy()
if nmsed_boxes.shape[1] != 6:
continue
im_id = data[0][1]
nmsed_box=nmsed_boxes
labels = nmsed_box[:, 0]
scores = nmsed_box[:, 1]
boxes = nmsed_box[:, 2:6]
dts_res += get_pred_result(boxes, scores, labels, im_id)
end_time = time.time()
print("batch id: {}, time: {}".format(iter_id, end_time - start_time))
total_time += end_time - start_time
with open("yolov3_result.json", 'w') as outfile:
json.dump(dts_res, outfile)
print("start evaluate detection result with coco api")
coco = COCO(os.path.join(cfg.data_dir, test_list))
cocoDt = coco.loadRes("yolov3_result.json")
cocoEval = COCOeval(coco, cocoDt, 'bbox')
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
print("evaluate done.")
print("Time per batch: {}".format(total_time / iter_id))
def infer():
if not os.path.exists('output_frame'):
os.mkdir('output_frame')
model = YOLOv3(is_train=False)
model.build_model()
outputs = model.get_pred()
input_size = cfg.input_size
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
# yapf: disable
if cfg.weights:
def if_exist(var):
return os.path.exists(os.path.join(cfg.weights, var.name))
fluid.io.load_vars(exe, cfg.weights, predicate=if_exist)
# yapf: enable
# you can save inference model by following code
# fluid.io.save_inference_model("./output/yolov3",
# feeded_var_names=['image', 'im_shape'],
# target_vars=outputs,
# executor=exe)
feeder = fluid.DataFeeder(place=place, feed_list=model.feeds())
fetch_list = [outputs]
video_path = "" #source
video_dir = "" #output path
video_writer = FFmpegWriter(video_dir)
cap = cv2.VideoCapture(video_path)
i = 0
while cap.isOpened():
if i % 100 == 0:
print('{}% '.format(i / 1020 * 100) + '%d' % i)
label_names, _ = reader.get_label_infos()
ok, image = cap.read()
if ok:
infer_reader = reader.infer_m(input_size, image)
data = next(infer_reader())
im_shape = data[0][2]
outputs = exe.run(fetch_list=[v.name for v in fetch_list],
feed=feeder.feed(data),
return_numpy=False)
bboxes = np.array(outputs[0])
if bboxes.shape[1] != 6:
continue
labels = bboxes[:, 0].astype('int32')
scores = bboxes[:, 1].astype('float32')
boxes = bboxes[:, 2:].astype('float32')
frame = box_utils.save_as_video(image, boxes, scores, labels,
label_names, cfg.draw_thresh)
frame = frame[:, :, ::-1]
frame = cv2.resize(frame, (492, 369))
video_writer.writeFrame(frame)
else:
print("over")
break
i += 1
video_writer.close()
def eval():
if '2014' in cfg.dataset:
test_list = 'annotations/instances_val2014.json'
elif '2017' in cfg.dataset:
test_list = 'annotations/instances_val2017.json'
if cfg.debug:
if not os.path.exists('output'):
os.mkdir('output')
model = YOLOv3(is_train=False)
model.build_model()
outputs = model.get_pred()
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
# yapf: disable
if cfg.weights:
def if_exist(var):
return os.path.exists(os.path.join(cfg.weights, var.name))
fluid.io.load_vars(exe, cfg.weights, predicate=if_exist)
# yapf: enable
# you can save inference model by following code
# fluid.io.save_inference_model("./output/yolov3",
# feeded_var_names=['image', 'im_shape'],
# target_vars=outputs,
# executor=exe)
input_size = cfg.input_size
test_reader = reader.test(input_size, 1)
label_names, label_ids = reader.get_label_infos()
if cfg.debug:
print("Load in labels {} with ids {}".format(label_names, label_ids))
feeder = fluid.DataFeeder(place=place, feed_list=model.feeds())
def get_pred_result(boxes, scores, labels, im_id):
result = []
for box, score, label in zip(boxes, scores, labels):
x1, y1, x2, y2 = box
w = x2 - x1 + 1
h = y2 - y1 + 1
bbox = [x1, y1, w, h]
res = {
'image_id': im_id,
'category_id': label_ids[int(label)],
'bbox': list(map(float, bbox)),
'score': float(score)
}
result.append(res)
return result
dts_res = []
fetch_list = [outputs]
total_time = 0
for batch_id, batch_data in enumerate(test_reader()):
start_time = time.time()
batch_outputs = exe.run(fetch_list=[v.name for v in fetch_list],
feed=feeder.feed(batch_data),
return_numpy=False,
use_program_cache=True)
lod = batch_outputs[0].lod()[0]
nmsed_boxes = np.array(batch_outputs[0])
if nmsed_boxes.shape[1] != 6:
continue
for i in range(len(lod) - 1):
im_id = batch_data[i][1]
start = lod[i]
end = lod[i + 1]
if start == end:
continue
nmsed_box = nmsed_boxes[start:end, :]
labels = nmsed_box[:, 0]
scores = nmsed_box[:, 1]
boxes = nmsed_box[:, 2:6]
dts_res += get_pred_result(boxes, scores, labels, im_id)
end_time = time.time()
print("batch id: {}, time: {}".format(batch_id, end_time - start_time))
total_time += end_time - start_time
with open("yolov3_result.json", 'w') as outfile:
json.dump(dts_res, outfile)
print("start evaluate detection result with coco api")
coco = COCO(os.path.join(cfg.data_dir, test_list))
cocoDt = coco.loadRes("yolov3_result.json")
cocoEval = COCOeval(coco, cocoDt, 'bbox')
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
print("evaluate done.")
print("Time per batch: {}".format(total_time / batch_id))