def predict(inp: Image, metadata):
image_raw, image = preprocessor.process(inp)
shape_orig_WH = image_raw.size
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference
print('Running inference on image')
# Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
inputs[0].host = image
a = perf_counter()
trt_outputs = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
b = perf_counter()
metadata['TensorRT Inference Latency (s)'] = (b - a)
trt_outputs = [
output.reshape(shape)
for output, shape in zip(trt_outputs, output_shapes)
]
postprocessor = PostprocessYOLO(**postprocessor_args)
# Run the post-processing algorithms on the TensorRT outputs and get the bounding box details of detected objects
boxes, classes, scores = postprocessor.process(trt_outputs,
(shape_orig_WH))
# Draw the bounding boxes onto the original input image and save it as a PNG file
obj_detected_img = draw_bboxes(image_raw, boxes, scores, classes,
ALL_CATEGORIES)
return obj_detected_img
def _post_process(self, output):
# start = timer()
# A = TestRunner.mem_to_tensor(output[0], self.output_shapes[0])
# B = TestRunner.mem_to_tensor(output[1], self.output_shapes[1])
# C = TestRunner.mem_to_tensor(output[2], self.output_shapes[2])
# tensor_creation_time = timer() - start
# print(f"Creating tensor {round((tensor_creation_time)*1000)} [ms] ")
# print(A.size(), B.size(), C.size())
# anchors_A = ([self.anchors[i] for i in self.yolo_masks[0]])
# anchors_B = ([self.anchors[i] for i in self.yolo_masks[1]])
# anchors_C = ([self.anchors[i] for i in self.yolo_masks[2]])
# print(anchors_A, anchors_B, anchors_C)
# output_A = self._forward_yolo_output(A, anchors_A)
# output_B = self._forward_yolo_output(B, anchors_B)
# output_C = self._forward_yolo_output(C, anchors_C)
# print(output_A.size(), output_B.size(), output_C.size())
# full_output = torch.cat((output_A, output_B, output_C), 1)
# print(full_output.size())
# w, h = self.raw_image.size
# pad_h, pad_w, ratio = calculate_padding(h, w, self.image_height, self.image_width)
# for detections in full_output:
# detections = detections[detections[:, 4] > self.conf_thres]
# box_corner = torch.zeros((detections.shape[0], 4), device=detections.device)
# xy = detections[:, 0:2]
# wh = detections[:, 2:4] / 2
# box_corner[:, 0:2] = xy - wh
# box_corner[:, 2:4] = xy + wh
# probabilities = detections[:, 4]
# nms_indices = nms(box_corner, probabilities, self.nms_thres)
# main_box_corner = box_corner[nms_indices]
# probabilities_nms = probabilities[nms_indices]
# if nms_indices.shape[0] == 0:
# continue
# BB_list = []
# for i in range(len(main_box_corner)):
# x0 = main_box_corner[i, 0].to('cpu').item() / ratio - pad_w
# y0 = main_box_corner[i, 1].to('cpu').item() / ratio - pad_h
# x1 = main_box_corner[i, 2].to('cpu').item() / ratio - pad_w
# y1 = main_box_corner[i, 3].to('cpu').item() / ratio - pad_h
# # draw.rectangle((x0, y0, x1, y1), outline="red")
# # print("BB ", i, "| x = ", x0, "y = ", y0, "w = ", x1 - x0, "h = ", y1 - y0, "probability = ", probabilities_nms[i].item())
# BB = [round(x0), round(y0), round(y1 - y0), round(x1 - x0)] # x, y, h, w
# BB_list.append(BB)
# return BB_list, probabilities_nms
# Before doing post-processing, we need to reshape the outputs as the common.do_inference will give us flat arrays.
output = [output.reshape(shape) for output, shape in zip(output, self.output_shapes)]
postprocessor_args = {"yolo_masks": self.yolo_masks, # A list of 3 three-dimensional tuples for the YOLO masks
"yolo_anchors": self.anchors, # A list of 9 two-dimensional tuples for the YOLO anchors
"obj_threshold": 0.5, # Threshold for object coverage, float value between 0 and 1
"nms_threshold": 0.25, # Threshold for non-max suppression algorithm, float value between 0 and 1
"yolo_input_resolution": self.input_resolution}
postprocessor = PostprocessYOLO(**postprocessor_args)
# # Run the post-processing algorithms on the TensorRT outputs and get the bounding box details of detected objects
return postprocessor.process(output, (self.raw_image.size))
def main(FLAGS):
"""Create a TensorRT engine for ONNX-based YOLOv3-608 and run inference."""
onnx_file_path = 'yolov3.onnx'
engine_file_path = "yolov3.trt"
input_image_path = 'debug_image/test1.jpg'
input_resolution_yolov3_HW = (608, 608)
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
image_raw, image = preprocessor.process(input_image_path)
shape_orig_WH = image_raw.size
trt_outputs = []
with get_engine(onnx_file_path, FLAGS, engine_file_path) as engine, \
engine.create_execution_context() as context:
inputs, outputs, bindings, stream = allocate_buffers(engine)
# print('Running inference on image {}...'.format(input_image_path))
max_batch_size = engine.max_batch_size
image = np.tile(image, [36, 1, 1, 1])
inputs[0].host = image
inf_batch = 36
trt_outputs = do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream,
batch_size=inf_batch)
output_shapes = [(max_batch_size, 255, 19, 19),
(max_batch_size, 255, 38, 38),
(max_batch_size, 255, 76, 76)]
trt_outputs = [
output.reshape(shape)
for output, shape in zip(trt_outputs, output_shapes)
] # [(1, 255, 19, 19), (1, 255, 38, 38), (1, 255, 76, 76)]
postprocessor_args = {
"yolo_masks": [(6, 7, 8), (3, 4, 5), (0, 1, 2)],
"yolo_anchors": [(10, 13), (16, 30), (33, 23), (30, 61), (62, 45),
(59, 119), (116, 90), (156, 198), (373, 326)],
"obj_threshold":
0.6,
"nms_threshold":
0.5,
"yolo_input_resolution":
input_resolution_yolov3_HW
}
postprocessor = PostprocessYOLO(**postprocessor_args)
feat_batch = [[trt_outputs[j][i] for j in range(len(trt_outputs))]
for i in range(len(trt_outputs[0]))]
for idx, layers in enumerate(feat_batch):
boxes, classes, scores = postprocessor.process(layers, (shape_orig_WH))
def process_multi(img_path, yolo, engine,context):
start_tf=time.time()
image=cv2.imread(img_path)
img_persons_new, boxes_new, trans=yolo.process_image(image)
start_tf = time.time()
img_persons_new, boxes_new, trans = yolo.process_image(image)
img=draw(image,boxes_new)
cv2.imwrite('img.jpg',img)
print('process time for tf is',time.time()-start_tf)
start_trt=time.time()
input_resolution_yolov3_HW = (608, 608)
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
image_raw, image = preprocessor.process(img_path)
shape_orig_WH = image_raw.size
# Output shapes expected by the post-processor
output_shapes = [(1, 255, 19, 19), (1, 255, 38, 38), (1, 255, 76, 76)]
# Do inference with TensorRT
trt_outputs = []
inputs, outputs, bindings, stream = common_utils.allocate_buffers(engine)
inputs[0].host = image
trt_outputs = common_utils.do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
start_trt = time.time()
trt_outputs = common_utils.do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
# Before doing post-processing, we need to reshape the outputs as the common.do_inference will give us flat arrays.
trt_outputs = [output.reshape(shape) for output, shape in zip(trt_outputs, output_shapes)]
print('process time for trt is', time.time() - start_trt)
post_trt=time.time()
postprocessor_args = {"yolo_masks": [(6, 7, 8), (3, 4, 5), (0, 1, 2)],
# A list of 3 three-dimensional tuples for the YOLO masks
"yolo_anchors": [(10, 13), (16, 30), (33, 23), (30, 61), (62, 45),
# A list of 9 two-dimensional tuples for the YOLO anchors
(59, 119), (116, 90), (156, 198), (373, 326)],
"obj_threshold": 0.6, # Threshold for object coverage, float value between 0 and 1
"nms_threshold": 0.5,
# Threshold for non-max suppression algorithm, float value between 0 and 1
"yolo_input_resolution": input_resolution_yolov3_HW}
postprocessor = PostprocessYOLO(**postprocessor_args)
# Run the post-processing algorithms on the TensorRT outputs and get the bounding box details of detected objects
boxes, classes, scores = postprocessor.process(trt_outputs, (shape_orig_WH))
# Draw the bounding boxes onto the original input image and save it as a PNG file
obj_detected_img = draw_bboxes(image_raw, boxes, scores, classes, ALL_CATEGORIES)
obj_detected_img.save('out_boxes.png', 'PNG')
print('process time for trt post is', time.time() - post_trt)
def main():
"""Create a TensorRT engine for ONNX-based YOLOv3-608 and run inference."""
# Try to load a previously generated YOLOv3-608 network graph in ONNX format:
onnx_file_path = '/home/nvidia/Documents/Projects/Fabric_defect_detection/YOLO/fast_yolo.onnx'
engine_file_path = "/home/nvidia/Documents/Projects/Fabric_defect_detection/YOLO/fast_yolo.trt"
# Download a dog image and save it to the following file path:
input_image_path = "/home/nvidia/Documents/Projects/Fabric_defect_detection/YOLO/sample.png"
# Two-dimensional tuple with the target network's (spatial) input resolution in HW ordered
input_resolution_yolov3_HW = (352, 352)
# Create a pre-processor object by specifying the required input resolution for YOLOv3
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
# Load an image from the specified input path, and return it together with a pre-processed version
image_raw, image = preprocessor.process(input_image_path)
# Store the shape of the original input image in WH format, we will need it for later
shape_orig_WH = image_raw.size
# Output shapes expected by the post-processor
output_shapes = [(1, 18, 11, 11)]
# Do inference with TensorRT
trt_outputs = []
with get_engine(onnx_file_path, engine_file_path) as engine, engine.create_execution_context() as context:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference
print('Running inference on image {}...'.format(input_image_path))
# Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
inputs[0].host = image
# start = time.time()
trt_outputs = common.do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
# print("time: %.2f s" %(time.time()-start))
# print(trt_outputs)
# Before doing post-processing, we need to reshape the outputs as the common.do_inference will give us flat arrays.
trt_outputs = [output.reshape(shape) for output, shape in zip(trt_outputs, output_shapes)]
postprocessor_args = {"yolo_masks": [(0, 1, 2)], # A list of 3 three-dimensional tuples for the YOLO masks
"yolo_anchors": [(188,15), (351,16), (351,30)], # A list of 9 two-dimensional tuples for the YOLO anchors],
"obj_threshold": 0.5, # Threshold for object coverage, float value between 0 and 1
"nms_threshold": 0.2, # Threshold for non-max suppression algorithm, float value between 0 and 1
"yolo_input_resolution": input_resolution_yolov3_HW}
postprocessor = PostprocessYOLO(**postprocessor_args)
# Run the post-processing algorithms on the TensorRT outputs and get the bounding box details of detected objects
boxes, classes, scores = postprocessor.process(trt_outputs, (shape_orig_WH))
# Draw the bounding boxes onto the original input image and save it as a PNG file
# obj_detected_img = draw_bboxes(image_raw, boxes, scores, classes, ALL_CATEGORIES)
# output_image_path = 'dog_bboxes.png'
# obj_detected_img.save(output_image_path, 'PNG')
# print('Saved image with bounding boxes of detected objects to {}.'.format(output_image_path))
return boxes, classes, scores
def main():
"""Create a TensorRT engine for ONNX-based YOLOv3-608 and run inference."""
# Try to load a previously generated YOLOv3-608 network graph in ONNX format:
onnx_file_path = 'yolov3.onnx'
engine_file_path = "yolov3.trt"
# Download a dog image and save it to the following file path:
input_image_path = 'dog.jpg'
# Two-dimensional tuple with the target network's (spatial) input resolution in HW ordered
input_resolution_yolov3_HW = (608, 608)
# Create a pre-processor object by specifying the required input resolution for YOLOv3
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
# Load an image from the specified input path, and return it together with a pre-processed version
image_raw, image = preprocessor.process(input_image_path)
# Store the shape of the original input image in WH format, we will need it for later
shape_orig_WH = image_raw.size
# Output shapes expected by the post-processor
output_shapes = [(1, 255, 19, 19), (1, 255, 38, 38), (1, 255, 76, 76)]
# Do inference with TensorRT
trt_outputs = []
with get_engine(onnx_file_path, engine_file_path) as engine, engine.create_execution_context() as context:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference
print('Running inference on image {}...'.format(input_image_path))
# Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
inputs[0].host = image
#for input in inputs:
#print(input.host)
trt_outputs = common.do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
# Before doing post-processing, we need to reshape the outputs as the common.do_inference will give us flat arrays.
trt_outputs = [output.reshape(shape) for output, shape in zip(trt_outputs, output_shapes)]
postprocessor_args = {"yolo_masks": [(6, 7, 8), (3, 4, 5), (0, 1, 2)], # A list of 3 three-dimensional tuples for the YOLO masks
"yolo_anchors": [(10, 13), (16, 30), (33, 23), (30, 61), (62, 45), # A list of 9 two-dimensional tuples for the YOLO anchors
(59, 119), (116, 90), (156, 198), (373, 326)],
"obj_threshold": 0.6, # Threshold for object coverage, float value between 0 and 1
"nms_threshold": 0.5, # Threshold for non-max suppression algorithm, float value between 0 and 1
"yolo_input_resolution": input_resolution_yolov3_HW}
postprocessor = PostprocessYOLO(**postprocessor_args)
# Run the post-processing algorithms on the TensorRT outputs and get the bounding box details of detected objects
boxes, classes, scores = postprocessor.process(trt_outputs, (shape_orig_WH))
# Draw the bounding boxes onto the original input image and save it as a PNG file
obj_detected_img = draw_bboxes(image_raw, boxes, scores, classes, ALL_CATEGORIES)
output_image_path = 'dog_bboxes.png'
obj_detected_img.save(output_image_path, 'PNG')
print('Saved image with bounding boxes of detected objects to {}.'.format(output_image_path))
def main():
ENGINE_FILE_PATH = "ped3_416.trt"
INPUT_LIST_FILE = './ped_list.txt'
INPUT_SIZE = 416
filenames = []
with open(INPUT_LIST_FILE, 'r') as l:
lines = l.readlines()
for line in lines:
filename = line.strip()
filenames.append(filename)
input_resolution_yolov3_HW = (INPUT_SIZE, INPUT_SIZE)
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
postprocessor_args = {"yolo_masks": [(3, 4, 5), (0, 1, 2)],
"yolo_anchors": [(8,34), (14,60), (23,94), (39,149), (87,291), (187,472)],
"obj_threshold": 0.1,
"nms_threshold": 0.3,
"yolo_input_resolution": input_resolution_yolov3_HW}
postprocessor = PostprocessYOLO(**postprocessor_args)
output_shapes = output_shapes_dic[str(INPUT_SIZE)]
with get_engine(ENGINE_FILE_PATH) as engine, engine.create_execution_context() as context:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
for filename in filenames:
image_raw, image = preprocessor.process(filename)
shape_orig_WH = image_raw.size
trt_outputs = []
# Do inference
print('Running inference on image {}...'.format(filename))
inputs[0].host = image
c_time = 0
t1 = time.time()
trt_outputs = common.do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
t2 = time.time()
c_time = t2-t1
print(c_time)
trt_outputs = [output.reshape(shape) for output, shape in zip(trt_outputs, output_shapes)]
boxes, classes, scores = postprocessor.process(trt_outputs, (shape_orig_WH))
if len(boxes) != 0:
obj_detected_img = draw_bboxes(image_raw, boxes, scores, classes, ALL_CATEGORIES)
else:
obj_detected_img = image_raw
savename_0 = filename.split('/')[-1]
savename = savename_0.split('.')[0]
output_image_path = './images_results/' + savename + '_' + str(INPUT_SIZE) + '.png'
obj_detected_img.save(output_image_path, 'PNG')
print('Saved image with bounding boxes of detected objects to {}.'.format(output_image_path))
def infer_cam():
"""Create a TensorRT engine for ONNX-based YOLOv3-608 and run inference."""
# Try to load a previously generated YOLOv3-608 network graph in ONNX format:
onnx_file_path = 'yolov3.onnx'; engine_file_path = 'yolov3.trt'
# Two-dimensional tuple with the target network's (spatial) input resolution in HW ordered
input_resolution_yolov3_HW = (608, 608)
# Output shapes expected by the post-processor
output_shapes = [(1, 255, 19, 19), (1, 255, 38, 38), (1, 255, 76, 76)]
# Create a pre-processor object by specifying the required input resolution for YOLOv3
postprocessor_args = {"yolo_masks": [(6, 7, 8), (3, 4, 5), (0, 1, 2)], # A list of 3 three-dimensional tuples for the YOLO masks
"yolo_anchors": [(10, 13), (16, 30), (33, 23), (30, 61), (62, 45), # A list of 9 two-dimensional tuples for the YOLO anchors
(59, 119), (116, 90), (156, 198), (373, 326)],
"obj_threshold": 0.6, # Threshold for object coverage, float value between 0 and 1
"nms_threshold": 0.5, # Threshold for non-max suppression algorithm, float value between 0 and 1
"yolo_input_resolution": input_resolution_yolov3_HW}
cap = cv2.VideoCapture(0)
trt_outputs = [] # Do inference with TensorRT
with get_engine(onnx_file_path, engine_file_path) as engine, engine.create_execution_context() as context:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
while True:
ret, frame = cap.read(); assert ret
# Load an image from the specified input path, and return it together with a pre-processed version
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
image_raw, image = preprocessor.process(frame)
# Store the shape of the original input image in WH format, we will need it for later
shape_orig_WH = image_raw.size; t = time()
# Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
inputs[0].host = image
trt_outputs = common.do_inference_v2(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
t = time()-t; fps = 1/t; print("infer: %.2fms, fps: %.2f" % (t*1000, fps))
# Before doing post-processing, we need to reshape the outputs as the common.do_inference will give us flat arrays.
trt_outputs = [output.reshape(shape) for output, shape in zip(trt_outputs, output_shapes)]
postprocessor = PostprocessYOLO(**postprocessor_args)
# Run the post-processing algorithms on the TensorRT outputs and get the bounding box details of detected objects
boxes, classes, scores = postprocessor.process(trt_outputs, (shape_orig_WH))
im = draw_bboxes(image_raw, boxes, scores, classes, ALL_CATEGORIES)
im = np.asarray(im)[...,::-1]
cv2.putText(im, "%.2f"%fps, (12,12), 3, 1, (0,255,0))
cv2.imshow("det",im)
if cv2.waitKey(5) == 27: break
cap.release(); cv2.destroyAllWindows()
def batch_show(image_path, image_save_path, onnx_file_path, engine_file_path):
img_list = gb.glob(image_path + r"/*.png")
# Two-dimensional tuple with the target network's (spatial) input resolution in HW ordered
input_resolution_yolov3_HW = (352, 352)
# Create a pre-processor object by specifying the required input resolution for YOLOv3
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
# Create a post-processor object by specifying the required input resolution for YOLOv3
postprocessor_args = {"yolo_masks": [(0, 1, 2)], # A list of 3 three-dimensional tuples for the YOLO masks
"yolo_anchors": [(188,15), (351,16), (351,30)], # A list of 9 two-dimensional tuples for the YOLO anchors],
"obj_threshold": 0.5, # Threshold for object coverage, float value between 0 and 1
"nms_threshold": 0.2, # Threshold for non-max suppression algorithm, float value between 0 and 1
"yolo_input_resolution": input_resolution_yolov3_HW}
postprocessor = PostprocessYOLO(**postprocessor_args)
# Store the shape of the original input image in WH format, we will need it for later
shape_orig_WH = input_resolution_yolov3_HW
# Output shapes expected by the post-processor
output_shapes = [(1, 18, 11, 11)]
# Do inference with TensorRT
total_time, trt_outputs = 0, []
with get_engine(onnx_file_path, engine_file_path) as engine, engine.create_execution_context() as context:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference
# print('Running inference on image {}...'.format(input_image_path))
# Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
for i, img_file in enumerate(img_list):
image_raw, image = preprocessor.process(img_file)
inputs[0].host = image
trt_outputs = common.do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
# Before doing post-processing, we need to reshape the outputs as the common.do_inference will give us flat arrays.
trt_outputs = [output.reshape(shape) for output, shape in zip(trt_outputs, output_shapes)]
# Run the post-processing algorithms on the TensorRT outputs and get the bounding box details of detected objects
boxes, classes, scores = postprocessor.process(trt_outputs, (shape_orig_WH))
image_show = draw_bboxes(image_raw, boxes, scores, classes, ['defect'], bbox_color='yellow')
# Save the marked image
filename, suffix = os.path.split(img_file)
_, fname = os.path.splitext(filename)
save_name = os.path.join(image_save_path, fname+suffix)
image_show.save(save_name)
print("Image", save_name, "saved.")
def main():
"""Create a TensorRT engine for ONNX-based YOLOv3-608 and run inference."""
args = parser.parse_args()
# Try to load a previously generated YOLOv3-608 network graph in ONNX format:
onnx_file_path = 'yolov3.onnx'
engine_file_path = "yolov3.trt"
cam = cv.VideoCapture(args.video)
# img = cv.imread("dog.jpg")
input_resolution_yolov3_HW = (608, 608)
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
postprocessor_args = {"yolo_masks": [(6, 7, 8), (3, 4, 5), (0, 1, 2)],
"yolo_anchors": [(10, 13), (16, 30), (33, 23), (30, 61), (62, 45),
(59, 119), (116, 90), (156, 198), (373, 326)],
"obj_threshold": 0.6,
"nms_threshold": 0.5,
"yolo_input_resolution": input_resolution_yolov3_HW}
postprocessor = PostprocessYOLO(**postprocessor_args)
with get_engine(onnx_file_path, engine_file_path) as engine, engine.create_execution_context() as context:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
while True:
_ret, img = cam.read()
if(_ret is False):
break
image_raw, image = preprocessor.process_image(img)
shape_orig_WH = image_raw.size
output_shapes = [(1, 255, 19, 19),
(1, 255, 38, 38), (1, 255, 76, 76)]
trt_outputs = []
inputs[0].host = image
trt_outputs = common.do_inference(
context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
trt_outputs = [output.reshape(shape)
for output, shape in zip(trt_outputs, output_shapes)]
boxes, classes, scores = postprocessor.process(
trt_outputs, (shape_orig_WH))
if(boxes is None):
continue
obj_detected_img = draw_bboxes(
image_raw, boxes, scores, classes, ALL_CATEGORIES)
det_img = np.array(obj_detected_img)
cv.imshow("frame", det_img)
cv.waitKey(5)
class YoloTRT(object):
def __init__(self):
super().__init__()
# resolution
self.preprocessor = PreprocessYOLO((608, 608))
self.trt = TensorRT("yolov3.trt")
postprocessor_args = {"yolo_masks": [(6, 7, 8), (3, 4, 5), (0, 1, 2)],
"yolo_anchors": [(10, 13), (16, 30), (33, 23), (30, 61), (62, 45),
(59, 119), (116, 90), (156, 198), (373, 326)],
"obj_threshold": 0.6,
"nms_threshold": 0.5,
"yolo_input_resolution": (608, 608)}
self.postprocessor = PostprocessYOLO(**postprocessor_args)
# def _preprocess(self, input_array:np.ndarray) -> np.ndarray: #
# return self.preprocessor.process(input_array) # in: <NHWC> raw image batch , out: <NCHW> resized <N,3,608,608>
def _inference(self, input: np.ndarray) -> list: #
trt_outputs = self.trt.inference(input)
output_shapes = [(self.trt.max_batch_size, 255, 19, 19),
(self.trt.max_batch_size, 255, 38, 38),
(self.trt.max_batch_size, 255, 76, 76)]
trt_outputs = [output.reshape(shape) for output, shape in zip(trt_outputs, output_shapes)] # [(1, 255, 19, 19), (1, 255, 38, 38), (1, 255, 76, 76)]
return trt_outputs # in: <NCHW> <N,3,608,608>, out: [(N, 255, 19, 19), (N, 255, 38, 38), (N, 255, 76, 76)]
# def _postprocess(self, feat_batch, shape_orig_WH:tuple):
# return [[self.postprocessor.process(feat,shape_orig)]for feat, shape_orig in zip(feat_batch,shape_orig_WH)]
@profile
def inference(self, input_array:np.ndarray): # img_array <N,H,W,C>
pre = self.preprocessor.process(input_array) # in: <NHWC> raw image batch , out: <NCHW> resized <N,3,608,608>
trt_outputs = self._inference(pre) # out: [(N, 255, 19, 19), (N, 255, 38, 38), (N, 255, 76, 76)]
feat_batch = [[trt_outputs[j][i] for j in range(len(trt_outputs))] for i in range(len(trt_outputs[0]))]
post = [[self.postprocessor.process(feat,input_array.shape)]for feat in feat_batch] # out:[[bbox,score,categories,confidences],...]
post = post[:len(input_array)]
return post
0.6, # 对象覆盖的阈值，[0,1]之间
"nms_threshold":
0.5, # nms的阈值，[0,1]之间
"yolo_input_resolution":
input_resolution_yolov3_HW
}
# 创建后处理类的实例
postprocessor = PostprocessYOLO(**postprocessor_args)
print("saving...")
for idx in range(len(results)):
trt_results = [
results[idx]["082_convolutional"][0],
results[idx]["094_convolutional"][0],
results[idx]["106_convolutional"][0]
]
trt_outputs = [
output.reshape(shape)
for output, shape in zip(trt_results, output_shapes)
]
# 运行后处理算法，并得到检测到对象的bounding box
boxes, classes, scores = postprocessor.process(trt_outputs,
(shape_orig_WH[idx]))
obj_detected_img = draw_bboxes(image_raws[idx], boxes, scores, classes,
ALL_CATEGORIES)
output_image_path = FLAGS.output + "{0}".format(
filenames[idx].split("/")[-1])
print(output_image_path)
obj_detected_img.save(output_image_path)
def main():
"""Create a TensorRT engine for ONNX-based YOLOv3-608 and run inference."""
# Try to load a previously generated YOLOv3-608 network graph in ONNX format:
onnx_file_path = './yolov3.onnx'
engine_file_path = "yolov3.trt"
data_path = "./data/unrel.data"
data = parse_data_cfg(data_path)
nc = int(data['classes']) # number of classes
path = data['valid'] # path to test images
names = load_classes(data['names']) # class names
iouv = torch.linspace(0.5, 0.95, 1,
dtype=torch.float32) # iou vector for [email protected]:0.95
niou = 1
conf_thres = 0.001
iou_thres = 0.6
verbose = True
# Genearte custom dataloader
img_size = 448 # copy form pytorch src
batch_size = 16
dataset = LoadImagesAndLabels(path, img_size, batch_size, rect=True)
batch_size = min(batch_size, len(dataset))
dataloader = data_loader(dataset, batch_size, img_size)
# Output shapes expected by the post-processor
output_shapes = [(16, 126, 14, 14), (16, 126, 28, 28), (16, 126, 56, 56)]
# Do inference with TensorRT
trt_outputs = []
with get_engine(onnx_file_path, engine_file_path
) as engine, engine.create_execution_context() as context:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
s = ('%20s' + '%10s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R',
'[email protected]', 'F1')
p, r, f1, mp, mr, map, mf1, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
pbar = tqdm.tqdm(dataloader, desc=s)
stats, ap, ap_class = [], [], []
seen = 0
for batch_i, (imgs, targets, paths, shapes) in enumerate(pbar):
imgs = imgs.astype(np.float32) / 255.0
nb, _, height, width = imgs.shape # batch size, channels, height, width
whwh = np.array([width, height, width, height])
inputs[0].host = imgs
postprocessor_args = {
"yolo_masks": [
(6, 7, 8), (3, 4, 5), (0, 1, 2)
], # A list of 3 three-dimensional tuples for the YOLO masks
"yolo_anchors": [
(10, 13),
(16, 30),
(33, 23),
(30, 61),
(
62, 45
), # A list of 9 two-dimensional tuples for the YOLO anchors
(59, 119),
(116, 90),
(156, 198),
(373, 326)
],
"num_classes":
37,
"stride": [32, 16, 8]
}
postprocessor = PostprocessYOLO(**postprocessor_args)
# Do layers before yolo
t = time.time()
trt_outputs = common.do_inference_v2(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
trt_outputs = [
output.reshape(shape)
for output, shape in zip(trt_outputs, output_shapes)
]
trt_outputs = [
np.ascontiguousarray(
otpt[:, :, :int(imgs.shape[2] * (2**i) /
32), :int(imgs.shape[3] * (2**i) / 32)],
dtype=np.float32) for i, otpt in enumerate(trt_outputs)
]
output_list = postprocessor.process(trt_outputs)
t0 += time.time() - t
inf_out = torch.cat(output_list, 1)
t = time.time()
output = non_max_suppression(inf_out,
conf_thres=conf_thres,
iou_thres=iou_thres) # nms
t1 += time.time() - t
# Statistics per image
for si, pred in enumerate(output):
labels = targets[targets[:, 0] == si, 1:]
nl = len(labels)
tcls = labels[:, 0].tolist() if nl else [] # target class
seen += 1
if pred is None:
if nl:
stats.append((torch.zeros(0, niou, dtype=torch.bool),
torch.Tensor(), torch.Tensor(), tcls))
continue
# Assign all predictions as incorrect
correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool)
if nl:
detected = [] # target indices
tcls_tensor = labels[:, 0]
# target boxes
tbox = xywh2xyxy(labels[:, 1:5]) * whwh
tbox = tbox.type(torch.float32)
# Per target class
for cls in torch.unique(tcls_tensor):
ti = (cls == tcls_tensor).nonzero().view(
-1) # prediction indices
pi = (cls == pred[:, 5]).nonzero().view(
-1) # target indices
# Search for detections
if pi.shape[0]:
# Prediction to target ious
ious, i = box_iou(pred[pi, :4], tbox[ti]).max(
1) # best ious, indices
# Append detections
for j in (ious > iouv[0]).nonzero():
d = ti[i[j]] # detected target
if d not in detected:
detected.append(d)
correct[pi[j]] = ious[
j] > iouv # iou_thres is 1xn
if len(
detected
) == nl: # all targets already located in image
break
# Append statistics (correct, conf, pcls, tcls)
stats.append(
(correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
# Plot images
if batch_i < 1:
f = 'test_batch%g_gt.jpg' % batch_i # filename
plot_images(imgs, targets, paths=paths, names=names,
fname=f) # ground truth
f = 'test_batch%g_pred.jpg' % batch_i
plot_images(imgs,
output_to_target(output, width, height),
paths=paths,
names=names,
fname=f) # predictions
# Compute statistics
stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
if len(stats):
p, r, ap, f1, ap_class = ap_per_class(*stats)
if niou > 1:
p, r, ap, f1 = p[:, 0], r[:, 0], ap.mean(
1), ap[:, 0] # [P, R, [email protected]:0.95, [email protected]]
mp, mr, map, mf1 = p.mean(), r.mean(), ap.mean(), f1.mean()
nt = np.bincount(stats[3].astype(np.int64),
minlength=nc) # number of targets per class
else:
nt = torch.zeros(1)
# Print results
pf = '%20s' + '%10.3g' * 6 # print format
print(pf % ('all', seen, nt.sum(), mp, mr, map, mf1))
# Print results per class
if verbose and nc > 1 and len(stats):
for i, c in enumerate(ap_class):
print(pf % (names[c], seen, nt[c], p[i], r[i], ap[i], f1[i]))
# Print speeds
if verbose:
t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (
img_size, img_size, batch_size) # tuple
print(
'Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g'
% t)
def read_queue(queue):
# 3.load model
# initialize
TRT_LOGGER = trt.Logger(trt.Logger.INFO)
trt.init_libnvinfer_plugins(TRT_LOGGER, '')
runtime = trt.Runtime(TRT_LOGGER)
# create engine
with open('model.bin', 'rb') as f:
buf = f.read()
engine = runtime.deserialize_cuda_engine(buf)
# create buffer
host_inputs = []
cuda_inputs = []
host_outputs = []
cuda_outputs = []
bindings = []
stream = cuda.Stream()
for binding in engine:
size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size
host_mem = cuda.pagelocked_empty(size, np.float32)
cuda_mem = cuda.mem_alloc(host_mem.nbytes)
bindings.append(int(cuda_mem))
if engine.binding_is_input(binding):
host_inputs.append(host_mem)
cuda_inputs.append(cuda_mem)
else:
host_outputs.append(host_mem)
cuda_outputs.append(cuda_mem)
context = engine.create_execution_context()
batch_size = 1
input_size = 416
output_shapes_416 = [(batch_size, 54, 13, 13), (batch_size, 54, 26, 26), (batch_size, 54, 52, 52)]
output_shapes_480 = [(batch_size, 54, 15, 15), (batch_size, 54, 30, 30), (batch_size, 54, 60, 60)]
output_shapes_544 = [(batch_size, 54, 17, 17), (batch_size, 54, 34, 34), (batch_size, 54, 68, 68)]
output_shapes_608 = [(batch_size, 54, 19, 19), (batch_size, 54, 38, 38), (batch_size, 54, 72, 72)]
output_shapes_dic = {'416': output_shapes_416, '480': output_shapes_480, '544': output_shapes_544, '608': output_shapes_608}
output_shapes = output_shapes_dic[str(input_size)]
input_resolution_yolov3_HW = (input_size, input_size)
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
postprocessor_args = {"yolo_masks": [(6, 7, 8), (3, 4, 5), (0, 1, 2)],
"yolo_anchors": [(4,7), (7,15), (13,25), (25,42), (41,67), (75,94), (91,162), (158,205), (250,332)],
"obj_threshold": 0.5,
"nms_threshold": 0.35,
"yolo_input_resolution": input_resolution_yolov3_HW}
postprocessor = PostprocessYOLO(**postprocessor_args)
inputs, outputs, bindings, stream = allocate_buffers(engine)
print('3.Load model successful.')
print('Everything is ready.')
num = 0
while cap.isOpened() and ser.isOpen():
if queue.empty():
continue
frame = queue.get()
images = []
image_raw, image = preprocessor.process(frame)
images.append(image)
num = num + 1
images_batch = np.concatenate(images, axis=0)
inputs[0].host = images_batch
#t1 = time.time()
trt_outputs = do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream, batch_size=batch_size)
trt_outputs = [output.reshape(shape) for output, shape in zip(trt_outputs, output_shapes)]
shape_orig_WH = image_raw.size
boxes, classes, scores = postprocessor.process(trt_outputs, (shape_orig_WH), 0)
#t2 = time.time()
#t_inf = t2 - t1
#print("time consumption:",t_inf)
print(boxes, scores, classes)
images.clear()
if np.all(scores == 0):
ser.write("h".encode("utf-8"))
print('exception.')
continue
index = np.nonzero(classes)
label = classes[index[0]]
cv2.imwrite('tmp/'+str(num)+'.jpg', frame)
if label == 0:
ser.write("c".encode("utf-8"))
print('plate front.')
elif label == 1:
ser.write("d".encode("utf-8"))
print('plate back.')
elif label == 2:
ser.write("f".encode("utf-8"))
print('bowl front.')
elif label == 3:
ser.write("e".encode("utf-8"))
print('bowl back.')
elif label == 4:
ser.write("g".encode("utf-8"))
print('glass cup side.')
elif label == 5:
ser.write("g".encode("utf-8"))
print('glass cup back.')
elif label == 6:
ser.write("g".encode("utf-8"))
print('glass cup front.')
elif label == 7:
ser.write("i".encode("utf-8"))
print('teacup side.')
elif label == 8:
ser.write("j".encode("utf-8"))
print('teacup back.')
elif label == 9:
ser.write("k".encode("utf-8"))
print('teacup front.')
elif label == 10:
ser.write("g".encode("utf-8"))
print('cup side.')
elif label == 11:
ser.write("g".encode("utf-8"))
print('cup back.')
elif label == 12:
ser.write("g".encode("utf-8"))
print('cup front.')
else:
ser.write("h".encode("utf-8"))
print('exception.')
def main():
args = build_argparser().parse_args()
model_xml = args.model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
# ------------- 1. Plugin initialization for specified device and load extensions library if specified -------------
log.info("Creating Inference Engine...")
ie = IECore()
if args.cpu_extension and 'CPU' in args.device:
ie.add_extension(args.cpu_extension, "CPU")
# -------------------- 2. Reading the IR generated by the Model Optimizer (.xml and .bin files) --------------------
log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
net = IENetwork(model=model_xml, weights=model_bin)
# ---------------------------------- 3. Load CPU extension for support specific layer ------------------------------
if "CPU" in args.device:
supported_layers = ie.query_network(net, "CPU")
not_supported_layers = [l for l in net.layers.keys() if l not in supported_layers]
if len(not_supported_layers) != 0:
log.error("Following layers are not supported by the plugin for specified device {}:\n {}".
format(args.device, ', '.join(not_supported_layers)))
log.error("Please try to specify cpu extensions library path in sample's command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
assert len(net.inputs.keys()) == 1, "Sample supports only YOLO V3 based single input topologies"
# ---------------------------------------------- 4. Preparing inputs -----------------------------------------------
log.info("Preparing inputs")
input_blob = next(iter(net.inputs))
output_blob = next(iter(net.outputs))
batch_size = 16
img_size = 448
data_path = "/usr/src/app/data/unrel.data"
data = parse_data_cfg(data_path)
nc = 37#int(data['classes'])
path = data['valid']
names = load_classes(data['names'])
iouv = torch.linspace(0.5, 0.95, 10, dtype=torch.float32) # iou vector for [email protected]:0.95
iouv = iouv[0].view(1)
niou = iouv.numel()#1
conf_thres = 0.001
iou_thres = 0.6
verbose = True
dataset = LoadImagesAndLabels(path, img_size, batch_size, rect=False)
batch_size = min(batch_size, len(dataset))
dataloader = data_loader(dataset, batch_size, img_size)
# Output shapes expected by the post-processor
output_shapes = [(16, 126, 14, 14), (16, 126, 28, 28), (16, 126, 56, 56)]
n, c, h, w = net.inputs[input_blob].shape
log.info("Loading model to the plugin")
exec_net = ie.load_network(network=net, device_name=args.device)
# ----------------------------------------- 5. Loading model to the plugin -----------------------------------------
log.info("Starting inference...")
s = ('%20s' + '%10s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', '[email protected]', 'F1')
p, r, f1, mp, mr, map, mf1, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
pbar = tqdm.tqdm(dataloader, desc=s)
stats, ap, ap_class = [], [], []
seen = 0
print('HERE0')
for batch_i, (imgs, targets, paths, shapes) in enumerate(pbar):
imgs = imgs.astype(np.float32) / 255.0
#print(imgs)
nb, _, height, width = imgs.shape # batch size, channels, height, width
#print(height,width)
whwh = np.array([width, height, width, height])
#print('HERE1')
postprocessor_args = {"yolo_masks": [(6, 7, 8), (3, 4, 5), (0, 1, 2)], # A list of 3 three-dimensional tuples for the YOLO masks
"yolo_anchors": [(10, 13), (16, 30), (33, 23), (30, 61), (62, 45), # A list of 9 two-dimensional tuples for the YOLO anchors
(59, 119), (116, 90), (156, 198), (373, 326)],
"num_classes": 37,
"stride":[32, 16, 8]}
postprocessor = PostprocessYOLO(**postprocessor_args)
# Start inference
t = time()
#print(imgs)
res = exec_net.infer(inputs={input_blob: imgs})
#print(res)
res0 = list(res.values())[0]
res1 = list(res.values())[1]
res2 = list(res.values())[2]
res = [res2,res0,res1]
#print(res0)
#print(res[0].shape,res[1].shape,res[2].shape)
#print('HERE2')
#res = [output.reshape(shape) for output, shape in zip(res0, output_shapes)]
res = [np.ascontiguousarray(otpt[:, :, :int(imgs.shape[2]*(2**i)/32), :int(imgs.shape[3]*(2**i)/32)], dtype=np.float32) for i, otpt in enumerate(res)]
#print(res[0].shape,res[1].shape,res[2].shape)
output_list = postprocessor.process(res)
#print('HERE2.25')
#print(output_list[0].shape,output_list[1].shape,output_list[2].shape)
#print('HERE2.5')
t0 += time() - t
inf_out = torch.cat(output_list, 1)
print(inf_out.shape)
print(inf_out)
"""
def main():
"""Create a TensorRT engine for ONNX-based YOLOv3-608 and run inference."""
# Try to load a previously generated YOLOv3-608 network graph in ONNX format:
#获取onnx模型和相应引擎文件的路径
onnx_file_path = 'yolov3.onnx'
engine_file_path = "yolov3.trt"
# Download a dog image and save it to the following file path:
#下载相关的图片数据
input_image_path = common.download_file(
'dog.jpg',
'https://github.com/pjreddie/darknet/raw/f86901f6177dfc6116360a13cc06ab680e0c86b0/data/dog.jpg',
checksum_reference=None)
# Two-dimensional tuple with the target network's (spatial) input resolution in HW ordered
#网络的输入图片weidth和height
input_resolution_yolov3_HW = (608, 608)
# Create a pre-processor object by specifying the required input resolution for YOLOv3
#PreprocessYOLO参考data_processing.py的实现
#加载图片并进行相应的预处理
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
# Load an image from the specified input path, and return it together with a pre-processed version
#从相应路径加载一张图片，将加载的原图和预处理后的图像一起返回
#具体参考data_processing.py的实现
image_raw, image = preprocessor.process(input_image_path)
# Store the shape of the original input image in WH format, we will need it for later
#存储原始图片的维度
shape_orig_WH = image_raw.size
# Output shapes expected by the post-processor
#输出层的维度
output_shapes = [(1, 255, 19, 19), (1, 255, 38, 38), (1, 255, 76, 76)]
# Do inference with TensorRT
#进行trt的推理
trt_outputs = []
#get_engine参考本文件的实现
#获取引擎文件并创建相关的推理上下文
with get_engine(onnx_file_path, engine_file_path
) as engine, engine.create_execution_context() as context:
#分配相应的内存缓冲区
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference
print('Running inference on image {}...'.format(input_image_path))
# Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
#将相应数据传到主机内存
inputs[0].host = image
#进行相应的推理
trt_outputs = common.do_inference_v2(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
# Before doing post-processing, we need to reshape the outputs as the common.do_inference will give us flat arrays.
#得到推理的输出
trt_outputs = [
output.reshape(shape)
for output, shape in zip(trt_outputs, output_shapes)
]
postprocessor_args = {
"yolo_masks":
[(6, 7, 8), (3, 4, 5),
(0, 1, 2)], # A list of 3 three-dimensional tuples for the YOLO masks
"yolo_anchors": [
(10, 13),
(16, 30),
(33, 23),
(30, 61),
(62,
45), # A list of 9 two-dimensional tuples for the YOLO anchors
(59, 119),
(116, 90),
(156, 198),
(373, 326)
],
"obj_threshold":
0.6, # Threshold for object coverage, float value between 0 and 1
"nms_threshold":
0.5, # Threshold for non-max suppression algorithm, float value between 0 and 1
"yolo_input_resolution":
input_resolution_yolov3_HW
}
#接下来就是相关的后处理内容了
postprocessor = PostprocessYOLO(**postprocessor_args)
# Run the post-processing algorithms on the TensorRT outputs and get the bounding box details of detected objects
boxes, classes, scores = postprocessor.process(trt_outputs,
(shape_orig_WH))
# Draw the bounding boxes onto the original input image and save it as a PNG file
obj_detected_img = draw_bboxes(image_raw, boxes, scores, classes,
ALL_CATEGORIES)
output_image_path = 'dog_bboxes.png'
obj_detected_img.save(output_image_path, 'PNG')
print('Saved image with bounding boxes of detected objects to {}.'.format(
output_image_path))
def main():
#########################################################################
# $ python3 onnx_to_tensorrt.py v3 608
#########################################################################
dir_onnx = sys.argv[1]
fn_onnx = sys.argv[2]
onnx_file_path = os.path.join(dir_onnx, fn_onnx)
#print('fn_onnx : ', fn_onnx); exit()
t1, t2 = get_exact_file_name_from_path(fn_onnx).split('_')
v_yolo = t1[4:]
said = int(t2)
#print('v_yolo : ', v_yolo, ', said : ', said); exit()
#said = int(sys.argv[2])
"""Create a TensorRT engine for ONNX-based YOLOv3-608 and run inference."""
# Try to load a previously generated YOLOv3-608 network graph in ONNX format:
#onnx_file_path = 'yolo{}_{}.onnx'.format(v_yolo, said)
engine_file_path = os.path.join(dir_onnx,
'yolo{}_{}.trt'.format(v_yolo, said))
# Download a dog image and save it to the following file path:
input_image_path = download_file(
'dog.jpg',
'https://github.com/pjreddie/darknet/raw/f86901f6177dfc6116360a13cc06ab680e0c86b0/data/dog.jpg',
checksum_reference=None)
# Two-dimensional tuple with the target network's (spatial) input resolution in HW ordered
input_resolution_yolov3_HW = (said, said)
# Create a pre-processor object by specifying the required input resolution for YOLOv3
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
# Load an image from the specified input path, and return it together with a pre-processed version
image_raw, image = preprocessor.process(input_image_path)
# Store the shape of the original input image in WH format, we will need it for later
shape_orig_WH = image_raw.size
#print('image_raw.size : ', image_raw.size); print('image.size : ', image.size); exit()
# Output shapes expected by the post-processor
#output_shapes = [(1, 255, 19, 19), (1, 255, 38, 38), (1, 255, 76, 76)]
output_shapes = get_output_shapes(v_yolo, said)
# Do inference with TensorRT
trt_outputs = []
with get_engine(onnx_file_path, engine_file_path
) as engine, engine.create_execution_context() as context:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference
print('Running inference on image {}...'.format(input_image_path))
# Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
inputs[0].host = image
'''
print('len(inputs) : ', len(inputs)); # 1
print('len(outputs) : ', len(outputs)); # 2 for v3-tiny, 3 for v3 #exit()
print('type(stream) : ', type(stream)); exit()
print('type(outputs[0] : ', type(outputs[0])); #exit()
print('type(outputs[1] : ', type(outputs[1])); exit()
'''
# start = time.time()
trt_outputs = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
# print("time: %.2f s" %(time.time()-start))
'''
print('len(trt_outputs) : ', len(trt_outputs));
print('trt_outputs[0].shape : ', trt_outputs[0].shape)
print('trt_outputs[1].shape : ', trt_outputs[1].shape); exit()
'''
print(trt_outputs)
# Before doing post-processing, we need to reshape the outputs as the common.do_inference will give us flat arrays.
trt_outputs = [
output.reshape(shape)
for output, shape in zip(trt_outputs, output_shapes)
]
postprocessor_args = get_postprocessor_args(v_yolo,
input_resolution_yolov3_HW)
#print('postprocessor_args : ', postprocessor_args); exit()
'''
postprocessor_args = {"yolo_masks": [(6, 7, 8), (3, 4, 5), (0, 1, 2)], # A list of 3 three-dimensional tuples for the YOLO masks
"yolo_anchors": [(10, 13), (16, 30), (33, 23), (30, 61), (62, 45), # A list of 9 two-dimensional tuples for the YOLO anchors
(59, 119), (116, 90), (156, 198), (373, 326)],
"obj_threshold": 0.6, # Threshold for object coverage, float value between 0 and 1
"nms_threshold": 0.5, # Threshold for non-max suppression algorithm, float value between 0 and 1
"yolo_input_resolution": input_resolution_yolov3_HW}
'''
postprocessor = PostprocessYOLO(**postprocessor_args)
# Run the post-processing algorithms on the TensorRT outputs and get the bounding box details of detected objects
boxes, classes, scores = postprocessor.process(trt_outputs,
(shape_orig_WH))
# Draw the bounding boxes onto the original input image and save it as a PNG file
obj_detected_img = draw_bboxes(image_raw, boxes, scores, classes,
ALL_CATEGORIES)
output_image_path = 'dog_bboxes_{}_{}.png'.format(v_yolo, said)
obj_detected_img.save(output_image_path, 'PNG')
print('Saved image with bounding boxes of detected objects to {}.'.format(
output_image_path))
def main():
"""Create a TensorRT engine for ONNX-based YOLOv3-608 and run inference."""
# Try to load a previously generated YOLOv3-608 network graph in ONNX format:
if COCO:
onnx_file_path = os.path.join('./engine/onnx/',
'yolov3-' + str(SIZE) + '.onnx')
engine_file_path = os.path.join('./engine/trt/',
'yolov3-' + str(SIZE) + BUILD + '.trt')
else:
onnx_file_path = os.path.join('./engine/onnx/',
'yolov3-voc-' + str(SIZE) + '.onnx')
engine_file_path = os.path.join(
'./engine/trt/', 'yolov3-voc-' + str(SIZE) + BUILD + '.trt')
# onnx_file_path = "./engine/yolov3-608.onnx"
# engine_file_path = "./engine/yolov3-608-voc-f32.trt"
# loop over images
if COCO:
test_images_file = './coco/5k.txt' #for coco
else:
test_images_file = './VOC/data/dataset/voc_test.txt' #for voc
with open(test_images_file, 'r') as f:
txt = f.readlines()
test_images = [line.strip() for line in txt]
timeRecSave = []
input_resolution_yolov3_HW = (SIZE, SIZE)
predicted_dir_path = './mAP/predicted'
if os.path.exists(predicted_dir_path):
shutil.rmtree(predicted_dir_path)
os.mkdir(predicted_dir_path)
# ground_truth_dirs_path = './mAP/ground-truth'
# if os.path.exists(ground_truth_dir_path):
# shutil.rmtree(ground_truth_dir_path)
# os.mkdir(ground_truth_dir_path)
with get_engine(onnx_file_path, engine_file_path
) as engine, engine.create_execution_context() as context:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
for idx, input_image_path in enumerate(test_images):
#print("image path = ", input_image_path)
filename = os.path.split(input_image_path)[1]
#print("filename = ",filename)
# try:
# label_file = './coco/labels/val2014/' + os.path.splitext(filename)[0]+'.txt'
# with open(label_file, 'r') as f:
# labels = f.readlines()
# except:
# continue
# Create a pre-processor object by specifying the required input resolution for YOLOv3
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
# Load an image from the specified input path, and return it together with a pre-processed version
image_raw, image = preprocessor.process(input_image_path)
# Store the shape of the original input image in WH format, we will need it for later
# print("image shape = ", image.shape)
# print("image data = ")
# print(image)
shape_orig_WH = image_raw.size
# print("image_raw.size = ", image_raw.size)
# print("image_raw.shape = ", image_raw.shape)
# Output shapes expected by the post-processor
# output_shapes = [(1, 255, 10, 10), (1, 255, 20, 20), (1, 255, 40, 40)] #for 320
# output_shapes = [(1, 255, 13, 13), (1, 255, 26, 26), (1, 255, 52, 52)] #for 416
output_shapes = [(1, int(OUT), int(SIZE / 32), int(SIZE / 32)),
(1, int(OUT), int(SIZE / 16), int(SIZE / 16)),
(1, int(OUT), int(SIZE / 8), int(SIZE / 8))
] #for 608
# Do inference with TensorRT
trt_outputs = []
# with get_engine(onnx_file_path, engine_file_path) as engine, engine.create_execution_context() as context:
# inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference
# print('Running inference on image {}...'.format(input_image_path)) # if idx==0 else 0
# Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
inputs[0].host = image
# start = time.time()
trt_outputs, timeRec = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
# print("time: %.2f s" %(time.time()-start))
# print(trt_outputs)
timeRecSave.append(timeRec)
print('%d, Image %s, Recognition Time %0.3f seconds' %
(idx, filename, timeRec))
# # Before the post-processing, we need to reshape the outputs as the common.do_inference will give us flat arrays.
trt_outputs = [
output.reshape(shape)
for output, shape in zip(trt_outputs, output_shapes)
]
# A list of 3 three-dimensional tuples for the YOLO masks
# A list of 9 two-dimensional tuples for the YOLO anchors
postprocessor_args = {"yolo_masks": [(6, 7, 8), (3, 4, 5), (0, 1, 2)], \
"yolo_anchors": [(10, 13), (16, 30), (33, 23), (30, 61), (62, 45), \
(59, 119), (116, 90), (156, 198), (373, 326)],\
# Threshold for object coverage, float value between 0 and 1
"obj_threshold": 0.6,\
# Threshold for non-max suppression algorithm, float value between 0 and 1
"nms_threshold": 0.5,\
"yolo_input_resolution": input_resolution_yolov3_HW}
postprocessor = PostprocessYOLO(**postprocessor_args)
# # Run the post-processing algorithms on the TensorRT outputs and get the bounding box details of detected objects
boxes, classes, scores = postprocessor.process(
trt_outputs, (shape_orig_WH))
# Draw the bounding boxes onto the original input image and save it as a PNG file
if PRINT_RESULTS:
obj_detected_img = draw_bboxes(image_raw, boxes, scores,
classes, ALL_CATEGORIES)
output_image_path = './results/yolo_' + filename
obj_detected_img.save(output_image_path)
print(
'Saved image with bounding boxes of detected objects to {}.'
.format(output_image_path))
predict_result_path = os.path.join(predicted_dir_path,
str(idx) + '.txt')
# ground_truth_path = os.path.join(ground_truth_dir_path, str(idx) + '.txt')
with open(predict_result_path, 'w') as f:
if boxes is not None:
for box, score, category_idx in zip(
boxes, scores, classes):
x_coord, y_coord, width, height = box
box = [
x_coord, y_coord, x_coord + width, y_coord + height
] # fit YunYang1994's mAP calculation input format
category = ALL_CATEGORIES[category_idx]
category = "".join(category.split())
# print("score info = ", score, score.type)
box = list(map(int, box))
xmin, ymin, xmax, ymax = list(map(str, box))
# bbox_mess = ' '.join([category, score, xmin, ymin, xmax, ymax]) + '\n'
bbox_mess = ' '.join([
category, "{:.4f}".format(score), xmin, ymin, xmax,
ymax
]) + '\n'
# print(bbox_mess)
f.write(bbox_mess)
timeRecMean = np.mean(timeRecSave)
print('The mean recognition time is {0:0.3f} seconds'.format(timeRecMean))
# %% Visualization of results
if PRINT_RESULTS:
np.save('results/timeRecognition.npy', timeRecSave)
plt.figure(figsize=(8, 5))
plt.plot(timeRecSave, label='Recg_time')
plt.ylim([0, 0.05])
plt.xlabel('Test image number'),
plt.ylabel('Time [second]'),
plt.title(
'Recognition time of Yolov3_DarkNet_ONNX_TensorRT_GPU_coco_test_2017'
)
plt.hlines(y=timeRecMean,
xmin=0,
xmax=len(test_images),
linewidth=3,
color='r',
label='Mean')
plt.savefig(
'results/Yolov3_DarkNet_ONNX_TensorRT_GPU_coco_test_2017.png',
bbox_inches='tight')
plt.show()
class Detect:
def __init__(self, yaml_path):
# yaml_path 参数配置文件路径
with open(yaml_path, 'r', encoding='utf-8') as f:
self.param_dict = yaml.load(f, Loader=yaml.FullLoader)
# 获取engine context
self.engine = get_engine(self.param_dict['onnx_path'],
self.param_dict['engine_path'],
self.param_dict['input_shape'],
self.param_dict['int8_calibration'])
# context 执行在engine后面
self.context = self.engine.create_execution_context()
# yolo 数据预处理 PreprocessYOLO类
assert len(self.param_dict['input_shape']) == 4, "input_shape必须是4个维度"
batch, _, height, width = self.param_dict['input_shape']
self.preprocessor = PreprocessYOLO((height, width))
# 生成预先的anchor [x,y,w,h,f_w,f_h]: xy是feature_map的列行坐标，wh是anchor，f_wh是feature_map大小
self.prior_anchors = PriorBox(cfg=self.param_dict).forward()
# 一些配置
# 标签名字
self.all_categories = load_label_categories(
self.param_dict['label_file_path'])
classes_num = len(self.all_categories)
# trt输出shape
stride = self.param_dict['stride']
num_anchors = self.param_dict['num_anchors']
grid_num = (height // stride[0]) * (
width // stride[0]) * num_anchors[0] + (height // stride[1]) * (
width // stride[1]) * num_anchors[1] + (
height // stride[2]) * (width //
stride[2]) * num_anchors[2]
self.output_shapes = [(batch, grid_num, (classes_num + 5))]
self.img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff',
'dng'] # acceptable image suffixes
self.vid_formats = [
'mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'
] # acceptable video suffixes
# yolo 后处理， yolov4将3个输出 concat在一起，[N, AHW*3, classes_num+5]，可判断yolov4原始预测 or yolov5新式预测
self.postprocessor = PostprocessYOLO(self.prior_anchors,
self.param_dict)
def predict(self,
input_path='dog.jpg',
output_save_root='./output',
write_txt=False):
'''
:param input_path: 输入：单张图像路径，图像文件夹，单个视频文件路径
:param output_save_root: 要求全部保存到文件夹内，若是视频统一保存为mp4
:param write_txt: 将预测的框坐标-类别-置信度以txt保存
:return:
'''
# 开始判断图像，文件夹，视频
is_video = False
path = input_path
if os.path.isdir(path):
# 图像文件夹
img_names = os.listdir(path)
img_names = [
name for name in img_names
if name.split('.')[-1] in self.img_formats
]
elif os.path.isfile(path):
# 将 '/hme/ai/111.jpg' -> ('/hme/ai', '111.jpg')
path, img_name = os.path.split(path)
# 标记 video
if img_name.split('.')[-1] in self.vid_formats:
is_video = True
else:
assert img_name.split('.')[-1] in self.img_formats, "必须是单张图像路径"
img_names = [img_name]
else:
print("输入无效！！！" * 3)
# 创建保存文件夹
check_path(output_save_root)
# 判断是否是视频
if is_video:
assert img_name.count('.') == 1, "视频名字必须只有1个 . "
# 读取视频
cap = cv2.VideoCapture(os.path.join(path, img_name))
# # 获取视频的fps， width height
fps = int(cap.get(cv2.CAP_PROP_FPS))
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
num = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) # 视频总帧数
# 创建视频
video_save_path = os.path.join(
output_save_root,
img_name.split('.')[0] + '_pred.mp4')
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
video_writer = cv2.VideoWriter(video_save_path,
fourcc=fourcc,
fps=fps,
frameSize=(width, height))
else:
num = len(img_names) # 图像数量
# 推理 默认是0卡
inputs, outputs, bindings, stream = common.allocate_buffers(
self.engine)
# Do inference
for i in range(num):
# 预处理
if is_video:
cap.set(cv2.CAP_PROP_POS_FRAMES, i) # 读取指定帧
image = cap.read()
# 输入的是bgr帧矩阵
image_raw, image = self.preprocessor.process(image)
else:
# 输入的默认是图像路径
image_raw, image = self.preprocessor.process(
os.path.join(path, img_names[i]))
# Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
inputs[0].host = image
trt_outputs = common.do_inference_v2(self.context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
# list中的输出个数，本来要位于外面一层的，但是考虑重新输入图像
trt_outputs = [
output.reshape(shape)
for output, shape in zip(trt_outputs, self.output_shapes)
]
# 后处理，按照2种方式判断处理，yolov4原始的预测-参考yolov5变化后的预测
# 图像原始尺寸 WH，因为时PIL读取
shape_orig_WH = image_raw.size
# 后处理是可以处理batch>=1的，但是这里的类写的只能是batch=1
outputs_pred = self.postprocessor.process(trt_outputs,
shape_orig_WH)
# TODO 将预测的框坐标-类别-置信度 写入txt
# 画框，由于这里只能是单张图像，因此不必for遍历
boxes, classes, scores = outputs_pred[0][0]
obj_detected_img = draw_bboxes(image_raw, boxes, scores, classes,
self.all_categories)
# 视频按照帧数来保存，图像按照名字保存, 注意一般视频不会超过5位数
# TODO 视频的预测写入视频
if is_video:
obj_detected_img.save(
os.path.join(output_save_root,
str(i).zfill(5)))
else:
obj_detected_img.save(
os.path.join(output_save_root, img_names[i]))
# 若是视频，需要 release
if is_video:
cap.release()
cv2.destroyAllWindows()
def main():
"""Create a TensorRT engine for ONNX-based YOLOv3-608 and run inference."""
parser = argparse.ArgumentParser(
prog='ONNX to TensorRT conversion',
description='Convert the Yolo ONNX model to TensorRT')
parser.add_argument('--input_size', help='Input size model', default='416')
parser.add_argument('--onnx_file_path',
help='ONNX model\'s path (.onnx)',
default='../../model_data/Suspect/yolov3-suspect.onnx')
parser.add_argument('--engine_file_path',
help='TensorRT engine\'s path (.trt)',
default='trt_model/yolov3-suspect_2_fp32.trt')
parser.add_argument('--num_classes', help='Number of classes', default='3')
parser.add_argument('--dataset_path',
help='Path of the folder Dataset',
default='../../Datasets/Suspect/images-416/')
parser.add_argument(
'--pred_dataset_path',
help='Output path of Yolo predictions',
default='../../Datasets/Suspect/Predictions/TensorRT/Yolo-Tiny-128/')
parser.add_argument(
'--result_images_path',
help='Path of images with predict bounding box',
default='../../Datasets/Suspect/Images_result/TensorRT/Yolo-Tiny-128/')
args = parser.parse_args()
input_size = int(args.input_size)
onnx_file_path = args.onnx_file_path
engine_file_path = args.engine_file_path
num_classes = int(args.num_classes)
test_dataset_path = args.dataset_path
save_path = args.result_images_path
pred_dataset_path = args.pred_dataset_path
fp16_on = False
batch_size = 2
filters = (4 + 1 + num_classes) * 3
output_shapes_416 = [
(batch_size, filters, 13, 13), (batch_size, filters, 26, 26)
] # 2 ème variable = (5+nbr classes)*3 (255 pour coco, 33 pour key,...)
output_shapes_480 = [(batch_size, filters, 15, 15),
(batch_size, filters, 30, 30)]
output_shapes_544 = [(batch_size, filters, 17, 17),
(batch_size, filters, 34, 34)]
output_shapes_608 = [(batch_size, filters, 19, 19),
(batch_size, filters, 38, 38)]
output_shapes_dic = {
'416': output_shapes_416,
'480': output_shapes_480,
'544': output_shapes_544,
'608': output_shapes_608
}
filenames = glob.glob(os.path.join(test_dataset_path, '*.jpg'))
nums = len(filenames)
input_resolution_yolov3_HW = (input_size, input_size)
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
output_shapes = output_shapes_dic[str(input_size)]
postprocessor_args = { #"yolo_masks": [(3, 4, 5), (0, 1, 2)], #tiny
"yolo_masks": [(6, 7, 8), (3, 4, 5), (0, 1, 2)],
#"yolo_anchors": [(10,14), (23,27), (37,58), (81,82), (135,169), (344,319)], #tiny-yolov3
"yolo_anchors": [(10, 13), (16, 30), (33, 23), (30, 61), (62, 45),
(59, 119), (116, 90), (156, 198),
(373, 326)], #YoloV3
"obj_threshold":
0.5,
"nms_threshold":
0.35,
"yolo_input_resolution":
input_resolution_yolov3_HW
}
postprocessor = PostprocessYOLO(**postprocessor_args)
# Do inference with TensorRT
filenames_batch = []
images = []
images_raw = []
trt_outputs = []
index = 0
moy_inf_time = 0
moy = 0
with get_engine(onnx_file_path, batch_size, fp16_on, engine_file_path
) as engine, engine.create_execution_context() as context:
# inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference
for filename in filenames:
#print("Path file : ", filename)
#path = filename.split('.')[4]
#path2 = path.split('/')[4]
#print("PATH: ", path2)
#name_ann = os.path.join(pred_dataset_path, path2)
#annotation_path = name_ann + '.txt'
#print("ANNOTATION : ", annotation_path)
filenames_batch.append(filename)
'''if os.path.isfile(annotation_path) == True :
os.remove(annotation_path)
print("Delete !")'''
image_raw, image = preprocessor.process(filename)
images_raw.append(image_raw)
images.append(image)
index += 1
if index != nums and len(images_raw) != batch_size:
continue
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
images_batch = np.concatenate(images, axis=0)
inputs[0].host = images_batch
t1 = time.time()
trt_outputs = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream,
batch_size=batch_size)
t2 = time.time()
t_inf = int(round((t2 - t1) * 1000))
#print("Inf time : ",t_inf)
moy_inf_time += t_inf
#print("MOY : ", moy)
print(len(trt_outputs))
trt_outputs = [
output.reshape(shape)
for output, shape in zip(trt_outputs, output_shapes)
]
for i in range(len(filenames_batch)):
fname = filenames_batch[i].split('/')
fname = fname[-1].split('.')[0]
print(fname)
name_ann = os.path.join(pred_dataset_path, fname)
annotation_path = name_ann + '.txt'
#print("ANNOTATION : ", annotation_path)
if os.path.isfile(annotation_path) == True:
os.remove(annotation_path)
print("Delete !")
img_raw = images_raw[i]
#print(img_raw)
shape_orig_WH = img_raw.size
print("SHAPE : ", shape_orig_WH)
boxes, classes, scores = postprocessor.process(
trt_outputs, (shape_orig_WH), i)
if boxes is not None:
print("boxes size:", len(boxes))
else:
continue
# Draw the bounding boxes onto the original input image and save it as a PNG file
obj_detected_img = draw_bboxes(img_raw, boxes, scores, classes,
ALL_CATEGORIES, annotation_path)
output_image_path = save_path + fname + '_' + str(
input_size) + '_bboxes.png'
obj_detected_img.save(output_image_path, 'PNG')
print(
'Saved image with bounding boxes of detected objects to {}.'
.format(output_image_path))
filenames_batch = []
images_batch = []
images = []
images_raw = []
trt_outputs = []
print(len(filenames))
moy_inf_time = moy_inf_time / len(filenames)
print("Moyenne temps d'inférence (par image) : ", moy_inf_time, "ms")
fps = 1 / moy_inf_time * 1000
print("FPS : ", fps)
def main():
# Parse the command line parameters
parser = argparse.ArgumentParser(
description='Tiny YOLO v2 Object Detector')
parser.add_argument('--camera', '-c', \
type=int, default=0, metavar='CAMERA_NUM', \
help='Camera number')
parser.add_argument('--csi', \
action='store_true', \
help='Use CSI camera')
parser.add_argument('--width', \
type=int, default=1280, metavar='WIDTH', \
help='Capture width')
parser.add_argument('--height', \
type=int, default=720, metavar='HEIGHT', \
help='Capture height')
parser.add_argument('--objth', \
type=float, default=0.6, metavar='OBJ_THRESH', \
help='Threshold of object confidence score (between 0 and 1)')
parser.add_argument('--nmsth', \
type=float, default=0.3, metavar='NMS_THRESH', \
help='Threshold of NMS algorithm (between 0 and 1)')
parser.add_argument('--host', \
type=str, default='localhost', metavar='MQTT_HOST', \
help='MQTT remote broker IP address')
parser.add_argument('--topic', \
type=str, metavar='MQTT_TOPIC', \
help='MQTT topic to be published on')
parser.add_argument('--port', \
type=int, default=1883, metavar='MQTT_PORT', \
help='MQTT port number')
parser.add_argument('--novout', \
action='store_true', \
help='No video output')
args = parser.parse_args()
client = None
if args.topic is not None:
client = init_mqtt(args.host, args.port)
if args.csi or (args.camera < 0):
if args.camera < 0:
args.camera = 0
# Open the MIPI-CSI camera
gst_cmd = appbase.GST_STR_CSI \
% (args.width, args.height, appbase.FPS, args.camera, args.width, args.height)
cap = cv2.VideoCapture(gst_cmd, cv2.CAP_GSTREAMER)
else:
# Open the V4L2 camera
cap = cv2.VideoCapture(args.camera)
# Set the capture parameters
#cap.set(cv2.CAP_PROP_FPS, FPS) # Comment-out for OpenCV 4.1
cap.set(cv2.CAP_PROP_FRAME_WIDTH, args.width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, args.height)
# Get the actual frame size
# OpenCV 4.1 does not get the correct frame size
#act_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
#act_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
act_width = args.width
act_height = args.height
frame_info = 'Frame:%dx%d' % (act_width, act_height)
# Download the label data
categories = appbase.download_label()
# Configure the post-processing
postprocessor_args = {
# YOLO masks (Tiny YOLO v2 has only single scale.)
"yolo_masks": [(0, 1, 2, 3, 4)],
# YOLO anchors
"yolo_anchors": [(1.08, 1.19), (3.42, 4.41), (6.63, 11.38),
(9.42, 5.11), (16.62, 10.52)],
# Threshold of object confidence score (between 0 and 1)
"obj_threshold":
args.objth,
# Threshold of NMS algorithm (between 0 and 1)
"nms_threshold":
args.nmsth,
# Input image resolution
"yolo_input_resolution":
appbase.INPUT_RES,
# Number of object classes
"num_categories":
len(categories)
}
postprocessor = PostprocessYOLO(**postprocessor_args)
# Image shape expected by the post-processing
output_shapes = [(1, 125, 13, 13)]
# Download the Tiny YOLO v2 ONNX model
onnx_file_path = appbase.download_model()
# Define the file name of local saved TensorRT plan
engine_file_path = 'model.trt'
time_list = np.zeros(10)
# Load the model into TensorRT
with get_engine(onnx_file_path, engine_file_path) as engine, \
engine.create_execution_context() as context:
# Allocate buffer memory for TensorRT
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
fps = 0.0
frame_count = 0
print('video capture started')
try:
while True:
# Get the frame start time for FPS calculation
start_time = time.time()
# Capture a frame
ret, img = cap.read()
if ret != True:
continue
# Reshape the capture image for Tiny YOLO v2
rs_img = cv2.resize(img, appbase.INPUT_RES)
rs_img = cv2.cvtColor(rs_img, cv2.COLOR_BGRA2RGB)
src_img = appbase.reshape_image(rs_img)
# Execute an inference in TensorRT
inputs[0].host = src_img
trt_outputs = common.do_inference(context, bindings=bindings, \
inputs=inputs, outputs=outputs, stream=stream)
# Reshape the network output for the post-processing
trt_outputs = [output.reshape(shape) \
for output, shape in zip(trt_outputs, output_shapes)]
# Calculates the bounding boxes
boxes, classes, scores \
= postprocessor.process(trt_outputs, (act_width, act_height))
if boxes is not None:
publish_bboxes(client, args.topic, frame_count, \
img, boxes, scores, classes, categories)
if not args.novout:
# Draw the bounding boxes
if boxes is not None:
appbase.draw_bboxes(img, boxes, scores, classes,
categories)
if frame_count > 10:
fps_info = '{0}{1:.2f}'.format('FPS:', fps)
msg = '%s %s' % (frame_info, fps_info)
appbase.draw_message(img, msg)
# Show the results
cv2.imshow(appbase.WINDOW_NAME, img)
# Check if ESC key is pressed to terminate this application
key = cv2.waitKey(20)
if key == 27: # ESC
break
# Check if the window was closed
if cv2.getWindowProperty(appbase.WINDOW_NAME,
cv2.WND_PROP_AUTOSIZE) < 0:
break
# Calculate the average FPS value of the last ten frames
elapsed_time = time.time() - start_time
time_list = np.append(time_list, elapsed_time)
time_list = np.delete(time_list, 0)
avg_time = np.average(time_list)
fps = 1.0 / avg_time
frame_count += 1
except KeyboardInterrupt:
print('exitting..')
# Release the capture object
cap.release()
if not args.novout:
cv2.destroyAllWindows()
if client is not None:
client.disconnect()
def main(self, image_name):
"""Create a TensorRT engine for ONNX-based YOLOv3-608 and run inference."""
# output_shapes_416 = [(self.batch_size, 18, 13, 13), (self.batch_size, 18, 26, 26)]
output_shapes_416 = [(self.batch_size, 255, 13, 13),
(self.batch_size, 255, 26, 26)]
output_shapes_480 = [(self.batch_size, 18, 15, 15),
(self.batch_size, 18, 30, 30)]
output_shapes_544 = [(self.batch_size, 18, 17, 17),
(self.batch_size, 18, 34, 34)]
output_shapes_608 = [(self.batch_size, 18, 19, 19),
(self.batch_size, 18, 38, 38)]
output_shapes_dic = {
'416': output_shapes_416,
'480': output_shapes_480,
'544': output_shapes_544,
'608': output_shapes_608
}
# with open(input_file_list, 'r') as f:
# filenames = []
# for line in f.readlines():
# filenames.append(line.strip())
#
# filenames = glob.glob(os.path.join(IMAGE_PATH, '*.jpg'))
#
# nums = len(filenames)
# print(filenames)
input_resolution_yolov3_HW = (self.input_size, self.input_size)
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
output_shapes = output_shapes_dic[str(self.input_size)]
postprocessor_args = {
"yolo_masks": [(3, 4, 5), (0, 1, 2)],
"yolo_anchors": [(10, 13), (17, 23), (26, 29), (46, 75), (72, 167),
(179, 323)],
"obj_threshold":
0.5,
"nms_threshold":
0.35,
"yolo_input_resolution":
input_resolution_yolov3_HW
}
postprocessor = PostprocessYOLO(**postprocessor_args)
# Do inference with TensorRT
filenames_batch = []
images = []
images_raw = []
trt_outputs = []
index = 0
# with self.get_engine(self.onnx_file_path, self.batch_size, self.fp16_on, self.engine_file_path) as engine, engine.create_execution_context() as context:
# inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference
filename = image_name
filenames_batch.append(filename)
image_raw, image = preprocessor.process(filename)
images_raw.append(image_raw)
images.append(image)
# index += 1
# if len(images_raw) != self.batch_size:
# continue
inputs, outputs, bindings, stream = common.allocate_buffers(
self.engine)
images_batch = np.concatenate(images, axis=0)
inputs[0].host = images_batch
t1 = time.time()
trt_outputs = common.do_inference(self.context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream,
batch_size=self.batch_size)
t2 = time.time()
t_inf = t2 - t1
print("time spent:", t_inf)
print(len(trt_outputs))
trt_outputs = [
output.reshape(shape)
for output, shape in zip(trt_outputs, output_shapes)
]
#print('test')
for i in range(len(filenames_batch)):
fname = filenames_batch[i].split('/')
fname = fname[-1].split('.')[0]
img_raw = images_raw[i]
shape_orig_WH = img_raw.size
boxes, classes, scores = postprocessor.process(trt_outputs,
(shape_orig_WH), i)
# print("boxes size:",len(boxes))
# Draw the bounding boxes onto the original input image and save it as a PNG file
obj_detected_img = self.draw_bboxes(img_raw, boxes, scores, classes,
ALL_CATEGORIES)
if os.path.exists(self.save_path):
pass
else:
os.makedirs(self.save_path)
output_image_path = self.save_path + fname + '_' + str(
self.input_size) + '_bboxes.png'
obj_detected_img.save(output_image_path, 'PNG')
print('Saved image with bounding boxes of detected objects to {}.'.
format(output_image_path))
def main():
"""Create a TensorRT engine for ONNX-based YOLOv3-608 and run inference."""
# Try to load a previously generated YOLOv3-608 network graph in ONNX format:
input_size = 608
batch_size = 1
fp16_on = True
onnx_file_path = 'ped3_' + str(input_size) + '_' + str(batch_size) + '.onnx'
engine_file_path = 'ped3_' + str(input_size) + '_' + str(batch_size) + '.trt'
input_file_list = './ped_list.txt'
IMAGE_PATH = './images/'
save_path = './img_re/'
output_shapes_416 = [(batch_size, 18, 13, 13), (batch_size, 18, 26, 26)]
output_shapes_480 = [(batch_size, 18, 15, 15), (batch_size, 18, 30, 30)]
output_shapes_544 = [(batch_size, 18, 17, 17), (batch_size, 18, 34, 34)]
output_shapes_608 = [(batch_size, 18, 19, 19), (batch_size, 18, 38, 38)]
output_shapes_dic = {'416': output_shapes_416, '480': output_shapes_480, '544': output_shapes_544, '608': output_shapes_608}
with open(input_file_list, 'r') as f:
filenames = []
for line in f.readlines():
filenames.append(line.strip())
# filenames = glob.glob(os.path.join(IMAGE_PATH, '*.jpg'))
nums = len(filenames)
# print(filenames)
input_resolution_yolov3_HW = (input_size, input_size)
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
output_shapes = output_shapes_dic[str(input_size)]
postprocessor_args = {"yolo_masks": [(3, 4, 5), (0, 1, 2)],
"yolo_anchors": [(8,34), (14,60), (23,94), (39,149), (87,291), (187,472)],
"obj_threshold": 0.1,
"nms_threshold": 0.3,
"yolo_input_resolution": input_resolution_yolov3_HW}
postprocessor = PostprocessYOLO(**postprocessor_args)
# Do inference with TensorRT
filenames_batch = []
images = []
images_raw = []
trt_outputs = []
index = 0
with get_engine(onnx_file_path, batch_size, fp16_on, engine_file_path) as engine, engine.create_execution_context() as context:
# inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference
for filename in filenames:
filenames_batch.append(filename)
image_raw, image = preprocessor.process(filename)
images_raw.append(image_raw)
images.append(image)
index += 1
if index != nums and len(images_raw) != batch_size:
continue
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
images_batch = np.concatenate(images, axis=0)
inputs[0].host = images_batch
t1 = time.time()
trt_outputs = common.do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream, batch_size=batch_size)
t2 = time.time()
t_inf = t2 - t1
print(t_inf)
print(len(trt_outputs))
trt_outputs = [output.reshape(shape) for output, shape in zip(trt_outputs, output_shapes)]
print('test')
for i in range(len(filenames_batch)):
fname = filenames_batch[i].split('/')
fname = fname[-1].split('.')[0]
img_raw = images_raw[i]
shape_orig_WH = img_raw.size
boxes, classes, scores = postprocessor.process(trt_outputs, (shape_orig_WH), i)
# Draw the bounding boxes onto the original input image and save it as a PNG file
obj_detected_img = draw_bboxes(img_raw, boxes, scores, classes, ALL_CATEGORIES)
output_image_path = save_path + fname + '_' + str(input_size) + '_bboxes.png'
obj_detected_img.save(output_image_path, 'PNG')
print('Saved image with bounding boxes of detected objects to {}.'.format(output_image_path))
filenames_batch = []
images_batch = []
images = []
images_raw = []
trt_outputs = []
def main():
"""Create a TensorRT engine for ONNX-based YOLOv3-608 and run inference."""
onnx_file_path = 'yolov3.onnx'
engine_file_path = 'yolo_in8.trt'
cfg_file_path = "yolov3.cfg"
input_image_path = download_file(
'dog.jpg',
'https://github.com/pjreddie/darknet/raw/f86901f6177dfc6116360a13cc06ab680e0c86b0/data/dog.jpg',
checksum_reference=None)
# Two-dimensional tuple with the target network's (spatial) input resolution in HW ordered
input_resolution_yolov3_HW = (608, 608)
# Create a pre-processor object by specifying the required input resolution for YOLOv3
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
# Load an image from the specified input path, and return it together with a pre-processed version
image_raw, image = preprocessor.process(input_image_path)
# Store the shape of the original input image in WH format, we will need it for later
shape_orig_WH = image_raw.size
# Output shapes
output_shapes = [(1, 255, 19, 19), (1, 255, 38, 38), (1, 255, 76, 76)]
middle_output_shapes = []
# calibrator definition
calibration_dataset_loc = "calibration_dataset/"
calibration_cache = "yolo_calibration.cache"
calib = calibra.PythonEntropyCalibrator(calibration_dataset_loc,
cache_file=calibration_cache)
# define the layer output you want to visualize
output_layer_name = [
"001_convolutional", "002_convolutional", "003_convolutional",
"005_shortcut", "006_convolutional"
]
# get filter number of defined layer name
filter_num = get_filter_num(cfg_file_path, output_layer_name)
# Do inference with TensorRT
trt_outputs = []
with build_int8_engine(
onnx_file_path, calib, cfg_file_path, output_layer_name,
engine_file_path) as engine, engine.create_execution_context(
) as context:
start = time.time()
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference
print('Running inference on image {}...'.format(input_image_path))
# Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
# if batch size != 1 you can use load_random_batch to do test inference, here I just use 1 image as test set
# inputs[0].host = load_random_batch(calib)
inputs[0].host = image
trt_outputs = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream,
batch_size=1)
# Before doing post-processing, we need to reshape the outputs as the common.do_inference will give us flat arrays.
end = time.time()
print("Inference costs %.02f sec." % (end - start))
for i, output in enumerate(trt_outputs[:len(filter_num)]):
# length of inference output should be filter_num*h*h
if "convolutional" in output_layer_name[i]:
h = int(math.sqrt(output.shape[0] / filter_num[i]))
w = h
else:
h = int(math.sqrt(output.shape[0] / filter_num[i] / 2))
w = 2 * h
middle_output_shapes.append((1, filter_num[i], w, h))
# reshape
middle_output = [
output.reshape(shape) for output, shape in zip(
trt_outputs[:len(filter_num)], middle_output_shapes)
]
# save middle output as grey image
for name, output in zip(output_layer_name, middle_output):
w, h = output.shape[2], output.shape[3]
img = misc.toimage(output.sum(axis=1).reshape(w, h))
img.save("{}.tiff".format(name))
print("Saveing middle output {}".format(output_layer_name))
trt_outputs = [
output.reshape(shape)
for output, shape in zip(trt_outputs[len(filter_num):], output_shapes)
]
postprocessor_args = {
"yolo_masks":
[(6, 7, 8), (3, 4, 5),
(0, 1, 2)], # A list of 3 three-dimensional tuples for the YOLO masks
"yolo_anchors": [
(10, 13),
(16, 30),
(33, 23),
(30, 61),
(62,
45), # A list of 9 two-dimensional tuples for the YOLO anchors
(59, 119),
(116, 90),
(156, 198),
(373, 326)
],
"obj_threshold":
0.6, # Threshold for object coverage, float value between 0 and 1
"nms_threshold":
0.5, # Threshold for non-max suppression algorithm, float value between 0 and 1
"yolo_input_resolution":
input_resolution_yolov3_HW
}
postprocessor = PostprocessYOLO(**postprocessor_args)
# Run the post-processing algorithms on the TensorRT outputs and get the bounding box details of detected objects
boxes, classes, scores = postprocessor.process(trt_outputs,
(shape_orig_WH))
# Draw the bounding boxes onto the original input image and save it as a PNG file
obj_detected_img = draw_bboxes(image_raw, boxes, scores, classes,
ALL_CATEGORIES)
output_image_path = 'dog_bboxes.png'
obj_detected_img.save(output_image_path, 'PNG')
print('Saved image with bounding boxes of detected objects to {}.'.format(
output_image_path))
def main(width=608, height=608, batch_size=1, dataset='coco_label.txt', int8mode=False, calib_file='yolo_calibration.cache',
onnx_file='yolov3.onnx', engine_file='yolov3.trt', image_file='dog.jpg', result_file='dog_bboxes.png'):
"""Load labels of the correspond dataset."""
label_file_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), dataset)
all_categories = load_label_categories(label_file_path)
classes = len(all_categories)
"""Create a TensorRT engine for ONNX-based YOLOv3-608 and run inference."""
# Try to load a previously generated YOLOv3-608 network graph in ONNX format:
onnx_file_path = onnx_file
engine_file_path = engine_file
# Download a dog image and save it to the following file path:
input_image_path = image_file
# Two-dimensional tuple with the target network's (spatial) input resolution in HW ordered
input_resolution_yolov3_HW = (height, width)
# Create a pre-processor object by specifying the required input resolution for YOLOv3
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
# Load an image from the specified input path, and return it together with a pre-processed version
image_raw, image = preprocessor.process(input_image_path, batch_size)
# Store the shape of the original input image in WH format, we will need it for later
shape_orig_WH = image_raw.size
# Output shapes expected by the post-processor
output_shapes = [(batch_size, (classes + 5) * 3, height // 32, width // 32),
(batch_size, (classes + 5) * 3, height // 16, width // 16),
(batch_size, (classes + 5) * 3, height // 8, width // 8)]
# Do inference with TensorRT
with get_engine(onnx_file_path, width, height, batch_size, engine_file_path, int8mode, calib_file) as engine, \
engine.create_execution_context() as context:
start = time.time()
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference
print('Running inference on image {}...'.format(input_image_path))
# Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
inputs[0].host = image
trt_outputs = common.do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
end = time.time()
print("Inference costs %.03f sec." % (end - start))
# Before doing post-processing, we need to reshape the outputs as the common.do_inference will give us flat arrays.
trt_outputs = [output.reshape(shape) for output, shape in zip(trt_outputs, output_shapes)]
postprocessor_args = {"yolo_masks": [(6, 7, 8), (3, 4, 5), (0, 1, 2)], # A list of 3 three-dimensional tuples for the YOLO masks
"yolo_anchors": [(10, 13), (16, 30), (33, 23), (30, 61), (62, 45), # A list of 9 two-dimensional tuples for the YOLO anchors
(59, 119), (116, 90), (156, 198), (373, 326)],
"obj_threshold": 0.6, # Threshold for object coverage, float value between 0 and 1
"nms_threshold": 0.5, # Threshold for non-max suppression algorithm, float value between 0 and 1
"yolo_input_resolution": input_resolution_yolov3_HW}
postprocessor = PostprocessYOLO(**postprocessor_args)
# Run the post-processing algorithms on the TensorRT outputs and get the bounding box details of detected objects
trt_outputs_1 = [np.expand_dims(trt_outputs[0][0], axis=0),
np.expand_dims(trt_outputs[1][0], axis=0),
np.expand_dims(trt_outputs[2][0], axis=0)]
boxes, classes, scores = postprocessor.process(trt_outputs_1, (shape_orig_WH), classes)
# Draw the bounding boxes onto the original input image and save it as a PNG file
obj_detected_img = draw_bboxes(image_raw, boxes, scores, classes, all_categories)
output_image_path = result_file
obj_detected_img.save(output_image_path, 'PNG')
print('Saved image with bounding boxes of detected objects to {}.'.format(output_image_path))
def myinfer(image, context, inputs, outputs, bindings, stream):
# Two-dimensional tuple with the target network's (spatial) input resolution in HW ordered
input_resolution_yolov3_HW = (416, 416)
# Create a pre-processor object by specifying the required input resolution for YOLOv3
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
# Load an image from the specified input path, and return it together with a pre-processed version
image_raw, image = preprocessor.process(image)
# Store the shape of the original input image in WH format, we will need it for later
shape_orig_HW = image_raw.shape[:2]
H, W = shape_orig_HW
# Output shapes expected by the post-processor
output_shapes = [(1, 255, 13, 13), (1, 255, 26, 26)]
# Do inference with TensorRT
trt_outputs = []
# Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
inputs[0].host = image
trt_outputs = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
# Before doing post-processing, we need to reshape the outputs as the common.do_inference will give us flat arrays.
trt_outputs = [
output.reshape(shape)
for output, shape in zip(trt_outputs, output_shapes)
]
postprocessor_args = {
"yolo_masks":
[(6, 7, 8), (3, 4, 5),
(0, 1, 2)], # A list of 3 three-dimensional tuples for the YOLO masks
"yolo_anchors": [
(10, 13),
(16, 30),
(33, 23),
(30, 61),
(62,
45), # A list of 9 two-dimensional tuples for the YOLO anchors
(59, 119),
(116, 90),
(156, 198),
(373, 326)
],
"obj_threshold":
0.6, # Threshold for object coverage, float value between 0 and 1
"nms_threshold":
0.2, # Threshold for non-max suppression algorithm, float value between 0 and 1
"yolo_input_resolution":
input_resolution_yolov3_HW
}
postprocessor = PostprocessYOLO(**postprocessor_args)
# Run the post-processing algorithms on the TensorRT outputs and get the bounding box details of detected objects
boxes, classes, scores = postprocessor.process(trt_outputs,
(shape_orig_HW))
# print(boxes,classes,scores)
# Draw the bounding boxes onto the original input image and save it as a PNG file
if boxes is not None:
obj_detected_img = draw_bboxes(image_raw, boxes, scores, classes,
ALL_CATEGORIES)
output_image_path = 'dog_bboxes.png'
cv2.imshow("test", obj_detected_img)
if boxes is not None:
boxes[:, 0] = boxes[:, 0] / W
boxes[:, 1] = boxes[:, 1] / H
boxes[:, 2] = boxes[:, 2] / W
boxes[:, 3] = boxes[:, 3] / H
return boxes, classes, scores
def main(inputSize):
#Load PAR model
"""Create a TensorRT engine for ONNX-based YOLOv3-608 and run inference."""
global vs, outputFrame, lock, t0, t1, fps, sess, input_name, label_name, PAR_Model
#model = ResNet50_nFC(30)
#model = load_network(model)
#torch.save(model.state_dict(), "model")
#device = torch.device('cuda')
#model.to(device)
#model.eval()
# Set graph optimization level
#sess_options.graph_optimization_level = rt.GraphOptimizationLevel.ORT_ENABLE_EXTENDED
# To enable model serialization after graph optimization set this
#sess_options.optimized_model_filepath = "resnet50_nFC.onnx"
#sess = rt.InferenceSession("resnet50_nFC.onnx", sess_options)
#sess.set_providers(['CUDAExecutionProvider'])
#sess.set_providers(['CPUExecutionProvider'])
cuda.init()
device = cuda.Device(0)
onnx_file_path = 'yolov3-{}.onnx'.format(inputSize)
engine_file_path = 'yolov3-{}.trt'.format(inputSize)
h, w = (inputSize, inputSize)
# Two-dimensional tuple with the target network's (spatial) input resolution in HW ordered
input_resolution_yolov3_HW = (inputSize, inputSize)
# Create a pre-processor object by specifying the required input resolution for YOLOv3
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
# Output shapes expected by the post-processor
output_shapes = [(1, 255, h // 32, w // 32), (1, 255, h // 16, w // 16),
(1, 255, h // 8, w // 8)]
"""output_shapes = [(1, 255, 13, 13),
(1, 255, 26, 26)]"""
# Do inference with TensorRT
cuda.init() # Initialize CUDA
ctx = make_default_context() # Create CUDA context
postprocessor_args = {
"yolo_masks": [(6, 7, 8), (3, 4, 5), (0, 1, 2)],
"yolo_anchors": [(10, 13), (16, 30), (33, 23), (30, 61), (62, 45),
(59, 119), (116, 90), (156, 198), (373, 326)],
"obj_threshold":
0.5,
"nms_threshold":
0.35,
"yolo_input_resolution":
input_resolution_yolov3_HW
}
"""postprocessor_args = {"yolo_masks": [(3, 4, 5), (0, 1, 2)],
"yolo_anchors": [(10,14), (23,27), (37,58), (81,82), (135,169), (344,319)],
"obj_threshold": 0.4,
"nms_threshold": 0.5,
"yolo_input_resolution": input_resolution_yolov3_HW}"""
postprocessor = PostprocessYOLO(**postprocessor_args)
with get_engine(onnx_file_path, engine_file_path
) as engine, engine.create_execution_context() as context:
print("performing inference")
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
while True:
trt_outputs = []
#image_raw=vs.read()
T0 = time.time()
ret, image_raw = cap.read()
if image_raw is not None:
image_raw, image = preprocessor.process(image_raw)
shape_orig_WH = image_raw.size
inputs[0].host = image
T1 = time.time()
t0 = time.time()
trt_outputs = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
trt_outputs = [
output.reshape(shape)
for output, shape in zip(trt_outputs, output_shapes)
]
T2 = time.time()
#here we have Yolo output
boxes, classes, scores = postprocessor.process(
trt_outputs, (shape_orig_WH))
t1 = time.time()
t_inf = t1 - t0
fps = 1 / t_inf
draw = True
if (boxes is None):
print("no bboxes")
draw = False
if (classes is None):
print("no classes")
draw = False
if (scores is None):
print("no scores")
draw = False
if draw:
obj_detected_img = draw_bboxes(
image_raw,
bboxes=boxes,
confidences=scores,
categories=classes,
all_categories=ALL_CATEGORIES)
else:
obj_detected_img = image_raw
#now stream this image
T3 = time.time()
total = T3 - T0
"""print("Total time per frame: {:.3f}s (~{:.2f}FPS)".format(total,1/total))
print("Pre process: {:.2f}%".format((T1-T0)/total))
print("Inference: {:.2f}%".format((T2-T1)/total))
print("Post process: {:.2f}%".format((T3-T2)/total))"""
with lock:
outputFrame = np.array(obj_detected_img)
ctx.pop()
def main():
# Parse the command line parameters
parser = argparse.ArgumentParser(description='Tiny YOLO v2 Object Detector')
parser.add_argument('--camera', '-c', \
type=int, default=0, metavar='CAMERA_NUM', \
help='Camera number, use any negative integer for MIPI-CSI camera')
parser.add_argument('--width', \
type=int, default=1280, metavar='WIDTH', \
help='Capture width')
parser.add_argument('--height', \
type=int, default=720, metavar='HEIGHT', \
help='Capture height')
parser.add_argument('--objth', \
type=float, default=0.6, metavar='OBJ_THRESH', \
help='Threshold of object confidence score (between 0 and 1)')
parser.add_argument('--nmsth', \
type=float, default=0.3, metavar='NMS_THRESH', \
help='Threshold of NMS algorithm (between 0 and 1)')
args = parser.parse_args()
if args.camera < 0:
# Open the MIPI-CSI camera
gst_cmd = GST_STR_CSI \
% (args.width, args.height, FPS, args.width, args.height)
cap = cv2.VideoCapture(gst_cmd, cv2.CAP_GSTREAMER)
else:
# Open the V4L2 camera
cap = cv2.VideoCapture(args.camera)
# Set the capture parameters
#cap.set(cv2.CAP_PROP_FPS, FPS) # Comment-out for OpenCV 4.1
cap.set(cv2.CAP_PROP_FRAME_WIDTH, args.width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, args.height)
# Get the actual frame size
# OpenCV 4.1 does not get the correct frame size
#act_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
#act_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
act_width = args.width
act_height = args.height
frame_info = 'Frame:%dx%d' % (act_width, act_height)
# Download the label data
categories = download_label()
# Configure the post-processing
postprocessor_args = {
# YOLO masks (Tiny YOLO v2 has only single scale.)
"yolo_masks": [(0, 1, 2, 3, 4)],
# YOLO anchors
"yolo_anchors": [(1.08, 1.19), (3.42, 4.41), (6.63, 11.38), (9.42, 5.11), (16.62, 10.52)],
# Threshold of object confidence score (between 0 and 1)
"obj_threshold": args.objth,
# Threshold of NMS algorithm (between 0 and 1)
"nms_threshold": args.nmsth,
# Input image resolution
"yolo_input_resolution": INPUT_RES,
# Number of object classes
"num_categories": len(categories)}
postprocessor = PostprocessYOLO(**postprocessor_args)
# Image shape expected by the post-processing
output_shapes = [(1, 125, 13, 13)]
# Download the Tiny YOLO v2 ONNX model
onnx_file_path = download_model()
# Define the file name of local saved TensorRT plan
engine_file_path = 'model.trt'
time_list = np.zeros(10)
# Load the model into TensorRT
with get_engine(onnx_file_path, engine_file_path) as engine, \
engine.create_execution_context() as context:
# Allocate buffer memory for TensorRT
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
fps = 0.0
frame_count = 0
while True:
# Get the frame start time for FPS calculation
start_time = time.time()
# Capture a frame
ret, img = cap.read()
if ret != True:
continue
# Reshape the capture image for Tiny YOLO v2
rs_img = cv2.resize(img, INPUT_RES)
rs_img = cv2.cvtColor(rs_img, cv2.COLOR_BGRA2RGB)
src_img = reshape_image(rs_img)
# Execute an inference in TensorRT
inputs[0].host = src_img
trt_outputs = common.do_inference(context, bindings=bindings, \
inputs=inputs, outputs=outputs, stream=stream)
# Reshape the network output for the post-processing
trt_outputs = [output.reshape(shape) \
for output, shape in zip(trt_outputs, output_shapes)]
# Calculates the bounding boxes
boxes, classes, scores \
= postprocessor.process(trt_outputs, (act_width, act_height))
# Draw the bounding boxes
if boxes is not None:
draw_bboxes(img, boxes, scores, classes, categories)
#c.okihara 2020-07-22(3/3)
# Turn-on enable_GPIO and turn-off buzzer_GPIO if bounding boxes is not
else:
GPIO.output(Buz_pin, GPIO.LOW)
GPIO.output(enable_pin, GPIO.HIGH)
if frame_count > 10:
fps_info = '{0}{1:.2f}'.format('FPS:', fps)
msg = '%s %s' % (frame_info, fps_info)
draw_message(img, msg)
# Show the results
cv2.imshow(WINDOW_NAME, img)
# Check if ESC key is pressed to terminate this application
key = cv2.waitKey(20)
if key == 27: # ESC
break
# Calculate the average FPS value of the last ten frames
elapsed_time = time.time() - start_time
time_list = np.append(time_list, elapsed_time)
time_list = np.delete(time_list, 0)
avg_time = np.average(time_list)
fps = 1.0 / avg_time
frame_count += 1
# Release the capture object
cap.release()
def main():
"""Create a TensorRT engine for ONNX-based YOLOv3-608 and run inference."""
# Try to load a previously generated YOLOv3-608 network graph in ONNX format:
onnx_file_path = 'yolov3.onnx'
engine_file_path = "yolov3.trt"
# Download a dog image and save it to the following file path:
#input_image_path = 'images/dog.jpg'
# Two-dimensional tuple with the target network's (spatial) input resolution in HW ordered
input_resolution_yolov3_HW = (416, 416)
# Create a pre-processor object by specifying the required input resolution for YOLOv3
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
# Load an image from the specified input path, and return it together with a pre-processed version
#image_raw, image = preprocessor.process(input_image_path)
# Store the shape of the original input image in WH format, we will need it for later
#shape_orig_WH = image_raw.size
# Output shapes expected by the post-processor
#output_shapes = [(1, 255, 19, 19), (1, 255, 38, 38), (1, 255, 76, 76)]
output_shapes = [(1, 255, 13, 13), (1, 255, 26, 26), (1, 255, 52, 52)]
# Do inference with TensorRT
trt_outputs = []
# Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
postprocessor_args = {
"yolo_masks": [(6, 7, 8), (3, 4, 5), (0, 1, 2)],
# A list of 3 three-dimensional tuples for the YOLO masks
"yolo_anchors": [
(10, 13),
(16, 30),
(33, 23),
(30, 61),
(62, 45),
# A list of 9 two-dimensional tuples for the YOLO anchors
(59, 119),
(116, 90),
(156, 198),
(373, 326)
],
"obj_threshold":
0.6, # Threshold for object coverage, float value between 0 and 1
"nms_threshold":
0.5,
# Threshold for non-max suppression algorithm, float value between 0 and 1
"yolo_input_resolution":
input_resolution_yolov3_HW
}
capture = cv2.VideoCapture(r"D:\b站下载视频\飙车.mp4")
fourcc = cv2.VideoWriter_fourcc(*'XVID')
fps = capture.get(cv2.CAP_PROP_FPS)
size = (int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
out = cv2.VideoWriter('camera_test.mp4', fourcc, fps, size)
fps = 0
while (True):
t1 = time.time()
ref, frame = capture.read()
# 格式转变，BGRtoRGB
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# 转变成Image
frame = Image.fromarray(np.uint8(frame))
image_raw, image = preprocessor2.process(frame)
shape_orig_WH = image_raw.size
with get_engine(
onnx_file_path, engine_file_path
) as engine, engine.create_execution_context() as context:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference
print(
'Running inference on image {}...'.format('input_image_path'))
inputs[0].host = image
trt_outputs = common.do_inference_v2(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
# Before doing post-processing, we need to reshape the outputs as the common.do_inference will give us flat arrays.
trt_outputs = [
output.reshape(shape)
for output, shape in zip(trt_outputs, output_shapes)
]
# postprocessor_args = {"yolo_masks": [(6, 7, 8), (3, 4, 5), (0, 1, 2)], # A list of 3 three-dimensional tuples for the YOLO masks
# "yolo_anchors": [(10, 13), (16, 30), (33, 23), (30, 61), (62, 45), # A list of 9 two-dimensional tuples for the YOLO anchors
# (59, 119), (116, 90), (156, 198), (373, 326)],
# "obj_threshold": 0.6, # Threshold for object coverage, float value between 0 and 1
# "nms_threshold": 0.5, # Threshold for non-max suppression algorithm, float value between 0 and 1
# "yolo_input_resolution": input_resolution_yolov3_HW}
postprocessor = PostprocessYOLO(**postprocessor_args)
# Run the post-processing algorithms on the TensorRT outputs and get the bounding box details of detected objects
boxes, classes, scores = postprocessor.process(trt_outputs,
(shape_orig_WH))
# Draw the bounding boxes onto the original input image and save it as a PNG file
obj_detected_img = draw_bboxes(image_raw, boxes, scores, classes,
ALL_CATEGORIES)
frame = cv2.cvtColor(obj_detected_img, cv2.COLOR_RGB2BGR)
fps = (fps + (1. / (time.time() - t1))) / 2
print("fps= %.2f" % (fps))
frame = cv2.putText(frame, "fps= %.2f" % (fps), (0, 40),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
out.write(frame)
cv2.imshow("video", frame)
c = cv2.waitKey(1) & 0xff
if c == 27:
capture.release()
break
# output_image_path = 'dog_bboxes.png'
# obj_detected_img.save(output_image_path, 'PNG')
print('Saved image with bounding boxes of detected objects to {}.'.
format('output_image_path'))