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bug fix

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upczww 2020-09-23 09:15:01 +08:00 committed by wang-xinyu
parent e3db20280b
commit 7cd9368fb6
1 changed files with 74 additions and 53 deletions
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125
yolov5/yolov5_trt.py
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@ -16,15 +16,14 @@ import tensorrt as trt
import torch import torch
import torchvision import torchvision
INPUT_W = 608 INPUT_W = 608
INPUT_H = 608 INPUT_H = 608
CONF_THRESH = 0.5 CONF_THRESH = 0.1
IOU_THRESHOLD = 0.4 IOU_THRESHOLD = 0.4
def plot_one_box(x, img, color=None, label=None, line_thickness=None): def plot_one_box(x, img, color=None, label=None, line_thickness=None):
''' """
description: Plots one bounding box on image img, description: Plots one bounding box on image img,
this function comes from YoLov5 project. this function comes from YoLov5 project.
param: param:
@ -36,9 +35,10 @@ def plot_one_box(x, img, color=None, label=None, line_thickness=None):
return: return:
no return no return
''' """
tl = line_thickness or round( tl = (
0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1
) # line/font thickness
color = color or [random.randint(0, 255) for _ in range(3)] color = color or [random.randint(0, 255) for _ in range(3)]
c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
@ -47,14 +47,23 @@ def plot_one_box(x, img, color=None, label=None, line_thickness=None):
t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled
cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, cv2.putText(
[225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) img,
label,
(c1[0], c1[1] - 2),
0,
tl / 3,
[225, 255, 255],
thickness=tf,
lineType=cv2.LINE_AA,
)
class YoLov5TRT(object): class YoLov5TRT(object):
''' """
description: A YOLOv5 class that warps TensorRT ops, preprocess and postprocess ops. description: A YOLOv5 class that warps TensorRT ops, preprocess and postprocess ops.
''' """
def __init__(self, engine_file_path): def __init__(self, engine_file_path):
# Create a Context on this device, # Create a Context on this device,
self.cfx = cuda.Device(0).make_context() self.cfx = cuda.Device(0).make_context()
@ -74,8 +83,7 @@ class YoLov5TRT(object):
bindings = [] bindings = []
for binding in engine: for binding in engine:
size = trt.volume(engine.get_binding_shape( size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size
binding)) * engine.max_batch_size
dtype = trt.nptype(engine.get_binding_dtype(binding)) dtype = trt.nptype(engine.get_binding_dtype(binding))
# Allocate host and device buffers # Allocate host and device buffers
host_mem = cuda.pagelocked_empty(size, dtype) host_mem = cuda.pagelocked_empty(size, dtype)
@ -115,7 +123,8 @@ class YoLov5TRT(object):
bindings = self.bindings bindings = self.bindings
# Do image preprocess # Do image preprocess
input_image, image_raw, origin_h, origin_w = self.preprocess_image( input_image, image_raw, origin_h, origin_w = self.preprocess_image(
input_image_path) input_image_path
)
# Copy input image to host buffer # Copy input image to host buffer
np.copyto(host_inputs[0], input_image.ravel()) np.copyto(host_inputs[0], input_image.ravel())
# Transfer input data to the GPU. # Transfer input data to the GPU.
@ -132,15 +141,21 @@ class YoLov5TRT(object):
output = host_outputs[0] output = host_outputs[0]
# Do postprocess # Do postprocess
result_boxes, result_scores, result_classid = self.post_process( result_boxes, result_scores, result_classid = self.post_process(
output, origin_h, origin_w) output, origin_h, origin_w
)
# Draw rectangles and labels on the original image # Draw rectangles and labels on the original image
for i in range(len(result_boxes)): for i in range(len(result_boxes)):
box = result_boxes[i] box = result_boxes[i]
plot_one_box(box, image_raw, label="{}:{:.2f}".format( plot_one_box(
categories[int(result_classid[i])], result_scores[i])) box,
image_raw,
label="{}:{:.2f}".format(
categories[int(result_classid[i])], result_scores[i]
),
)
parent, filename = os.path.split(input_image_path) parent, filename = os.path.split(input_image_path)
save_name = os.path.join(parent, "output_"+filename) save_name = os.path.join(parent, "output_" + filename)
#　Save image # 　Save image
cv2.imwrite(save_name, image_raw) cv2.imwrite(save_name, image_raw)
def destory(self): def destory(self):
@ -148,7 +163,7 @@ class YoLov5TRT(object):
self.cfx.pop() self.cfx.pop()
def preprocess_image(self, input_image_path): def preprocess_image(self, input_image_path):
''' """
description: Read an image from image path, convert it to RGB, description: Read an image from image path, convert it to RGB,
resize and pad it to target size, normalize to [0,1], resize and pad it to target size, normalize to [0,1],
transform to NCHW format. transform to NCHW format.
@ -159,7 +174,7 @@ class YoLov5TRT(object):
image_raw: the original image image_raw: the original image
h: original height h: original height
w: original width w: original width
''' """
image_raw = cv2.imread(input_image_path) image_raw = cv2.imread(input_image_path)
h, w, c = image_raw.shape h, w, c = image_raw.shape
image = cv2.cvtColor(image_raw, cv2.COLOR_BGR2RGB) image = cv2.cvtColor(image_raw, cv2.COLOR_BGR2RGB)
@ -169,18 +184,21 @@ class YoLov5TRT(object):
if r_h > r_w: if r_h > r_w:
tw = INPUT_W tw = INPUT_W
th = int(r_w * h) th = int(r_w * h)
tx = 0 tx1 = tx2 = 0
ty = int((INPUT_H - th) / 2) ty1 = int((INPUT_H - th) / 2)
ty2 = INPUT_H - th - ty1
else: else:
tw = int(r_h * w) tw = int(r_h * w)
th = INPUT_H th = INPUT_H
tx = int((INPUT_W - tw) / 2) tx1 = int((INPUT_W - tw) / 2)
ty = 0 tx2 = INPUT_W - tw - tx1
ty1 = ty2 = 0
# Resize the image with long side while maintaining ratio # Resize the image with long side while maintaining ratio
image = cv2.resize(image, (tw, th)) image = cv2.resize(image, (tw, th))
# Pad the short side with (128,128,128) # Pad the short side with (128,128,128)
image = cv2.copyMakeBorder( image = cv2.copyMakeBorder(
image, ty, ty, tx, tx, cv2.BORDER_CONSTANT, (128, 128, 128)) image, ty1, ty2, tx1, tx2, cv2.BORDER_CONSTANT, (128, 128, 128)
)
image = image.astype(np.float32) image = image.astype(np.float32)
# Normalize to [0,1] # Normalize to [0,1]
image /= 255.0 image /= 255.0
@ -193,7 +211,7 @@ class YoLov5TRT(object):
return image, image_raw, h, w return image, image_raw, h, w
def xywh2xyxy(self, origin_h, origin_w, x): def xywh2xyxy(self, origin_h, origin_w, x):
''' """
description: Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right description: Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
param: param:
origin_h: height of original image origin_h: height of original image
@ -201,28 +219,27 @@ class YoLov5TRT(object):
x: A boxes tensor, each row is a box [center_x, center_y, w, h] x: A boxes tensor, each row is a box [center_x, center_y, w, h]
return: return:
y: A boxes tensor, each row is a box [x1, y1, x2, y2] y: A boxes tensor, each row is a box [x1, y1, x2, y2]
''' """
y = torch.zeros_like(x) if isinstance( y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x)
x, torch.Tensor) else np.zeros_like(x)
r_w = INPUT_W / origin_w r_w = INPUT_W / origin_w
r_h = INPUT_H / origin_h r_h = INPUT_H / origin_h
if r_h > r_w: if r_h > r_w:
y[:, 0] = x[:, 0] - x[:, 2]/2 y[:, 0] = x[:, 0] - x[:, 2] / 2
y[:, 2] = x[:, 0] + x[:, 2]/2 y[:, 2] = x[:, 0] + x[:, 2] / 2
y[:, 1] = x[:, 1] - x[:, 3]/2 - (INPUT_H - r_w * origin_h) / 2 y[:, 1] = x[:, 1] - x[:, 3] / 2 - (INPUT_H - r_w * origin_h) / 2
y[:, 3] = x[:, 1] + x[:, 3]/2 - (INPUT_H - r_w * origin_h) / 2 y[:, 3] = x[:, 1] + x[:, 3] / 2 - (INPUT_H - r_w * origin_h) / 2
y /= r_w y /= r_w
else: else:
y[:, 0] = x[:, 0] - x[:, 2]/2 - (INPUT_W - r_h * origin_w) / 2 y[:, 0] = x[:, 0] - x[:, 2] / 2 - (INPUT_W - r_h * origin_w) / 2
y[:, 2] = x[:, 0] + x[:, 2]/2 - (INPUT_W - r_h * origin_w) / 2 y[:, 2] = x[:, 0] + x[:, 2] / 2 - (INPUT_W - r_h * origin_w) / 2
y[:, 1] = x[:, 1] - x[:, 3]/2 y[:, 1] = x[:, 1] - x[:, 3] / 2
y[:, 3] = x[:, 1] + x[:, 3]/2 y[:, 3] = x[:, 1] + x[:, 3] / 2
y /= r_h y /= r_h
return y return y
def post_process(self, output, origin_h, origin_w): def post_process(self, output, origin_h, origin_w):
''' """
description: postprocess the prediction description: postprocess the prediction
param: param:
output: A tensor likes [num_boxes,cx,cy,w,h,conf,cls_id, cx,cy,w,h,conf,cls_id, ...] output: A tensor likes [num_boxes,cx,cy,w,h,conf,cls_id, cx,cy,w,h,conf,cls_id, ...]
@ -232,7 +249,7 @@ class YoLov5TRT(object):
result_boxes: finally boxes, a boxes tensor, each row is a box [x1, y1, x2, y2] result_boxes: finally boxes, a boxes tensor, each row is a box [x1, y1, x2, y2]
result_scores: finally scores, a tensor, each element is the score correspoing to box result_scores: finally scores, a tensor, each element is the score correspoing to box
result_classid: finally classid, a tensor, each element is the classid correspoing to box result_classid: finally classid, a tensor, each element is the classid correspoing to box
''' """
# Get the num of boxes detected # Get the num of boxes detected
num = int(output[0]) num = int(output[0])
# Reshape to a two dimentional ndarray # Reshape to a two dimentional ndarray
@ -253,8 +270,7 @@ class YoLov5TRT(object):
# Trandform bbox from [center_x, center_y, w, h] to [x1, y1, x2, y2] # Trandform bbox from [center_x, center_y, w, h] to [x1, y1, x2, y2]
boxes = self.xywh2xyxy(origin_h, origin_w, boxes) boxes = self.xywh2xyxy(origin_h, origin_w, boxes)
# Do nms # Do nms
indices = torchvision.ops.nms( indices = torchvision.ops.nms(boxes, scores, iou_threshold=IOU_THRESHOLD).cpu()
boxes, scores, iou_threshold=IOU_THRESHOLD).cpu()
result_boxes = boxes[indices, :].cpu() result_boxes = boxes[indices, :].cpu()
result_scores = scores[indices].cpu() result_scores = scores[indices].cpu()
result_classid = classid[indices].cpu() result_classid = classid[indices].cpu()
@ -271,30 +287,35 @@ class myThread(threading.Thread):
self.func(*self.args) self.func(*self.args)
if __name__ == '__main__': if __name__ == "__main__":
# load custom plugins # load custom plugins
PLUGIN_LIBRARY = 'build/libmyplugins.so' PLUGIN_LIBRARY = "build/libmyplugins.so"
ctypes.CDLL(PLUGIN_LIBRARY) ctypes.CDLL(PLUGIN_LIBRARY)
engine_file_path = "build/yolov5s.engine" engine_file_path = "build/yolov5s.engine"
# load coco labels # load coco labels
coco_labels = "coco_labels.txt"
categories = [] categories = ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
with open(coco_labels, "r") as f: "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
for line in f: "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
categories.append(line.strip()) "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
"microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
"hair drier", "toothbrush"]
# a YoLov5TRT instance # a YoLov5TRT instance
yolov5_warpper = YoLov5TRT(engine_file_path) yolov5_wrapper = YoLov5TRT(engine_file_path)
# from https://github.com/ultralytics/yolov5/tree/master/inference/images
input_image_paths = ["zidane.jpg", "bus.jpg"] input_image_paths = ["zidane.jpg", "bus.jpg"]
for input_image_path in input_image_paths: for input_image_path in input_image_paths:
# create a new thread to do inference # create a new thread to do inference
thread1 = myThread(yolov5_warpper.infer, [input_image_path]) thread1 = myThread(yolov5_wrapper.infer, [input_image_path])
thread1.start() thread1.start()
thread1.join() thread1.join()
# destory the instance # destory the instance
yolov5_warpper.destory() yolov5_wrapper.destory()