openvino_yoloV3
import sys, os, cv2, time
import numpy as np, math
from argparse import ArgumentParser
from openvino.inference_engine import IECore
m_input_size = 416 #模型输入416*416
yolo_scale_13 = 13 #cell 13*13
yolo_scale_26 = 26 #cell 26*26
yolo_scale_52 = 52 #cell 52*52
classes = 2 #类别个数
coords = 4 #坐标占位 (中心点x,中心点y，高度，宽度)
num = 3 #每个cell三个检测框
anchors = [10,13,16,30,33,23,30,61,62,45,59,119,116,90,156,198,373,326] #先验框
LABELS = ("operator1", "operator2") #label列表
label_text_color = (255, 255, 255)
label_background_color = (125, 175, 75)
box_color = (255, 128, 0)
box_thickness = 1
def build_argparser():
parser = ArgumentParser()
parser.add_argument("-d", "--device", help="Specify the target device to infer on; CPU, GPU, FPGA or MYRIAD is acceptable. \
Sample will look for a suitable plugin for device specified (CPU by default)", default="CPU", type=str)
return parser
#获取展平后的数据的索引
def EntryIndex(side, lcoords, lclasses, location, entry):
n = int(location / (side * side))
loc = location % (side * side)
return int(n * side * side * (lcoords + lclasses + 1) + entry * side * side + loc)
class DetectionObject():
xmin = 0
ymin = 0
xmax = 0
ymax = 0
class_id = 0
confidence = 0.0
def __init__(self, x, y, h, w, class_id, confidence, h_scale, w_scale):
self.xmin = int((x - w / 2) * w_scale)
self.ymin = int((y - h / 2) * h_scale)
self.xmax = int(self.xmin + w * w_scale)
self.ymax = int(self.ymin + h * h_scale)
self.class_id = class_id
self.confidence = confidence
# IOU交并比
def IntersectionOverUnion(box_1, box_2):
width_of_overlap_area = min(box_1.xmax, box_2.xmax) - max(box_1.xmin, box_2.xmin)
height_of_overlap_area = min(box_1.ymax, box_2.ymax) - max(box_1.ymin, box_2.ymin)
area_of_overlap = 0.0
if (width_of_overlap_area < 0.0 or height_of_overlap_area < 0.0):
area_of_overlap = 0.0
else:
area_of_overlap = width_of_overlap_area * height_of_overlap_area
box_1_area = (box_1.ymax - box_1.ymin) * (box_1.xmax - box_1.xmin)
box_2_area = (box_2.ymax - box_2.ymin) * (box_2.xmax - box_2.xmin)
area_of_union = box_1_area + box_2_area - area_of_overlap
retval = 0.0
if area_of_union <= 0.0:
retval = 0.0
else:
retval = (area_of_overlap / area_of_union)
return retval
def ParseYOLOV3Output(blob, resized_im_h, resized_im_w, original_im_h, original_im_w, threshold, objects):
# 输出结果 例子V3
# conv2d_58/BiasAdd （1, 13, 13, 255） anchor(116,90, 156,198, 373,326)
# conv2d_66/BiasAdd （1, 26, 26, 255） anchor(30,61, 62,45, 59,119)
# conv2d_74/BiasAdd （1, 52, 52, 255） anchor(10,13, 16,30, 33,23)
# 255(B, Cx, Cy, N*85) N:num of cell 85:(x, y, h, w, box_score, class_no_1, …, class_no_80)
# print(blob.shape) #(B,N*(5+class),Cx,Cy)
out_blob_h = blob.shape[2] #Cx
out_blob_w = blob.shape[3] #Cy
side = out_blob_h
anchor_offset = 0
if len(anchors) == 18: ## YoloV3 #根据anchor判断yolo类型
if side == yolo_scale_13:
anchor_offset = 2 * 6
elif side == yolo_scale_26:
anchor_offset = 2 * 3
elif side == yolo_scale_52:
anchor_offset = 2 * 0
elif len(anchors) == 12: ## tiny-YoloV3
if side == yolo_scale_13:
anchor_offset = 2 * 3
elif side == yolo_scale_26:
anchor_offset = 2 * 0
else: ## ???
if side == yolo_scale_13:
anchor_offset = 2 * 6
elif side == yolo_scale_26:
anchor_offset = 2 * 3
elif side == yolo_scale_52:
anchor_offset = 2 * 0
side_square = side * side
output_blob = blob.flatten() #展平（例：1*(5+2)*3*13*13）
for i in range(side_square):
row = int(i / side) #cell的x
col = int(i % side) #cell的y
for n in range(num): #3个预测框
obj_index = EntryIndex(side, coords, classes, n * side * side + i, coords) #置信度索引 box_score
box_index = EntryIndex(side, coords, classes, n * side * side + i, 0) #表示框x索引 x
scale = output_blob[obj_index] #取得置信度
if (scale < threshold):
continue
# 中心点坐标(x,y)
x = (col + output_blob[box_index + 0 * side_square]) / side * resized_im_w #根据原始坐标，得到绝对坐标
y = (row + output_blob[box_index + 1 * side_square]) / side * resized_im_h
# 高度和宽度
height = math.exp(output_blob[box_index + 3 * side_square]) * anchors[anchor_offset + 2 * n + 1] #得到绝对坐标
width = math.exp(output_blob[box_index + 2 * side_square]) * anchors[anchor_offset + 2 * n]
for j in range(classes):
class_index = EntryIndex(side, coords, classes, n * side_square + i, coords + 1 + j) #类索引
prob = scale * output_blob[class_index] #类置信度
if prob < threshold:
continue
# 将矩形框及置信度、类别进行存储
obj = DetectionObject(x, y, height, width, j, prob, (original_im_h / resized_im_h), (original_im_w / resized_im_w))
objects.append(obj)
return objects
def main_IE_infer():
camera_width = 320
camera_height = 240
fps = ""
framepos = 0
frame_count = 0
vidfps = 0
skip_frame = 0
elapsedTime = 0
new_w = int(camera_width * m_input_size/camera_width)
new_h = int(camera_height * m_input_size/camera_height)
args = build_argparser().parse_args()
model_xml = "/home/bhc/darknet-master/backup1/16/16.xml" #<--- CPU
#model_xml = "lrmodels/YoloV3/FP16/frozen_yolo_v3.xml" #<--- MYRIAD
model_bin = os.path.splitext(model_xml)[0] + ".bin"
# cap = cv2.VideoCapture(0)
# cap.set(cv2.CAP_PROP_FPS, 30)
# cap.set(cv2.CAP_PROP_FRAME_WIDTH, camera_width)
# cap.set(cv2.CAP_PROP_FRAME_HEIGHT, camera_height)
cap = cv2.VideoCapture("/home/bhc/BHC/Q3/1_Astemo_DL/video/D16_operator/D16_20211025135235.mp4")
camera_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) #视频的宽度
camera_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) #视频的高度
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) #视频总帧数
vidfps = int(cap.get(cv2.CAP_PROP_FPS)) #视频fps
print("videosFrameCount =", str(frame_count))
print("videosFPS =", str(vidfps))
time.sleep(1)
ie = IECore()
net = ie.read_network(model=model_xml) #读取模型
input_blob = next(iter(net.input_info)) #模型输入
exec_net = ie.load_network(network=net, device_name="CPU") #加载模型
while cap.isOpened(): #视频是否打开
t1 = time.time()
## Uncomment only when playing video files
#cap.set(cv2.CAP_PROP_POS_FRAMES, framepos)
ret, image = cap.read() #读取帧
if not ret:
break
resized_image = cv2.resize(image, (new_w, new_h), interpolation = cv2.INTER_CUBIC) #resize操作到输入要求
canvas = np.full((m_input_size, m_input_size, 3), 128)
canvas[(m_input_size-new_h)//2:(m_input_size-new_h)//2 + new_h,(m_input_size-new_w)//2:(m_input_size-new_w)//2 + new_w, :] = resized_image
prepimg = canvas
prepimg = prepimg[np.newaxis, :, :, :] # Batch size axis add
prepimg = prepimg.transpose((0, 3, 1, 2)) # NHWC to NCHW
outputs = exec_net.infer(inputs={input_blob: prepimg}) # 推理BHWC（1,416,416,3） 3:RGB
objects = []
for output in outputs.values():
objects = ParseYOLOV3Output(output, new_h, new_w, camera_height, camera_width, 0.7, objects) #解析推理结果
# Filtering overlapping boxes
objlen = len(objects)
for i in range(objlen):
if (objects[i].confidence == 0.0):
continue
for j in range(i + 1, objlen):
if (IntersectionOverUnion(objects[i], objects[j]) >= 0.4): #如果IOU大于0.4的则将置信度设置为0
objects[j].confidence = 0
# Drawing boxes
for obj in objects:
if obj.confidence < 0.2:
continue
label = obj.class_id
confidence = obj.confidence
if confidence > 0.2:
label_text = LABELS[label] + " (" + "{:.1f}".format(confidence * 100) + "%)"
cv2.rectangle(image, (obj.xmin, obj.ymin), (obj.xmax, obj.ymax), box_color, box_thickness)
cv2.putText(image, label_text, (obj.xmin, obj.ymin - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, label_text_color, 1)
cv2.putText(image, fps, (camera_width - 170, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (38, 0, 255), 1, cv2.LINE_AA)
cv2.imshow("Result", image)
if cv2.waitKey(1)&0xFF == ord('q'):
break
elapsedTime = time.time() - t1
fps = "(Playback) {:.1f} FPS".format(1/elapsedTime)
## frame skip, video file only
#skip_frame = int((vidfps - int(1/elapsedTime)) / int(1/elapsedTime))
#framepos += skip_frame
cv2.destroyAllWindows()
del net
del exec_net
if __name__ == '__main__':
sys.exit(main_IE_infer() or 0)
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