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1)火焰面积模型bug修复 2）新增安防模型

This commit is contained in:
th 2025-08-16 11:15:41 +08:00
parent e82f07aa1a
commit 6e89b6587b
5 changed files with 148 additions and 65 deletions
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4
concurrency/PullVideoStreamProcess.py
View File

@ -129,7 +129,7 @@ class OnlinePullVideoStreamProcess(PullVideoStreamProcess):
frame, pull_p, width, height = pull_read_video_stream(pull_p, pull_url, width, height, width_height_3,
w_2, h_2, request_id)
if pull_queue.full():
logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
#logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
if full_timeout is None:
full_timeout = time()
if time() - full_timeout > 180:
@ -269,7 +269,7 @@ class OfflinePullVideoStreamProcess(PullVideoStreamProcess):
width, height, width_height_3, all_frames, w_2, h_2 = build_video_info(pull_url, request_id)
continue
if pull_queue.full():
logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
#logger.info("pull拉流队列满了:{}, requestId: {}", os.getppid(), request_id)
if full_timeout is None:
full_timeout = time()
if time() - full_timeout > 180:

62
concurrency/PushVideoStreamProcess.py
View File

@ -36,7 +36,7 @@ class PushStreamProcess(Process):
# 传参
self._msg, self._push_queue, self._image_queue, self._push_ex_queue, self._hb_queue, self._context = args
self._algStatus = False # 默认关闭
self._algSwitch = self._context['service']['algSwitch']
self._algSwitch = self._context['service']['algSwitch']
# 0521:
default_enabled = str(self._msg.get("defaultEnabled", "True")).lower() == "true"
@ -139,6 +139,7 @@ class OnPushStreamProcess(PushStreamProcess):
# 所有问题的矩阵集合
qs_np = None
qs_reurn = []
bp_np = None
for det in push_objs[i]:
code, det_result = det
# 每个单独模型处理
@ -167,12 +168,13 @@ class OnPushStreamProcess(PushStreamProcess):
else:
try: # 应对NaN情况
box, score, cls = xywh2xyxy2(qs)
if cls not in allowedList or score < frame_score:
continue
if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
# 借score作为points点集
box.append(qs[-1])
except:
continue
if cls not in allowedList or score < frame_score:
continue
label_array, color = label_arrays[cls], rainbows[cls]
if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
rr = t.submit(draw_name_joint, box, copy_frame,
@ -190,17 +192,24 @@ class OnPushStreamProcess(PushStreamProcess):
if cd is None:
det_xywh[code][cls] = [[cls, box, score, label_array, color]]
else:
det_xywh[code][cls].append([cls, box, score, label_array, color])
det_xywh[code][cls].append([cls, box, score, label_array, color])
if qs_np is None:
qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
box[2][0], box[2][1], box[3][0], box[3][1],
qs_np = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
box[2][0], box[2][1], box[3][0], box[3][1],
score, cls, code],dtype=np.float32)
else:
result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
result_li = np.array([box[0][0], box[0][1], box[1][0], box[1][1],
box[2][0], box[2][1], box[3][0], box[3][1],
score, cls, code],dtype=np.float32)
qs_np = np.row_stack((qs_np, result_li))
if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
if bp_np is None:
bp_np = np.array([box[0][0], box[0][1], box[-1]], dtype=object)
else:
bp_li = np.array([box[0][0], box[0][1], box[-1]], dtype=object)
bp_np = np.row_stack((bp_np, bp_li))
if logo:
frame = add_water_pic(frame, logo, request_id)
copy_frame = add_water_pic(copy_frame, logo, request_id)
@ -231,7 +240,7 @@ class OnPushStreamProcess(PushStreamProcess):
if picture_similarity:
qs_np_tmp = qs_np_id.copy()
b = np.zeros(qs_np.shape[0])
qs_reurn = np.column_stack((qs_np,b))
qs_reurn = np.column_stack((qs_np,b))
else:
qs_reurn = filterBox(qs_np, qs_np_tmp, pix_dis)
if picture_similarity:
@ -259,8 +268,12 @@ class OnPushStreamProcess(PushStreamProcess):
label_array, color = label_arrays[cls], rainbows[cls]
box = [(int(q[0]), int(q[1])), (int(q[2]), int(q[3])),
(int(q[4]), int(q[5])), (int(q[6]), int(q[7]))]
if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
box.append(qs[-1])
if bp_np is not None:
if len(bp_np.shape)==1:
bp_np = bp_np[np.newaxis, ...]
for bp in bp_np:
if np.array_equal(bp[:2], np.array([int(q[0]), int(q[1])])):
box.append(bp[-1])
is_new = False
if q[11] == 1:
is_new = True
@ -284,7 +297,7 @@ class OnPushStreamProcess(PushStreamProcess):
if push_r[0] == 2:
logger.info("拉流进程收到控制命令为:{}, requestId: {}",push_r[1] ,request_id)
if 'algStart' == push_r[1]: self._algStatus = True;logger.info("算法识别开启, requestId: {}", request_id)
if 'algStop' == push_r[1]: self._algStatus = False;logger.info("算法识别关闭, requestId: {}", request_id)
if 'algStop' == push_r[1]: self._algStatus = False;logger.info("算法识别关闭, requestId: {}", request_id)
if 'stop' == push_r[1]:
logger.info("停止推流进程, requestId: {}", request_id)
break
@ -385,9 +398,9 @@ class OffPushStreamProcess(PushStreamProcess):
# 所有问题的矩阵集合
qs_np = None
qs_reurn = []
bp_np = None
for det in push_objs[i]:
code, det_result = det
# 每个单独模型处理
# 模型编号、100帧的所有问题, 检测目标、颜色、文字图片
if len(det_result) > 0:
@ -415,10 +428,11 @@ class OffPushStreamProcess(PushStreamProcess):
else:
box, score, cls = xywh2xyxy2(qs)
if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
box.append(qs[-1])
if cls not in allowedList or score < frame_score:
continue
if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
box.append(qs[-1])
label_array, color = label_arrays[cls], rainbows[cls]
if ModelType.CHANNEL2_MODEL.value[1] == str(code) and cls == 2:
rr = t.submit(draw_name_joint, box, copy_frame, draw_config[code]["label_dict"], score, color, font_config, qs[6])
@ -426,7 +440,6 @@ class OffPushStreamProcess(PushStreamProcess):
rr = t.submit(draw_painting_joint, box, copy_frame, label_array, score, color, font_config)
thread_p.append(rr)
if det_xywh.get(code) is None:
det_xywh[code] = {}
cd = det_xywh[code].get(cls)
@ -445,6 +458,13 @@ class OffPushStreamProcess(PushStreamProcess):
score, cls, code],dtype=np.float32)
qs_np = np.row_stack((qs_np, result_li))
if ModelType.CITY_FIREAREA_MODEL.value[1]== str(code):
if bp_np is None:
bp_np = np.array([box[0][0], box[0][1],box[-1]],dtype=object)
else:
bp_li = np.array([box[0][0], box[0][1],box[-1]],dtype=object)
bp_np = np.row_stack((bp_np, bp_li))
if logo:
frame = add_water_pic(frame, logo, request_id)
copy_frame = add_water_pic(copy_frame, logo, request_id)
@ -472,7 +492,7 @@ class OffPushStreamProcess(PushStreamProcess):
if picture_similarity:
qs_np_tmp = qs_np_id.copy()
b = np.zeros(qs_np.shape[0])
qs_reurn = np.column_stack((qs_np,b))
qs_reurn = np.column_stack((qs_np,b))
else:
qs_reurn = filterBox(qs_np, qs_np_tmp, pix_dis)
if picture_similarity:
@ -501,8 +521,12 @@ class OffPushStreamProcess(PushStreamProcess):
label_array, color = label_arrays[cls], rainbows[cls]
box = [(int(q[0]), int(q[1])), (int(q[2]), int(q[3])),
(int(q[4]), int(q[5])), (int(q[6]), int(q[7]))]
if ModelType.CITY_FIREAREA_MODEL.value[1] == str(code):
box.append(qs[-1])
if bp_np is not None:
if len(bp_np.shape)==1:
bp_np = bp_np[np.newaxis, ...]
for bp in bp_np:
if np.array_equal(bp[:2], np.array([int(q[0]), int(q[1])])):
box.append(bp[-1])
is_new = False
if q[11] == 1:
is_new = True
@ -524,7 +548,7 @@ class OffPushStreamProcess(PushStreamProcess):
if push_r[0] == 2:
logger.info("拉流进程收到控制命令为:{}, requestId: {}",push_r[1] ,request_id)
if 'algStart' == push_r[1]: self._algStatus = True;logger.info("算法识别开启, requestId: {}", request_id)
if 'algStop' == push_r[1]: self._algStatus = False;logger.info("算法识别关闭, requestId: {}", request_id)
if 'algStop' == push_r[1]: self._algStatus = False;logger.info("算法识别关闭, requestId: {}", request_id)
if 'stop' == push_r[1]:
logger.info("停止推流进程, requestId: {}", request_id)
break

24
enums/ModelTypeEnum.py
View File

@ -14,6 +14,7 @@ from utilsK.drownUtils import mixDrowing_water_postprocess
from utilsK.noParkingUtils import mixNoParking_road_postprocess
from utilsK.illParkingUtils import illParking_postprocess
from utilsK.pannelpostUtils import pannel_post_process
from utilsK.securitypostUtils import security_post_process
from stdc import stdcModel
from yolov5 import yolov5Model
from p2pNet import p2NnetModel
@ -1044,7 +1045,7 @@ class ModelType(Enum):
CITY_FIREAREA_MODEL = ("30", "307", "火焰面积模型", 'FireArea', lambda device, gpuName: {
'device': device,
'gpu_name': gpuName,
'labelnames': ["火焰面积"],
'labelnames': ["火焰"],
'seg_nclass': 2, # 分割模型类别数目，默认2类
'segRegionCnt': 0,
'trtFlag_det': True,
@ -1068,6 +1069,27 @@ class ModelType(Enum):
})
CITY_SECURITY_MODEL = ("30", "308", "安防模型", 'SECURITY', lambda device, gpuName: {
'labelnames': ["带安全帽","安全帽","攀爬","斗殴","未戴安全帽"],
'postProcess': {'function': security_post_process, 'pars': {'objs': [0,1],'iou':0.25,'unhelmet':4}},
'models':
[
{
'weight': "../weights/trt/AIlib2/security/yolov5_%s_fp16.engine" % (gpuName), ###检测模型路径
'name': 'yolov5',
'model': yolov5Model,
'par': {'half': True, 'device': 'cuda:0', 'conf_thres': 0.25, 'iou_thres': 0.45,
'segRegionCnt': 1, 'trtFlag_det': True, 'trtFlag_seg': False},
}
],
'postFile': {
"rainbows": COLOR
},
'fiterList': [0,1],
"score_byClass": {"0": 0.50}
})
@staticmethod
def checkCode(code):
for model in ModelType:

7
util/ModelUtils.py
View File

@ -772,4 +772,11 @@ MODEL_CONFIG = {
lambda x, y, z: one_label(x, y, z),
lambda x: forest_process(x)
),
# 加载安防模型
ModelType.CITY_SECURITY_MODEL.value[1]: (
lambda x, y, r, t, z, h: cityManagementModel(x, y, r, ModelType.CITY_SECURITY_MODEL, t, z, h),
ModelType.CITY_SECURITY_MODEL,
lambda x, y, z: one_label(x, y, z),
lambda x: detSeg_demo2(x)
),
}

116
util/PlotsUtils.py
View File

@ -5,7 +5,7 @@ import unicodedata
from loguru import logger
FONT_PATH = "../AIlib2/conf/platech.ttf"
zhFont = ImageFont.truetype(FONT_PATH, 20, encoding="utf-8")
zhFont = ImageFont.truetype(FONT_PATH, 20, encoding="utf-8")
def get_label_array(color=None, label=None, font=None, fontSize=40, unify=False):
if unify:
@ -50,6 +50,48 @@ def get_label_array_dict(colors, fontSize=40, fontPath="platech.ttf"):
zh_dict[code] = arr
return zh_dict
def get_label_left(x0,y1,label_array,img):
imh, imw = img.shape[0:2]
lh, lw = label_array.shape[0:2]
# x1 框框左上x位置 + 描述的宽
# y0 框框左上y位置 - 描述的高
x1, y0 = x0 + lw, y1 - lh
# 如果y0小于0, 说明超过上边框
if y0 < 0:
y0 = 0
# y1等于文字高度
y1 = y0 + lh
# 如果y1框框的高大于图片高度
if y1 > imh:
# y1等于图片高度
y1 = imh
# y0等于y1减去文字高度
y0 = y1 - lh
# 如果x0小于0
if x0 < 0:
x0 = 0
x1 = x0 + lw
if x1 > imw:
x1 = imw
x0 = x1 - lw
return x0,y0,x1,y1
def get_label_right(x1,y0,label_array):
lh, lw = label_array.shape[0:2]
# x1 框框右上x位置 + 描述的宽
# y0 框框右上y位置 - 描述的高
x0, y1 = x1 - lw, y0 - lh
# 如果y0小于0, 说明超过上边框
if y0 < 0 or y1 < 0:
y1 = 0
# y1等于文字高度
y0 = y1 + lh
# 如果x0小于0
if x0 < 0 or x1 < 0:
x0 = 0
x1 = x0 + lw
return x0,y1,x1,y0
def xywh2xyxy(box):
if not isinstance(box[0], (list, tuple, np.ndarray)):
@ -78,48 +120,41 @@ def xy2xyxy(box):
def draw_painting_joint(box, img, label_array, score=0.5, color=None, config=None, isNew=False):
# 识别问题描述图片的高、宽
# 图片的长度和宽度
imh, imw = img.shape[0:2]
points = np.array([])
lh, lw = label_array.shape[0:2]
tl = config[0]
if isinstance(box[-1], np.ndarray):
font = ImageFont.truetype(FONT_PATH, 10, encoding='utf-8')
points = box[-1]
arrea = cv2.contourArea(points)
label = '火焰面积:%s'%arrea
label = '火焰'
#arealabel = '面积:%s'%f"{arrea:.1e}"
arealabel = '面积:%s'%arrea
label_array_area = get_label_array(color, arealabel, font, 10)
label_array = get_label_array(color, label, font, 10)
else:
label = ' %.2f' % score
lh, lw = label_array.shape[0:2]
box = xywh2xyxy(box[:4])
lh_area, lw_area = label_array_area.shape[0:2]
box = box[:4]
# 框框左上的位置
x0, y1 = box[0][0], max(box[0][1]-lh_area-3,0)
x1, y0 = box[1][0], box[1][1]
x0_label,y0_label,x1_label,y1_label = get_label_left(x0, y1,label_array,img)
x0_area, y0_area, x1_area, y1_area = get_label_right(x1, y0, label_array_area)
img[y0_label:y1_label, x0_label:x1_label, :] = label_array
img[y0_area:y1_area, x0_area:x1_area, :] = label_array_area
#cv2.drawContours(img, points, -1, color, tl)
cv2.polylines(img, [points], False, color, 1)
if lw_area < box[1][0]-box[0][0]:
box = [(x0, y1), (x1, y1),(x1, box[2][1]),(x0, box[2][1])]
else:
box = [(x0_label, y1), (x1, y1), (x1, box[2][1]), (x0_label, box[2][1])]
box = np.asarray(box, np.int32)
cv2.polylines(img, [box], True, color, 1)
return img, box
label = ' %.2f' % score
box = xywh2xyxy(box)
# 框框左上的位置
x0, y1 = box[0][0], box[0][1]
# if score_location == 'leftTop':
# x0, y1 = box[0][0], box[0][1]
# # 框框左下的位置
# elif score_location == 'leftBottom':
# x0, y1 = box[3][0], box[3][1]
# else:
# x0, y1 = box[0][0], box[0][1]
# x1 框框左上x位置 + 描述的宽
# y0 框框左上y位置 - 描述的高
x1, y0 = x0 + lw, y1 - lh
# 如果y0小于0, 说明超过上边框
if y0 < 0:
y0 = 0
# y1等于文字高度
y1 = y0 + lh
# 如果y1框框的高大于图片高度
if y1 > imh:
# y1等于图片高度
y1 = imh
# y0等于y1减去文字高度
y0 = y1 - lh
# 如果x0小于0
if x0 < 0:
x0 = 0
x1 = x0 + lw
if x1 > imw:
x1 = imw
x0 = x1 - lw
x0, y0, x1, y1 = get_label_left(x0, y1, label_array, img)
# box_tl = max(int(round(imw / 1920 * 3)), 1) or round(0.002 * (imh + imw) / 2) + 1
'''
1. img（array） 为ndarray类型（可以为cv.imread）直接读取的数据
@ -129,14 +164,9 @@ def draw_painting_joint(box, img, label_array, score=0.5, color=None, config=Non
5. thickness（int）：画线的粗细
6. shift：顶点坐标中小数的位数
'''
tl = config[0]
img[y0:y1, x0:x1, :] = label_array
if points.size != 0:
cv2.drawContours(img, points, -1, color, 2)
return img, box
else:
box1 = np.asarray(box, np.int32)
cv2.polylines(img, [box1], True, color, tl)
box1 = np.asarray(box, np.int32)
cv2.polylines(img, [box1], True, color, tl)
pts_cls = [(x0, y0), (x1, y1)]
# 把英文字符score画到类别旁边
# tl = max(int(round(imw / 1920 * 3)), 1) or round(0.002 * (imh + imw) / 2) + 1