import cv2

import numpy as np

import math

import os

import struct

from tqdm import tqdm

from glob import glob

PALETTE = np.random.randint(0, 255, $255, 3$ , dtype=np.uint32)

模型输入尺寸（W, H），用于把模型坐标缩放回原图

MODEL_IN_W = 608

MODEL_IN_H = 736

调试：同时输出"模型输入尺寸"的可视化

DEBUG_DRAW_MODEL_SIZE = True

zh

imagespath = "/ai/DataSets/OD_FSD_zh/TI_test/rm/4/image/"

imagespath = "/ai/DataSets/OD_FSD_zh/TI_test/ppchen/DVR-20250804152834-2382380-PLR/image/"

savepath = "/ai/zhdata/multiyolov5_point_v2/test_images/out"

zh1 = 0

def readTensor(tensorFile):

global zh1

tensor = open(tensorFile,'rb')

infer_data = np.fromfile(tensor, dtype=np.int32)

print(infer_data.shape)

soltnum = int(len(infer_data) / 20)

im0 = cv2.imread(imagespath + tensorFile.split('/') $-1$ $:-8$ + '.bmp')

if im0 is None:

print("读取图片失败:", tensorFile)

return

h0, w0 = im0.shape $:2$

sx = w0 / float(MODEL_IN_W)

sy = h0 / float(MODEL_IN_H)

point_all =\[\]

for i in range(soltnum):

point_dict1={}

raw_x1 = max(int(infer_data $20\*i+6$ ), 0)

raw_y1 = max(int(infer_data $20\*i+7$ ), 0)

raw_x2 = max(int(infer_data $20\*i+8$ ), 0)

raw_y2 = max(int(infer_data $20\*i+9$ ), 0)

raw_x3 = max(int(infer_data $20\*i+10$ ), 0)

raw_y3 = max(int(infer_data $20\*i+11$ ), 0)

raw_x4 = max(int(infer_data $20\*i+12$ ), 0)

raw_y4 = max(int(infer_data $20\*i+13$ ), 0)

x1 = max(int(raw_x1 * sx), 0)

y1 = max(int(raw_y1 * sy), 0)

x2 = max(int(raw_x2 * sx), 0)

y2 = max(int(raw_y2 * sy), 0)

x3 = max(int(raw_x3 * sx), 0)

y3 = max(int(raw_y3 * sy), 0)

x4 = max(int(raw_x4 * sx), 0)

y4 = max(int(raw_y4 * sy), 0)

zh = struct.unpack('!f',int(bin(infer_data $20\*i+4$ ) $2:$ ,2).to_bytes(4,byteorder='big'))

point_dict1 $"conf"$ = struct.unpack('!f',int(bin(infer_data $20\*i+4$ ) $2:$ ,2).to_bytes(4,byteorder='big')) $0$

point_dict1 $"isOccupied"$ = struct.unpack('!f',int(bin(infer_data $20\*i+14$ ) $2:$ ,2).to_bytes(4,byteorder='big')) $0$

point_dict1 $"isVIP"$ = struct.unpack('!f',int(bin(infer_data $20\*i+15$ ) $2:$ ,2).to_bytes(4,byteorder='big')) $0$

point_dict1 $"iswoman"$ = struct.unpack('!f',int(bin(infer_data $20\*i+16$ ) $2:$ ,2).to_bytes(4,byteorder='big')) $0$

point_dict1 $"isdisabled"$ = struct.unpack('!f',int(bin(infer_data $20\*i+17$ ) $2:$ ,2).to_bytes(4,byteorder='big')) $0$

point_dict1 $"ischarging"$ = struct.unpack('!f',int(bin(infer_data $20\*i+18$ ) $2:$ ,2).to_bytes(4,byteorder='big')) $0$

point_dict1 $"step"$ = struct.unpack('!f',int(bin(infer_data $20\*i+19$ ) $2:$ ,2).to_bytes(4,byteorder='big')) $0$

point_dict1 $"name"$ = str(struct.unpack('!f',int(bin(infer_data $20\*i+5$ ) $2:$ ,2).to_bytes(4,byteorder='big')) $0$ )

point_dict1 $"delrule"$ = 0

point_dict1 $"pointx"$ = $x1,x2,x3,x4$

point_dict1 $"pointy"$ = $y1,y2,y3,y4$

if DEBUG_DRAW_MODEL_SIZE:

point_dict1 $"raw_pointx"$ = $raw_x1, raw_x2, raw_x3, raw_x4$

point_dict1 $"raw_pointy"$ = $raw_y1, raw_y2, raw_y3, raw_y4$

if x3 > 1000 or x4 > 1000 or x1 > 1000 or x2 > 1000 or y3 > 1000 or y4 > 1000 or y1 > 1000 or y2 > 1000:

zh1 +=1

kk = struct.unpack('!f',int(bin(infer_data $20\*i+17$ ) $2:$ ,2).to_bytes(4,byteorder='big')) $0$

print("数据解析错误"+ tensorFile + str(x3) + '--'+ str(zh1))

break

point_all.append(point_dict1)

end = len(point_all)

for i in range(len(point_all)):

if point_all $i$ $"delrule"$ == 0:

for j in range(i+1,end):

#简单就是求入口顶点之间的距离

xi1 = point_all $i$ $'pointx'$ $0$

yi1 = point_all $i$ $'pointy'$ $0$

xi2 = point_all $i$ $'pointx'$ $1$

yi2 = point_all $i$ $'pointy'$ $1$

xj1 = point_all $j$ $'pointx'$ $0$

yj1 = point_all $j$ $'pointy'$ $0$

xj2 = point_all $j$ $'pointx'$ $1$

yj2 = point_all $j$ $'pointy'$ $1$

if (abs(xi1 - xj1) + abs(yi1 - yj1)) < 40 or (abs(xi2 - xj2) + abs(yi2 - yj2)) < 40:

point_all $j$ $"delrule"$ = 1

for i in range(len(point_all)):

if point_all $i$ $"delrule"$ == 0:

line1 = $point_all\[i$ $'pointx'$ $0$ ,point_all $i$ $'pointy'$ $0$ ,point_all $i$ $'pointx'$ $3$ ,point_all $i$ $'pointy'$ $3$ ]

line2 = $point_all\[i$ $'pointx'$ $1$ ,point_all $i$ $'pointy'$ $1$ ,point_all $i$ $'pointx'$ $2$ ,point_all $i$ $'pointy'$ $2$ ]

vec1 = $line1\[2$ -line1 $0$ ,line1 $3$ -line1 $1$ ]

vec2 = $line2\[2$ -line2 $0$ ,line2 $3$ -line2 $1$ ]

#计算向量的点积和模长

dot_product = vec1 $0$ * vec2 $0$ + vec1 $1$ * vec2 $1$

m1 = math.sqrt(vec1 $0$ **2 + vec1 $1$ **2) + 0.000000000001

m2 = math.sqrt(vec2 $0$ **2 + vec2 $1$ **2) + 0.000000000001

val = dot_product/(m1 * m2)

if val > 1:

val = 1

if val < -1:

val = -15

radians = math.acos(val)

du = math.degrees(radians)

if du > 20:

point_all $i$ $"delrule"$ = 2

if 1:

for point_i in point_all:

if point_i $"delrule"$ == 0:

if point_i $"conf"$ > 0.45:#0.45

print(point_i $"conf"$ )

cv2.putText(im0, f'{point_i $"conf"$ :.3f}',

(point_i $"pointx"$ $0$ + 6, point_i $"pointy"$ $0$ + 6),

cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0),2) #置信度

cv2.putText(im0, point_i $"name"$ ,

(point_i $"pointx"$ $0$ + 6, point_i $"pointy"$ $0$ + 30),

cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0),2) #类别

if float(point_i $"isOccupied"$ )> 0.1: #0.5

cv2.putText(im0, "Occ :" + f'{point_i $"isOccupied"$ :.3f}',

(point_i $"pointx"$ $0$ + 6, point_i $"pointy"$ $0$ + 54),

cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0),2) #是否被占用

if float(point_i $"isVIP"$ ) > 0.5:

cv2.putText(im0, "VIP :" + f'{point_i $"isVIP"$ :.3f}',

(point_i $"pointx"$ $0$ + 6, point_i $"pointy"$ $0$ + 78),

cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0),2) #是否VIP车位

if float(point_i $"iswoman"$ ) > 0.5:

cv2.putText(im0, "woman :" + f'{point_i $"iswoman"$ :.3f}',

(point_i $"pointx"$ $0$ + 6, point_i $"pointy"$ $0$ + 102),

cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0),2) #是否女性车位

if float(point_i $"isdisabled"$ ) > 0.5:

cv2.putText(im0, "disab :" + f'{point_i $"isdisabled"$ :.3f}',

(point_i $"pointx"$ $0$ + 6, point_i $"pointy"$ $0$ + 126),

cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0),2) #是否残疾人车位

if float(point_i $"ischarging"$ ) > 0.5:

cv2.putText(im0, "charg :" + f'{point_i $"ischarging"$ :.3f}',

(point_i $"pointx"$ $0$ + 6, point_i $"pointy"$ $0$ + 150),

cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0),2) #是否充电车位

if float(point_i $"step"$ ) > 0.5:

cv2.putText(im0, "step :" + f'{point_i $"step"$ :.3f}',

(point_i $"pointx"$ $0$ + 6, point_i $"pointy"$ $0$ + 174),

cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0),2) #是否阶梯形车位

cv2.arrowedLine(im0, (point_i $"pointx"$ $0$ , point_i $"pointy"$ $0$ ),(point_i $"pointx"$ $1$ , point_i $"pointy"$ $1$ ), (0, 255, 0), 1, cv2.LINE_AA)

cv2.arrowedLine(im0, (point_i $"pointx"$ $1$ , point_i $"pointy"$ $1$ ),(point_i $"pointx"$ $2$ , point_i $"pointy"$ $2$ ), (255, 255, 0), 1, cv2.LINE_AA)

cv2.arrowedLine(im0, (point_i $"pointx"$ $2$ , point_i $"pointy"$ $2$ ),(point_i $"pointx"$ $3$ , point_i $"pointy"$ $3$ ), (255, 255, 0), 1, cv2.LINE_AA)

cv2.arrowedLine(im0, (point_i $"pointx"$ $3$ , point_i $"pointy"$ $3$ ),(point_i $"pointx"$ $0$ , point_i $"pointy"$ $0$ ), (255, 255, 0), 1, cv2.LINE_AA)

else:

cv2.putText(im0, f'{point_i $"conf"$ :.3f}',

(point_i $"pointx"$ $0$ + 6, point_i $"pointy"$ $0$ + 6),

cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255),3)

cv2.arrowedLine(im0, (point_i $"pointx"$ $0$ , point_i $"pointy"$ $0$ ),(point_i $"pointx"$ $1$ , point_i $"pointy"$ $1$ ), (0, 0, 255), 1, cv2.LINE_AA)

cv2.arrowedLine(im0, (point_i $"pointx"$ $1$ , point_i $"pointy"$ $1$ ),(point_i $"pointx"$ $2$ , point_i $"pointy"$ $2$ ), (0, 0, 255), 1, cv2.LINE_AA)

cv2.arrowedLine(im0, (point_i $"pointx"$ $2$ , point_i $"pointy"$ $2$ ),(point_i $"pointx"$ $3$ , point_i $"pointy"$ $3$ ), (0, 0, 255), 1, cv2.LINE_AA)

cv2.arrowedLine(im0, (point_i $"pointx"$ $3$ , point_i $"pointy"$ $3$ ),(point_i $"pointx"$ $0$ , point_i $"pointy"$ $0$ ), (0, 0, 255), 1, cv2.LINE_AA)

if point_i $"delrule"$ == 1:

cv2.putText(im0, f'{point_i $"conf"$ :.3f}',

(point_i $"pointx"$ $0$ + 6, point_i $"pointy"$ $0$ + 6),

cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255),3)

cv2.arrowedLine(im0, (point_i $"pointx"$ $0$ , point_i $"pointy"$ $0$ ),(point_i $"pointx"$ $1$ , point_i $"pointy"$ $1$ ), (0, 0, 0), 1, cv2.LINE_AA)

cv2.arrowedLine(im0, (point_i $"pointx"$ $1$ , point_i $"pointy"$ $1$ ),(point_i $"pointx"$ $2$ , point_i $"pointy"$ $2$ ), (0, 0, 0), 1, cv2.LINE_AA)

cv2.arrowedLine(im0, (point_i $"pointx"$ $2$ , point_i $"pointy"$ $2$ ),(point_i $"pointx"$ $3$ , point_i $"pointy"$ $3$ ), (0, 0, 0), 1, cv2.LINE_AA)

cv2.arrowedLine(im0, (point_i $"pointx"$ $3$ , point_i $"pointy"$ $3$ ),(point_i $"pointx"$ $0$ , point_i $"pointy"$ $0$ ), (0, 0, 0), 1, cv2.LINE_AA)

if point_i $"delrule"$ == 2:

cv2.putText(im0, f'{point_i $"conf"$ :.3f}',

(point_i $"pointx"$ $0$ + 6, point_i $"pointy"$ $0$ + 6),

cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255),3)

cv2.arrowedLine(im0, (point_i $"pointx"$ $0$ , point_i $"pointy"$ $0$ ),(point_i $"pointx"$ $1$ , point_i $"pointy"$ $1$ ), (0, 0, 0), 1, cv2.LINE_AA)

cv2.arrowedLine(im0, (point_i $"pointx"$ $1$ , point_i $"pointy"$ $1$ ),(point_i $"pointx"$ $2$ , point_i $"pointy"$ $2$ ), (0, 0, 0), 1, cv2.LINE_AA)

cv2.arrowedLine(im0, (point_i $"pointx"$ $2$ , point_i $"pointy"$ $2$ ),(point_i $"pointx"$ $3$ , point_i $"pointy"$ $3$ ), (0, 0, 0), 1, cv2.LINE_AA)

cv2.arrowedLine(im0, (point_i $"pointx"$ $3$ , point_i $"pointy"$ $3$ ),(point_i $"pointx"$ $0$ , point_i $"pointy"$ $0$ ), (0, 0, 0), 1, cv2.LINE_AA)

zh = "/ai/TopViewMul/4/psd_out/" + tensorFile.split('/') $-1$ $:-8$ + '.bmp'

print(zh)

cv2.imwrite(zh, im0)

if DEBUG_DRAW_MODEL_SIZE:

im_model = cv2.resize(im0, (MODEL_IN_W, MODEL_IN_H))

for point_i in point_all:

if point_i.get("raw_pointx") is None:

continue

if point_i $"delrule"$ == 0 and point_i $"conf"$ > 0.45:

cv2.arrowedLine(im_model, (point_i $"raw_pointx"$ $0$ , point_i $"raw_pointy"$ $0$ ), (point_i $"raw_pointx"$ $1$ , point_i $"raw_pointy"$ $1$ ), (0, 255, 0), 1, cv2.LINE_AA)

cv2.arrowedLine(im_model, (point_i $"raw_pointx"$ $1$ , point_i $"raw_pointy"$ $1$ ), (point_i $"raw_pointx"$ $2$ , point_i $"raw_pointy"$ $2$ ), (255, 255, 0), 1, cv2.LINE_AA)

cv2.arrowedLine(im_model, (point_i $"raw_pointx"$ $2$ , point_i $"raw_pointy"$ $2$ ), (point_i $"raw_pointx"$ $3$ , point_i $"raw_pointy"$ $3$ ), (255, 255, 0), 1, cv2.LINE_AA)

cv2.arrowedLine(im_model, (point_i $"raw_pointx"$ $3$ , point_i $"raw_pointy"$ $3$ ), (point_i $"raw_pointx"$ $0$ , point_i $"raw_pointy"$ $0$ ), (255, 255, 0), 1, cv2.LINE_AA)

zh_model = "/ai/TopViewMul/4/psd_out/" + tensorFile.split('/') $-1$ $:-8$ + '_model.bmp'

cv2.imwrite(zh_model, im_model)

zh

for binpath in tqdm(glob(os.path.join("/ai/DataSets/OD_FSD_zh/TI_test/rm/2/psd/",'*.bin'))):

readTensor(binpath)

num=0

for binpath in tqdm(glob(os.path.join("/ai/TopViewMul/4/out_480_544_2039/psd/",'*.bin'))):

readTensor(binpath)

num+=1

if num==100:

print('exit')

exit(0)

draw_tensor2psd.py0126v1

模型输入尺寸（W, H），用于把模型坐标缩放回原图

调试：同时输出"模型输入尺寸"的可视化

zh

imagespath = "/ai/DataSets/OD_FSD_zh/TI_test/ppchen/DVR-20250804152834-2382380-PLR/image/"

savepath = "/ai/zhdata/multiyolov5_point_v2/test_images/out"

zh = struct.unpack('!f',int(bin(infer_data20\*i+4)2:,2).to_bytes(4,byteorder='big'))

zh

for binpath in tqdm(glob(os.path.join("/ai/DataSets/OD_FSD_zh/TI_test/rm/2/psd/",'*.bin'))):

readTensor(binpath)

zh = struct.unpack('!f',int(bin(infer_data $20\*i+4$ ) $2:$ ,2).to_bytes(4,byteorder='big'))