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)