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test setup
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√(noham)²
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96
yolov7-setup/compute_yolov3_anchors.py
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96
yolov7-setup/compute_yolov3_anchors.py
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import json
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import os, sys
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import math
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import matplotlib.pyplot as plt
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import numpy as np
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import seaborn as sns; sns.set() # for plot styling
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from PIL import Image
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imgsize = int(input("Image size for train and run? "))
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print("=> imgsize =",imgsize)
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# imgsize = 256
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BBDlabeldict = {"runner":0}
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datasetpath = '/Users/noham/Documents/GitHub/Stage-2024/test/yolov7-tracker/data/dataset2fps/'
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images_path = datasetpath+"images"
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labels_path = datasetpath+"labels"
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filenames = sorted(os.listdir(images_path))
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print("Nb d'images =",len(filenames))
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w,h = [] , []
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for i,file in enumerate(filenames):
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#print(file)
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try:
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# if True:
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im = Image.open(images_path+"/"+file)
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img_width = im.width
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img_height = im.height
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#print(img_width,img_height)
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fo = open(images_path.replace("images","labels")+"/"+file.replace("jpg","txt"), "r+")
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lines = fo.readlines()
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for line in lines:
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a = np.array(line.split(" ")).astype(float)
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w.append(a[3]*img_width)
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h.append(a[4]*img_height)
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if (a[3]*img_height<0.001) or (a[4]*img_height<0.001):
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print("!! ATTENTION : boite trop petite dans le fichier ",file, "=> il faut vérifier et supprimer la ligne dans le fichier label au format txt !!")
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except:
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pass
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w=np.asarray(w)#+0.001*(np.random.random((len(w),)) - 0.5)
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h=np.asarray(h)#+0.001*(np.random.random((len(w),)) - 0.5)
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print("h => min =",h.min()," max =",h.max())
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print("w => min =",w.min()," max =",h.max())
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print("Nb objets entourés =",len(h))
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x=[w,h]
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x=np.asarray(x)
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x=x.transpose()
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########################################## K- Means
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##########################################
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from sklearn.cluster import KMeans
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kmeans3 = KMeans(n_clusters=9)
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kmeans3.fit(x)
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y_kmeans3 = kmeans3.predict(x)
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##########################################
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centers3 = kmeans3.cluster_centers_
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yolo_anchor_average=[]
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for ind in range (9):
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yolo_anchor_average.append(np.mean(x[y_kmeans3==ind],axis=0))
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yolo_anchor_average=np.array(yolo_anchor_average)
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plt.scatter(x[:, 0], x[:, 1], c=y_kmeans3, s=2, cmap='viridis')
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plt.scatter(yolo_anchor_average[:, 0], yolo_anchor_average[:, 1], c='red', s=50)
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yoloV3anchors = yolo_anchor_average
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yoloV3anchors[:, 0] =yolo_anchor_average[:, 0] /1920 *imgsize
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yoloV3anchors[:, 1] =yolo_anchor_average[:, 1] /1056 *imgsize
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yoloV3anchors = np.rint(yoloV3anchors)
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fig, ax = plt.subplots()
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for ind in range(9):
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rectangle= plt.Rectangle((0.5*imgsize-yoloV3anchors[ind,0]/2,0.5*imgsize-yoloV3anchors[ind,1]/2), yoloV3anchors[ind,0],yoloV3anchors[ind,1] , fc='b',edgecolor='b',fill = None)
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ax.add_patch(rectangle)
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ax.set_aspect(1.0)
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plt.axis([0,imgsize,0,imgsize])
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plt.savefig(datasetpath+"boites.png",dpi=150)
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plt.show()
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yoloV3anchors.sort(axis=0)
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print("Your custom anchor boxes are {}".format(yoloV3anchors))
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F = open(datasetpath+"YOLOV_BDD_Anchors_"+str(imgsize)+".txt", "w")
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F.write("{}".format(yoloV3anchors))
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F.close()
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print("Anchors box for yaml file :")
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s_anchors = yoloV3anchors[yoloV3anchors[:, 0].argsort()]
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anchor_lists = [s_anchors[i:i+3].tolist() for i in range(0, len(s_anchors), 3)]
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# anchor_lists = [[[14.0, 37.0], [15.0, 48.0], [19.0, 53.0]], [[21.0, 70.0], [24.0, 88.0], [31.0, 112.0]], [[34.0, 136.0], [35.0, 155.0], [76.0, 161.0]]]
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p = ['] # P3/8', '] # P4/16', '] # P5/32']
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out = "anchors:"
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for l in anchor_lists:
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out += '\n' + " - [" + ", ".join([f"{int(i[0])},{int(i[1])}" for i in l]) + p[anchor_lists.index(l)]
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print(out)
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46
yolov7-setup/config_dataset1_2024_06_19.yaml
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46
yolov7-setup/config_dataset1_2024_06_19.yaml
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# Config file of dataset
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DATASET_ROOT: '/home/sylvain.faure/RN/athle/yolov7-tracker/dataset1_2024_06_19/'
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SPLIT: train
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CATEGORY_NAMES:
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- 'coureur'
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CATEGORY_DICT:
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0: 'coureur'
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CERTAIN_SEQS:
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-
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IGNORE_SEQS: # Seqs you want to ignore
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-
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YAML_DICT: './data/files_dataset1_2024_06_19.yaml' # NOTE: ONLY for yolo v5 model loader(func DetectMultiBackend)
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# Pas utilisé :
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#
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TRACK_EVAL: # If use TrackEval to evaluate, use these configs
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'DISPLAY_LESS_PROGRESS': False
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'GT_FOLDER': '/data/wujiapeng/datasets/VisDrone2019/VisDrone2019/VisDrone2019-MOT-test-dev/annotations'
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'TRACKERS_FOLDER': './tracker/results'
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'SKIP_SPLIT_FOL': True
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'TRACKER_SUB_FOLDER': ''
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'SEQ_INFO':
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'uav0000009_03358_v': 219
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'uav0000073_00600_v': 328
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'uav0000073_04464_v': 312
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'uav0000077_00720_v': 780
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'uav0000088_00290_v': 296
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'uav0000119_02301_v': 179
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'uav0000120_04775_v': 1000
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'uav0000161_00000_v': 308
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'uav0000188_00000_v': 260
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'uav0000201_00000_v': 677
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'uav0000249_00001_v': 360
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'uav0000249_02688_v': 244
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'uav0000297_00000_v': 146
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'uav0000297_02761_v': 373
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'uav0000306_00230_v': 420
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'uav0000355_00001_v': 468
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'uav0000370_00001_v': 265
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'GT_LOC_FORMAT': '{gt_folder}/{seq}.txt'
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63
yolov7-setup/info_SF.txt
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yolov7-setup/info_SF.txt
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Dépôt :
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git clone https://github.com/JackWoo0831/Yolov7-tracker
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mv Yolov7-tracker yolov7-tracker
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cd yolov7-tracker
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git checkout v2 # change to v2 branch !!
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conda create -n yolov7 python=3.9 pytorch=1.12 ## ancienne version, comme indiquée dans le readme, sinon numpy.distutils manque...
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conda activate yolov7
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( si besoin : conda remove --name yolov7 --all )
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(yolov7) 14:37:21 sylvain.faure@cinaps ~/RN/athle/yolov7-tracker $ pip3 install numpy scipy matplotlib cython pandas cuda-python
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(yolov7) 15:02:25 sylvain.faure@cinaps ~/RN/athle/yolov7-tracker $ pip3 install -r requirements.txt
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(yolov7) 15:02:25 sylvain.faure@cinaps ~/RN/athle/yolov7-tracker $ pip3 install ultralytics==8.0.94
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#################
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## TRAIN
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#################
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## entrainement avec un premier dataset : ~/RN/athle/yolov7-tracker/dataset1_2024_06_19/
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Deux fichiers de configuration créées :
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tracker/config_files/dataset1_2024_06_19.yaml
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data/files_dataset1_2024_06_19.yaml
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yolov7 sur node20 du cluster :
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## pour voir la version de CUDA du noeud :
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(yolov7) sylvain.faure@node20:~$ nvidia-smi
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=> cuda 11.4 sur node20
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cf notes d'installation de pytorch pour trouver les bonnes versions : https://pytorch.org/get-started/previous-versions/
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pip3 install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cu118
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pip3 install filterpy
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## test pour verifier que cuda est bien ok :
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>>> import torch
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>>> torch.cuda.is_available()
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True
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## lancement de l'entrainement :
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(yolov7) sylvain.faure@node20:~/RN/athle/yolov7-tracker$ python3 train.py --dataset dataset1_2024_06_19__ --workers 1 --device 0 --batch-size 4 --d
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ata data/dataset1_2024_06_19/dataset.yaml --img 1280 720 --cfg cfg/training/yolov7x_dataset1_2024_06_19.yaml --weights '' --name yolov7x-dataset1_
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2024_06_19 --hyp data/hyp.scratch.custom.yaml
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Si erreur : _pickle.UnpicklingError: STACK_GLOBAL requires str
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Effacer les fichiers .cache : liste_images.cache par exemple...
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(yolov7) sylvain.faure@node20:~/RN/athle/yolov7-tracker$rm data/dataset1_2024_06_19/liste_images.cache
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Si erreur np.int => remplacer par des np.int64
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Si erreur cuda/cpu dans loss.py :
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you have to replace the line in the file yolo7/utils/loss.py
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"from_which_layer.append((torch.ones(size=(len(b),)) * i)"
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to "from_which_layer.append((torch.ones(size=(len(b),)) * i).to('cuda'))",
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and add new line "fg_mask_inboxes = fg_mask_inboxes.to(torch.device('cuda'))"
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after "fg_mask_inboxes = matching_matrix.sum(0) > 0.0"
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so you need to do it 3 times in the file
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ssh cinaps
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73
yolov7-setup/instructions.md
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73
yolov7-setup/instructions.md
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https://github.com/JackWoo0831/Yolov7-tracker
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https://github.com/microsoft/VoTT?tab=readme-ov-file#build-and-run-from-source
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# Yolov7-tracker
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git clone https://github.com/JackWoo0831/Yolov7-tracker.git
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mv Yolov7-tracker yolov7-tracker
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cd yolov7-tracker
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git checkout v2 # change to v2 branch !!
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# Python：3.9, Pytorch: 1.12
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conda create -n yolov7 python=3.9 pytorch=1.12
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conda activate yolov7
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pip3 install numpy scipy matplotlib cython pandas cuda-python
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## ERROR: Could not find a version that satisfies the requirement cuda-python (from versions: none) Requires-Python >=3.10
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pip3 install -r requirements.txt
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pip3 install ultralytics==8.0.94
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# Setup cluster
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nvidia-smi
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=> cuda 11.4 sur node20
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cf notes d'installation de pytorch pour trouver les bonnes versions : https://pytorch.org/get-started/previous-versions/
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pip3 install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cu118
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pip3 install filterpy
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>>> import torch
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>>> torch.cuda.is_available()
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True
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# Setup macos
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conda install pytorch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 -c pytorch
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# Config train
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- create config tracker/config_files/dataset1_2024_06_19.yaml
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data/files_dataset1_2024_06_19.yaml
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- export from VoTT as json
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- convert to Yolov using vott2yolov.py (data/dataset1_2024_06_19/vott2yolov.py) -> data/dataset1_2024_06_19/labels/dataset1/ + data/dataset1_2024_06_19/images/dataset1/
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- edit data/dataset1_2024_06_19/dataset.yaml
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- get box sizes from data/dataset1_2024_06_19/YOLOV_BDD_Anchors_1280.txt -> cfg/training/yolov7x_dataset1_2024_06_19.yaml
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structure :
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dataset_name
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|---images
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|---train
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|---sequence_name1
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|---000001.jpg
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|---000002.jpg ...
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|---val ...
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|---test ...
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# Launch train
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python3 train.py --dataset dataset1_2024_06_19__ --workers 1 --device 0 --batch-size 4 --data data/dataset1_2024_06_19/datasetyaml --img 1280 720 --cfg cfg/training/yolov7x_dataset1_2024_06_19.yaml --weights '' --name yolov7x-dataset1_2024_06_19 --hyp data/hyp.scratch.custom.yaml
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Si erreur : _pickle.UnpicklingError: STACK_GLOBAL requires str
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Effacer les fichiers .cache : liste_images.cache par exemple...
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Si erreur np.int => remplacer par des np.int64
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Si erreur cuda/cpu dans loss.py :
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you have to replace the line in the file yolo7/utils/loss.py
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"from_which_layer.append((torch.ones(size=(len(b),)) * i)"
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to "from_which_layer.append((torch.ones(size=(len(b),)) * i).to('cuda'))",
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and add new line "fg_mask_inboxes = fg_mask_inboxes.to(torch.device('cuda'))"
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after "fg_mask_inboxes = matching_matrix.sum(0) > 0.0"
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so you need to do it 3 times in the file
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<!-- https://wandb.ai/noham- -->
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<!-- /Users/noham/Documents/GitHub/Stage/2024 -->
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<!-- /Users/noham/Documents/GitHub/Stage-2024 -->
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75
yolov7-setup/vott2yolov.py
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75
yolov7-setup/vott2yolov.py
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import numpy as np
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import json, sys, os, shutil
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## cliquer sur export dans Vott puis :
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# prefix = "../exports_VOTT/CH01-20230614-083436-091514-Zone2/vott-json-export/"
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# vottjson_filename = "Poulets-export.json"
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# cc = 1000000
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prefix = "/Users/noham/Documents/GitHub/Stage-2024/yolov7-tracker/"
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print('Working folder: ', prefix)
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jsonname = 'vott-json-export'
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jsonfile = 'track-2fps-export.json'
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json_path = prefix + jsonname + '/' + jsonfile
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print('Working json file: ', json_path)
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jsonfolder = prefix+jsonname
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print('Working json folder: ', jsonfolder)
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outputname = 'dataset2fps'
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print('Ooutput folder: ', outputname)
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output = '/Users/noham/Documents/GitHub/Stage-2024/test/yolov7-tracker/data/'+outputname
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print('Output folder: ', output)
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cc = 0
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label = 'runner'
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for folder in [output, output+"/images",output+"/labels"]:
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if not os.path.exists(folder):
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os.makedirs(folder)
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with open(json_path, "r") as read_file:
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data = json.load(read_file)
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imglist = []
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for a in data["assets"]:
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asset = data["assets"][a]["asset"]
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regions = data["assets"][a]["regions"]
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img_name = asset["name"]
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width = asset["size"]["width"]
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height = asset["size"]["height"]
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# print("\nimage name : ",img_name," width =",width," height =",height)
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shutil.copyfile(jsonfolder+"/"+img_name,output+"/images/image_"+str(cc).zfill(12)+".jpg")
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imglist.append(output+"/images/image_"+str(cc).zfill(12)+".jpg")
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f = open(output+"/labels/image_"+str(cc).zfill(12)+".txt", "w")
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# f = open(prefix+"/"+img_name.replace(".jpg",".txt"), "w")
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for region in regions:
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boundingBox = region["boundingBox"]
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points = region["points"]
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# print(" tags =",region["tags"]," boundingBox =",boundingBox," points =",points)
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# ## Labels :
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# D’après la doc on doit avoir :
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# Label_ID_1 X_CENTER_NORM Y_CENTER_NORM WIDTH_NORM HEIGHT_NORM
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# The label_id is the index number in the classes.names file. The id of the first label will be 0 and an increasing integer after that. Note all the position attributes in the label file are not absolute but normalised.
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# X_CENTER_NORM = X_CENTER_ABS/IMAGE_WIDTH centre image
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# Y_CENTER_NORM = Y_CENTER_ABS/IMAGE_HEIGHT centre image
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# WIDTH_NORM = WIDTH_OF_LABEL_ABS/IMAGE_WIDTH largeur boite
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# HEIGHT_NORM = HEIGHT_OF_LABEL_ABS/IMAGE_HEIGHT hauteur boite
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# dans un repere dont l'origine est en haut a gauche et X de gauche à droite, Y du haut vers le bas
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Label_ID = "0"
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X_CENTER_NORM = (boundingBox["left"]+boundingBox["width"]/2)/width
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Y_CENTER_NORM = (boundingBox["top"]+boundingBox["height"]/2)/height
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WIDTH_NORM = boundingBox["width"]/width
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HEIGHT_NORM = boundingBox["height"]/height
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f.write(Label_ID+" "+str(X_CENTER_NORM)+" "+str(Y_CENTER_NORM)+" "+str(WIDTH_NORM)+" "+str(HEIGHT_NORM)+"\n" ) # 0 = poulet si l'on a qu'une classe d'objet...
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f.close()
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cc += 1
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# print(" written file :",prefix+"/"+img_name.replace(".jpg",".txt"))
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with open(output+"/liste_images.txt", "w") as file:
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for item in imglist:
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file.write(item + "\n")
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with open(output+"/dataset.yaml", "w") as file:
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file.write(f"""train: data/{outputname}/liste_images.txt
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val: data/{outputname}/liste_images.txt
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nc: 1
|
||||
names: ['{label}']""")
|
||||
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Reference in New Issue
Block a user