From 5c9313c4ca64028a9db82dafef56cab87227783e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=88=9A=28noham=29=C2=B2?= <100566912+NohamR@users.noreply.github.com> Date: Wed, 17 Jul 2024 23:50:12 +0200 Subject: [PATCH] test setup --- yolov7-setup/compute_yolov3_anchors.py | 96 ++++++++++++++++++++ yolov7-setup/config_dataset1_2024_06_19.yaml | 46 ++++++++++ yolov7-setup/info_SF.txt | 63 +++++++++++++ yolov7-setup/instructions.md | 73 +++++++++++++++ yolov7-setup/vott2yolov.py | 75 +++++++++++++++ 5 files changed, 353 insertions(+) create mode 100644 yolov7-setup/compute_yolov3_anchors.py create mode 100644 yolov7-setup/config_dataset1_2024_06_19.yaml create mode 100644 yolov7-setup/info_SF.txt create mode 100644 yolov7-setup/instructions.md create mode 100644 yolov7-setup/vott2yolov.py diff --git a/yolov7-setup/compute_yolov3_anchors.py b/yolov7-setup/compute_yolov3_anchors.py new file mode 100644 index 0000000..ac0ba36 --- /dev/null +++ b/yolov7-setup/compute_yolov3_anchors.py @@ -0,0 +1,96 @@ +import json +import os, sys +import math +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns; sns.set() # for plot styling +from PIL import Image + +imgsize = int(input("Image size for train and run? ")) +print("=> imgsize =",imgsize) +# imgsize = 256 + +BBDlabeldict = {"runner":0} + +datasetpath = '/Users/noham/Documents/GitHub/Stage-2024/test/yolov7-tracker/data/dataset2fps/' +images_path = datasetpath+"images" +labels_path = datasetpath+"labels" + +filenames = sorted(os.listdir(images_path)) +print("Nb d'images =",len(filenames)) +w,h = [] , [] +for i,file in enumerate(filenames): + #print(file) + try: + # if True: + im = Image.open(images_path+"/"+file) + img_width = im.width + img_height = im.height + #print(img_width,img_height) + fo = open(images_path.replace("images","labels")+"/"+file.replace("jpg","txt"), "r+") + lines = fo.readlines() + for line in lines: + a = np.array(line.split(" ")).astype(float) + w.append(a[3]*img_width) + h.append(a[4]*img_height) + if (a[3]*img_height<0.001) or (a[4]*img_height<0.001): + print("!! ATTENTION : boite trop petite dans le fichier ",file, "=> il faut vérifier et supprimer la ligne dans le fichier label au format txt !!") + except: + pass +w=np.asarray(w)#+0.001*(np.random.random((len(w),)) - 0.5) +h=np.asarray(h)#+0.001*(np.random.random((len(w),)) - 0.5) +print("h => min =",h.min()," max =",h.max()) +print("w => min =",w.min()," max =",h.max()) +print("Nb objets entourés =",len(h)) + +x=[w,h] +x=np.asarray(x) +x=x.transpose() +########################################## K- Means +########################################## + +from sklearn.cluster import KMeans +kmeans3 = KMeans(n_clusters=9) +kmeans3.fit(x) +y_kmeans3 = kmeans3.predict(x) + +########################################## +centers3 = kmeans3.cluster_centers_ + +yolo_anchor_average=[] +for ind in range (9): + yolo_anchor_average.append(np.mean(x[y_kmeans3==ind],axis=0)) + +yolo_anchor_average=np.array(yolo_anchor_average) + +plt.scatter(x[:, 0], x[:, 1], c=y_kmeans3, s=2, cmap='viridis') +plt.scatter(yolo_anchor_average[:, 0], yolo_anchor_average[:, 1], c='red', s=50) +yoloV3anchors = yolo_anchor_average +yoloV3anchors[:, 0] =yolo_anchor_average[:, 0] /1920 *imgsize +yoloV3anchors[:, 1] =yolo_anchor_average[:, 1] /1056 *imgsize +yoloV3anchors = np.rint(yoloV3anchors) +fig, ax = plt.subplots() +for ind in range(9): + 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) + ax.add_patch(rectangle) +ax.set_aspect(1.0) +plt.axis([0,imgsize,0,imgsize]) +plt.savefig(datasetpath+"boites.png",dpi=150) +plt.show() + +yoloV3anchors.sort(axis=0) +print("Your custom anchor boxes are {}".format(yoloV3anchors)) + +F = open(datasetpath+"YOLOV_BDD_Anchors_"+str(imgsize)+".txt", "w") +F.write("{}".format(yoloV3anchors)) +F.close() + +print("Anchors box for yaml file :") +s_anchors = yoloV3anchors[yoloV3anchors[:, 0].argsort()] +anchor_lists = [s_anchors[i:i+3].tolist() for i in range(0, len(s_anchors), 3)] +# 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]]] +p = ['] # P3/8', '] # P4/16', '] # P5/32'] +out = "anchors:" +for l in anchor_lists: + out += '\n' + " - [" + ", ".join([f"{int(i[0])},{int(i[1])}" for i in l]) + p[anchor_lists.index(l)] +print(out) \ No newline at end of file diff --git a/yolov7-setup/config_dataset1_2024_06_19.yaml b/yolov7-setup/config_dataset1_2024_06_19.yaml new file mode 100644 index 0000000..d7e89ad --- /dev/null +++ b/yolov7-setup/config_dataset1_2024_06_19.yaml @@ -0,0 +1,46 @@ +# Config file of dataset + +DATASET_ROOT: '/home/sylvain.faure/RN/athle/yolov7-tracker/dataset1_2024_06_19/' +SPLIT: train +CATEGORY_NAMES: + - 'coureur' + +CATEGORY_DICT: + 0: 'coureur' + +CERTAIN_SEQS: + - + +IGNORE_SEQS: # Seqs you want to ignore + - + +YAML_DICT: './data/files_dataset1_2024_06_19.yaml' # NOTE: ONLY for yolo v5 model loader(func DetectMultiBackend) + + +# Pas utilisé : +# +TRACK_EVAL: # If use TrackEval to evaluate, use these configs + 'DISPLAY_LESS_PROGRESS': False + 'GT_FOLDER': '/data/wujiapeng/datasets/VisDrone2019/VisDrone2019/VisDrone2019-MOT-test-dev/annotations' + 'TRACKERS_FOLDER': './tracker/results' + 'SKIP_SPLIT_FOL': True + 'TRACKER_SUB_FOLDER': '' + 'SEQ_INFO': + 'uav0000009_03358_v': 219 + 'uav0000073_00600_v': 328 + 'uav0000073_04464_v': 312 + 'uav0000077_00720_v': 780 + 'uav0000088_00290_v': 296 + 'uav0000119_02301_v': 179 + 'uav0000120_04775_v': 1000 + 'uav0000161_00000_v': 308 + 'uav0000188_00000_v': 260 + 'uav0000201_00000_v': 677 + 'uav0000249_00001_v': 360 + 'uav0000249_02688_v': 244 + 'uav0000297_00000_v': 146 + 'uav0000297_02761_v': 373 + 'uav0000306_00230_v': 420 + 'uav0000355_00001_v': 468 + 'uav0000370_00001_v': 265 + 'GT_LOC_FORMAT': '{gt_folder}/{seq}.txt' \ No newline at end of file diff --git a/yolov7-setup/info_SF.txt b/yolov7-setup/info_SF.txt new file mode 100644 index 0000000..7d10c80 --- /dev/null +++ b/yolov7-setup/info_SF.txt @@ -0,0 +1,63 @@ +Dépôt : +git clone https://github.com/JackWoo0831/Yolov7-tracker + +mv Yolov7-tracker yolov7-tracker +cd yolov7-tracker +git checkout v2 # change to v2 branch !! + +conda create -n yolov7 python=3.9 pytorch=1.12 ## ancienne version, comme indiquée dans le readme, sinon numpy.distutils manque... +conda activate yolov7 + +( si besoin : conda remove --name yolov7 --all ) + +(yolov7) 14:37:21 sylvain.faure@cinaps ~/RN/athle/yolov7-tracker $ pip3 install numpy scipy matplotlib cython pandas cuda-python +(yolov7) 15:02:25 sylvain.faure@cinaps ~/RN/athle/yolov7-tracker $ pip3 install -r requirements.txt + +(yolov7) 15:02:25 sylvain.faure@cinaps ~/RN/athle/yolov7-tracker $ pip3 install ultralytics==8.0.94 + +################# +## TRAIN +################# + +## entrainement avec un premier dataset : ~/RN/athle/yolov7-tracker/dataset1_2024_06_19/ +Deux fichiers de configuration créées : +tracker/config_files/dataset1_2024_06_19.yaml +data/files_dataset1_2024_06_19.yaml + +yolov7 sur node20 du cluster : + +## pour voir la version de CUDA du noeud : +(yolov7) sylvain.faure@node20:~$ nvidia-smi +=> cuda 11.4 sur node20 + +cf notes d'installation de pytorch pour trouver les bonnes versions : https://pytorch.org/get-started/previous-versions/ +pip3 install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cu118 +pip3 install filterpy + +## test pour verifier que cuda est bien ok : +>>> import torch +>>> torch.cuda.is_available() +True + +## lancement de l'entrainement : + +(yolov7) sylvain.faure@node20:~/RN/athle/yolov7-tracker$ python3 train.py --dataset dataset1_2024_06_19__ --workers 1 --device 0 --batch-size 4 --d +ata data/dataset1_2024_06_19/dataset.yaml --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 + + +Si erreur : _pickle.UnpicklingError: STACK_GLOBAL requires str +Effacer les fichiers .cache : liste_images.cache par exemple... +(yolov7) sylvain.faure@node20:~/RN/athle/yolov7-tracker$rm data/dataset1_2024_06_19/liste_images.cache + +Si erreur np.int => remplacer par des np.int64 + +Si erreur cuda/cpu dans loss.py : +you have to replace the line in the file yolo7/utils/loss.py +"from_which_layer.append((torch.ones(size=(len(b),)) * i)" +to "from_which_layer.append((torch.ones(size=(len(b),)) * i).to('cuda'))", +and add new line "fg_mask_inboxes = fg_mask_inboxes.to(torch.device('cuda'))" +after "fg_mask_inboxes = matching_matrix.sum(0) > 0.0" +so you need to do it 3 times in the file + +ssh cinaps \ No newline at end of file diff --git a/yolov7-setup/instructions.md b/yolov7-setup/instructions.md new file mode 100644 index 0000000..eb7d5a6 --- /dev/null +++ b/yolov7-setup/instructions.md @@ -0,0 +1,73 @@ +https://github.com/JackWoo0831/Yolov7-tracker +https://github.com/microsoft/VoTT?tab=readme-ov-file#build-and-run-from-source + +# Yolov7-tracker +git clone https://github.com/JackWoo0831/Yolov7-tracker.git +mv Yolov7-tracker yolov7-tracker +cd yolov7-tracker +git checkout v2 # change to v2 branch !! + +# Python：3.9, Pytorch: 1.12 +conda create -n yolov7 python=3.9 pytorch=1.12 +conda activate yolov7 +pip3 install numpy scipy matplotlib cython pandas cuda-python +## ERROR: Could not find a version that satisfies the requirement cuda-python (from versions: none) Requires-Python >=3.10 +pip3 install -r requirements.txt +pip3 install ultralytics==8.0.94 + +# Setup cluster +nvidia-smi +=> cuda 11.4 sur node20 + +cf notes d'installation de pytorch pour trouver les bonnes versions : https://pytorch.org/get-started/previous-versions/ +pip3 install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cu118 +pip3 install filterpy + +>>> import torch +>>> torch.cuda.is_available() +True + +# Setup macos +conda install pytorch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 -c pytorch + +# Config train +- create config tracker/config_files/dataset1_2024_06_19.yaml +data/files_dataset1_2024_06_19.yaml + +- export from VoTT as json +- 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/ +- edit data/dataset1_2024_06_19/dataset.yaml +- get box sizes from data/dataset1_2024_06_19/YOLOV_BDD_Anchors_1280.txt -> cfg/training/yolov7x_dataset1_2024_06_19.yaml + +structure : +dataset_name + |---images + |---train + |---sequence_name1 + |---000001.jpg + |---000002.jpg ... + |---val ... + |---test ... + + | + +# Launch train +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 + +Si erreur : _pickle.UnpicklingError: STACK_GLOBAL requires str +Effacer les fichiers .cache : liste_images.cache par exemple... + +Si erreur np.int => remplacer par des np.int64 + +Si erreur cuda/cpu dans loss.py : +you have to replace the line in the file yolo7/utils/loss.py +"from_which_layer.append((torch.ones(size=(len(b),)) * i)" +to "from_which_layer.append((torch.ones(size=(len(b),)) * i).to('cuda'))", +and add new line "fg_mask_inboxes = fg_mask_inboxes.to(torch.device('cuda'))" +after "fg_mask_inboxes = matching_matrix.sum(0) > 0.0" +so you need to do it 3 times in the file + + + + + \ No newline at end of file diff --git a/yolov7-setup/vott2yolov.py b/yolov7-setup/vott2yolov.py new file mode 100644 index 0000000..c735882 --- /dev/null +++ b/yolov7-setup/vott2yolov.py @@ -0,0 +1,75 @@ +import numpy as np +import json, sys, os, shutil + +## cliquer sur export dans Vott puis : + +# prefix = "../exports_VOTT/CH01-20230614-083436-091514-Zone2/vott-json-export/" +# vottjson_filename = "Poulets-export.json" +# cc = 1000000 + +prefix = "/Users/noham/Documents/GitHub/Stage-2024/yolov7-tracker/" +print('Working folder: ', prefix) +jsonname = 'vott-json-export' +jsonfile = 'track-2fps-export.json' +json_path = prefix + jsonname + '/' + jsonfile +print('Working json file: ', json_path) +jsonfolder = prefix+jsonname +print('Working json folder: ', jsonfolder) + +outputname = 'dataset2fps' +print('Ooutput folder: ', outputname) +output = '/Users/noham/Documents/GitHub/Stage-2024/test/yolov7-tracker/data/'+outputname +print('Output folder: ', output) +cc = 0 + +label = 'runner' + +for folder in [output, output+"/images",output+"/labels"]: + if not os.path.exists(folder): + os.makedirs(folder) + +with open(json_path, "r") as read_file: + data = json.load(read_file) + +imglist = [] +for a in data["assets"]: + asset = data["assets"][a]["asset"] + regions = data["assets"][a]["regions"] + img_name = asset["name"] + width = asset["size"]["width"] + height = asset["size"]["height"] + # print("\nimage name : ",img_name," width =",width," height =",height) + shutil.copyfile(jsonfolder+"/"+img_name,output+"/images/image_"+str(cc).zfill(12)+".jpg") + imglist.append(output+"/images/image_"+str(cc).zfill(12)+".jpg") + f = open(output+"/labels/image_"+str(cc).zfill(12)+".txt", "w") + # f = open(prefix+"/"+img_name.replace(".jpg",".txt"), "w") + for region in regions: + boundingBox = region["boundingBox"] + points = region["points"] + # print(" tags =",region["tags"]," boundingBox =",boundingBox," points =",points) + # ## Labels : + # D’après la doc on doit avoir : + # Label_ID_1 X_CENTER_NORM Y_CENTER_NORM WIDTH_NORM HEIGHT_NORM + # 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. + # X_CENTER_NORM = X_CENTER_ABS/IMAGE_WIDTH centre image + # Y_CENTER_NORM = Y_CENTER_ABS/IMAGE_HEIGHT centre image + # WIDTH_NORM = WIDTH_OF_LABEL_ABS/IMAGE_WIDTH largeur boite + # HEIGHT_NORM = HEIGHT_OF_LABEL_ABS/IMAGE_HEIGHT hauteur boite + # dans un repere dont l'origine est en haut a gauche et X de gauche à droite, Y du haut vers le bas + Label_ID = "0" + X_CENTER_NORM = (boundingBox["left"]+boundingBox["width"]/2)/width + Y_CENTER_NORM = (boundingBox["top"]+boundingBox["height"]/2)/height + WIDTH_NORM = boundingBox["width"]/width + HEIGHT_NORM = boundingBox["height"]/height + 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... + f.close() + cc += 1 + # print(" written file :",prefix+"/"+img_name.replace(".jpg",".txt")) +with open(output+"/liste_images.txt", "w") as file: + for item in imglist: + file.write(item + "\n") +with open(output+"/dataset.yaml", "w") as file: + file.write(f"""train: data/{outputname}/liste_images.txt +val: data/{outputname}/liste_images.txt +nc: 1 +names: ['{label}']""") \ No newline at end of file