exemple from sylvain

.gitignore

@@ -128,15 +128,20 @@ dmypy.json
 # node
 node_modules/
 *.tar
+*init.pt
+*best.pt
+*.pt
+
+/yolov7-tracker-example/data/dataset1_2024_06_19/vott-json-export.zip
 
 /test
 # /yolov7-setup
 
 # /yolov7-tracker-example
-/yolov7-tracker-example/cfg/training/yolov7x_dataset1_2024_06_19.yaml
-/yolov7-tracker-example/data/dataset1_2024_06_19
-/yolov7-tracker-example/runs
-/yolov7-tracker-example/tracker/config_files/dataset1_2024_06_19.yaml
-/yolov7-tracker-example/wandb
-/yolov7-tracker-example/info_SF.txt
-/yolov7-tracker-example/400m.mp4
+# /yolov7-tracker-example/cfg/training/yolov7x_dataset1_2024_06_19.yaml
+# /yolov7-tracker-example/data/dataset1_2024_06_19
+# /yolov7-tracker-example/runs
+# /yolov7-tracker-example/tracker/config_files/dataset1_2024_06_19.yaml
+# /yolov7-tracker-example/wandb
+# /yolov7-tracker-example/info_SF.txt
+# /yolov7-tracker-example/400m.mp4

yolov7-tracker-example/cfg/training/yolov7x_dataset1_2024_06_19.yaml

@@ -0,0 +1,156 @@
+# parameters
+nc: 1  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+
+# anchors
+anchors:
+  - [15,37, 18,54, 22,54]  # P3/8
+  - [28,70, 28,85, 31,117]  # P4/16
+  - [38,119, 41,152, 60,186]  # P5/32
+
+# yolov7 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Conv, [40, 3, 1]],  # 0
+  
+   [-1, 1, Conv, [80, 3, 2]],  # 1-P1/2      
+   [-1, 1, Conv, [80, 3, 1]],
+   
+   [-1, 1, Conv, [160, 3, 2]],  # 3-P2/4  
+   [-1, 1, Conv, [64, 1, 1]],
+   [-2, 1, Conv, [64, 1, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, Conv, [64, 3, 1]],
+   [[-1, -3, -5, -7, -8], 1, Concat, [1]],
+   [-1, 1, Conv, [320, 1, 1]],  # 13
+         
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [160, 1, 1]],
+   [-3, 1, Conv, [160, 1, 1]],
+   [-1, 1, Conv, [160, 3, 2]],
+   [[-1, -3], 1, Concat, [1]],  # 18-P3/8  
+   [-1, 1, Conv, [128, 1, 1]],
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [[-1, -3, -5, -7, -8], 1, Concat, [1]],
+   [-1, 1, Conv, [640, 1, 1]],  # 28
+         
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [320, 1, 1]],
+   [-3, 1, Conv, [320, 1, 1]],
+   [-1, 1, Conv, [320, 3, 2]],
+   [[-1, -3], 1, Concat, [1]],  # 33-P4/16  
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [[-1, -3, -5, -7, -8], 1, Concat, [1]],
+   [-1, 1, Conv, [1280, 1, 1]],  # 43
+         
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [640, 1, 1]],
+   [-3, 1, Conv, [640, 1, 1]],
+   [-1, 1, Conv, [640, 3, 2]],
+   [[-1, -3], 1, Concat, [1]],  # 48-P5/32  
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [[-1, -3, -5, -7, -8], 1, Concat, [1]],
+   [-1, 1, Conv, [1280, 1, 1]],  # 58
+  ]
+
+# yolov7 head
+head:
+  [[-1, 1, SPPCSPC, [640]], # 59
+  
+   [-1, 1, Conv, [320, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [43, 1, Conv, [320, 1, 1]], # route backbone P4
+   [[-1, -2], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [[-1, -3, -5, -7, -8], 1, Concat, [1]],
+   [-1, 1, Conv, [320, 1, 1]], # 73
+   
+   [-1, 1, Conv, [160, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [28, 1, Conv, [160, 1, 1]], # route backbone P3
+   [[-1, -2], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [128, 1, 1]],
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, Conv, [128, 3, 1]],
+   [[-1, -3, -5, -7, -8], 1, Concat, [1]],
+   [-1, 1, Conv, [160, 1, 1]], # 87
+      
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [160, 1, 1]],
+   [-3, 1, Conv, [160, 1, 1]],
+   [-1, 1, Conv, [160, 3, 2]],
+   [[-1, -3, 73], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [256, 1, 1]],
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, Conv, [256, 3, 1]],
+   [[-1, -3, -5, -7, -8], 1, Concat, [1]],
+   [-1, 1, Conv, [320, 1, 1]], # 102
+      
+   [-1, 1, MP, []],
+   [-1, 1, Conv, [320, 1, 1]],
+   [-3, 1, Conv, [320, 1, 1]],
+   [-1, 1, Conv, [320, 3, 2]],
+   [[-1, -3, 59], 1, Concat, [1]],
+   
+   [-1, 1, Conv, [512, 1, 1]],
+   [-2, 1, Conv, [512, 1, 1]],
+   [-1, 1, Conv, [512, 3, 1]],
+   [-1, 1, Conv, [512, 3, 1]],
+   [-1, 1, Conv, [512, 3, 1]],
+   [-1, 1, Conv, [512, 3, 1]],
+   [-1, 1, Conv, [512, 3, 1]],
+   [-1, 1, Conv, [512, 3, 1]],
+   [[-1, -3, -5, -7, -8], 1, Concat, [1]],
+   [-1, 1, Conv, [640, 1, 1]], # 117
+   
+   [87, 1, Conv, [320, 3, 1]],
+   [102, 1, Conv, [640, 3, 1]],
+   [117, 1, Conv, [1280, 3, 1]],
+
+   [[118,119,120], 1, IDetect, [nc, anchors]],   # Detect(P3, P4, P5)
+  ]

yolov7-tracker-example/data/dataset1_2024_06_19/YOLOV_BDD_Anchors_1280.txt

@@ -0,0 +1,9 @@
+[[ 15.  37.]
+ [ 18.  54.]
+ [ 22.  54.]
+ [ 28.  70.]
+ [ 28.  85.]
+ [ 31. 117.]
+ [ 38. 119.]
+ [ 41. 152.]
+ [ 60. 186.]]

yolov7-tracker-example/data/dataset1_2024_06_19/YOLOV_BDD_Anchors_640.txt

@@ -0,0 +1,9 @@
+[[ 8. 19.]
+ [ 9. 27.]
+ [11. 27.]
+ [14. 35.]
+ [14. 44.]
+ [16. 60.]
+ [19. 60.]
+ [21. 78.]
+ [30. 94.]]

yolov7-tracker-example/data/dataset1_2024_06_19/compute_yolov3_anchors.py

@@ -0,0 +1,126 @@
+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
+
+# os.chdir("/media/deniz/02B89600B895F301/BBD100K")
+# train_path = "data/labels/train/bdd100k_labels_images_train.json"
+#
+# with open(train_path,"r") as ftr:
+#     trlabel = json.load(ftr)
+#
+# BBDlabeldict = {"bike":0,
+#              "bus":1,
+#              "car":2,
+#              "motor":3,
+#              "person":4,
+#              "rider":5,
+#              "traffic light":6,
+#              "traffic sign":7,
+#              "train":8,
+#              "truck":9,
+#              "drivable area":[],
+#              "lane":[]}
+
+# BBDlabeldict = {"bacterie":0}
+# imgsize = 256
+#
+# images_path = "data/bacteries/images/train_and_test1"
+# # images_path = "data/poulets/images/train_and_test1"
+
+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))
+
+# sys.exit()
+# for ind1 in range(len(trlabel)):
+#     for ind2 in range(len(trlabel[ind1]["labels"])):
+#         try:
+#             a=trlabel[ind1]["labels"][ind2]["box2d"]   #traffic sign
+#             x1,y1,x2,y2 = list(a.values())
+#             width = abs(x1-x2)
+#             height = abs(y1-y2)
+#             w.append(width)
+#             h.append(height)
+#         except:
+#             pass
+# w=np.asarray(w)
+# h=np.asarray(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("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()

yolov7-tracker-example/data/dataset1_2024_06_19/dataset.yaml

@@ -0,0 +1,4 @@
+train: data/dataset1_2024_06_19/liste_images.txt
+val: data/dataset1_2024_06_19/liste_images.txt
+nc: 1
+names: ['coureur']