add in yolo training

rpiRobotics · Jul 24, 2024 · 07deb65 · 07deb65
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diff --git a/pyproject.toml b/pyproject.toml
@@ -8,14 +8,13 @@ version = "0.1.0"
 description = "FLIR WAAM Helper Functions."
 
 packages = [
-    { include = "flir_toolbox", from = "src" },
-    { include = "torch_tracking", from = "src" }
+    { include = "flir_toolbox", from = "src" }
 ]
 
 authors = ["Honglu He <[email protected]>",]
 maintainers = ["Honglu He <[email protected]>",]
 
-include = ["CHANGELOG.md", "LICENSE", "py.typed","torch_tracking/torch.pt"]
+include = ["CHANGELOG.md", "LICENSE", "py.typed"]
 license = "BSD-3-Clause"
 readme = "README.md"
 

diff --git a/src/flir_toolbox/flir_toolbox.py b/src/flir_toolbox/flir_toolbox.py
@@ -179,6 +179,15 @@ def weld_detection_steel(raw_img,yolo_model,threshold=1.5e4,area_threshold=50,pe
 
     return centroid, bbox, torch_centroid, torch_bbox
 
+def weld_detection_steel(raw_img,torch_model,tip_model):
+
+    ## Torch detection
+    torch_centroid, torch_bbox=torch_detect_yolo(raw_img,torch_model)
+    ## Tip detection
+    tip_centroid, tip_bbox=tip_wire_detect_yolo(raw_img,tip_model)
+
+    return tip_centroid, tip_bbox, torch_centroid, torch_bbox
+
 
 def torch_detect(ir_image,template,template_threshold=0.3,pixel_threshold=1e4):
     ###template matching for torch, return the upper left corner of the matched region
@@ -217,6 +226,25 @@ def torch_detect_yolo(ir_image,yolo_model,pixel_threshold=1e4,percentage_thresho
 
     #run yolo
     result= yolo_model.predict(ir_torch_tracking,verbose=False, conf=0.5)[0]
+    conf_all = result.boxes.conf.cpu().numpy()  #find the most confident torch prediction
+    if len(conf_all)>0:
+        max_conf_idx=np.argmax(conf_all)
+        bbox = result.boxes.cpu().xyxy[max_conf_idx].numpy()\
+        #change bbox to opencv format
+        bbox[2]=bbox[2]-bbox[0]
+        bbox[3]=bbox[3]-bbox[1]
+        centroid = np.array([(bbox[0]+bbox[2])/2,(bbox[1]+bbox[3])/2])
+        return centroid, bbox.astype(int)
+    else:
+        return None, None
+
+def tip_detect_yolo(ir_image,tip_model):
+    ir_tip_tracking=copy.deepcopy(ir_image)
+    ir_tip_tracking = ((ir_tip_tracking - np.min(ir_tip_tracking)) / (np.max(ir_tip_tracking) - np.min(ir_tip_tracking))) * 255
+    ir_tip_tracking = ir_tip_tracking.astype(np.uint8)
+    ir_tip_tracking = cv2.cvtColor(ir_tip_tracking, cv2.COLOR_GRAY2BGR)
+    #run yolo
+    result= tip_model.predict(ir_tip_tracking,verbose=False)[0]
     if result.boxes.cls.cpu().numpy()==0:
         bbox = result.boxes.cpu().xyxy[0].numpy()\
         #change bbox to opencv format
@@ -227,6 +255,33 @@ def torch_detect_yolo(ir_image,yolo_model,pixel_threshold=1e4,percentage_thresho
     else:
         return None, None
 
+def tip_wire_detect_yolo(ir_image,tip_wire_model):
+    ir_tip_wire_tracking=copy.deepcopy(ir_image)
+    pixel_threshold=0.77*np.max(ir_tip_wire_tracking)
+    ir_tip_wire_tracking[ir_tip_wire_tracking>pixel_threshold]=pixel_threshold
+    ir_tip_wire_tracking = ((ir_tip_wire_tracking - np.min(ir_tip_wire_tracking)) / (np.max(ir_tip_wire_tracking) - np.min(ir_tip_wire_tracking))) * 255
+    ir_tip_wire_tracking = ir_tip_wire_tracking.astype(np.uint8)
+    ir_tip_wire_tracking = cv2.cvtColor(ir_tip_wire_tracking, cv2.COLOR_GRAY2BGR)
+    #run yolo
+    result= tip_wire_model.predict(ir_tip_wire_tracking,verbose=False,conf=0.5)[0]
+    cls_list = result.boxes.cls.cpu().numpy().astype(int)
+    counts = np.bincount(cls_list)
+    # Check if there is exactly one 0 and one 1
+    if len(counts) >= 2 and counts[0] == 1 and counts[1] == 1:
+        idx0=np.where(cls_list==0)[0][0]
+        idx1=np.where(cls_list==1)[0][0]
+        #get the lower right corner of the bounding box of cls 0
+        x0_left,y0_left,x1_left,y1_left = result.boxes.cpu().xyxy[idx0].numpy()
+        #get the lower left corner of the bounding box of cls 1
+        x0_right,y0_right,x1_right,y1_right = result.boxes.cpu().xyxy[idx1].numpy()
+        #get the average of the two points
+        centroid_x = (x1_left+x0_right)/2
+        centroid_y = (y1_left+y1_right)/2
+
+        return np.array([centroid_x,centroid_y]), (int(centroid_x - 1.5), int(centroid_y - 1.5), 3, 3)
+
+    return None, None
+
 def get_pixel_value(ir_image,coord,window_size):
     ###get pixel value larger than avg within the window
     window = ir_image[coord[1]-window_size//2:coord[1]+window_size//2+1,coord[0]-window_size//2:coord[0]+window_size//2+1]

diff --git a/src/flir_toolbox/tip_wire.pt b/src/flir_toolbox/tip_wire.pt
diff --git a/src/torch_tracking/torch.pt → src/flir_toolbox/torch.pt b/src/torch_tracking/torch.pt → src/flir_toolbox/torch.pt
diff --git a/yolov8/data_gen_tip.py b/yolov8/data_gen_tip.py
@@ -0,0 +1,32 @@
+import cv2
+import pickle, sys
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+
+data_dir='../../../recorded_data/'
+data_sets=['ER316L/wallbf_70ipm_v7_70ipm_v7/','ER316L/wallbf_150ipm_v15_150ipm_v15/',
+           'ER316L/trianglebf_100ipm_v10_100ipm_v10','ER316L/cylinderspiral_100ipm_v10/',
+           'ER316L/streaming/cylinderspiral_T19000/','ER316L/streaming/cylinderspiral_T25000/',
+           'ER316L/VPD10/cylinderspiral_50ipm_v5','ER316L/VPD10/cylinderspiral_70ipm_v7',
+           'ER316L/VPD10/cylinderspiral_110ipm_v11','ER316L/VPD10/cylinderspiral_130ipm_v13']
+
+output_dir='../../../recorded_data/yolov8/tip/'
+
+num_images_per_set=10
+img_counts=0
+#radmonly select 10 images from each set
+for data_set in data_sets:
+    with open(data_dir+data_set+'/ir_recording.pickle', 'rb') as file:
+        ir_recording = pickle.load(file)
+    #select random frames
+    idxs=np.random.choice(range(3000,len(ir_recording)), num_images_per_set, replace=False)
+    for idx in idxs:
+        ir_torch_tracking=np.rot90(ir_recording[idx], k=-1)
+        ir_torch_tracking_normalized = ((ir_torch_tracking - np.min(ir_torch_tracking)) / (np.max(ir_torch_tracking) - np.min(ir_torch_tracking))) * 255
+        #save image
+        cv2.imwrite(output_dir+str(img_counts)+'.png', ir_torch_tracking_normalized)
+        img_counts+=1
+
+
diff --git a/yolov8/data_gen_torch.py b/yolov8/data_gen_torch.py
@@ -0,0 +1,37 @@
+import cv2
+import pickle, sys
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+
+data_dir='../../../../recorded_data/'
+# data_sets=['ER316L/wallbf_70ipm_v7_70ipm_v7/','ER316L/wallbf_150ipm_v15_150ipm_v15/',
+#            'ER316L/trianglebf_100ipm_v10_100ipm_v10','ER316L/cylinderspiral_100ipm_v10/']
+# data_sets=['ER316L/streaming/cylinderspiral_T19000/','ER4043/wallbf_100ipm_v10_120ipm_v12/',
+#            'ER4043/wallbf_100ipm_v10_70ipm_v7/','ER4043/wall2bead_w3.0_150ipm_v10_150ipm_v10/']
+# data_sets=['ER4043/wallbf_100ipm_v10_100ipm_v10/', 'ER4043/wallbf_100ipm_v10/',]
+data_sets=['ER316L/streaming/cylinderspiral_T25000/']
+output_dir='../../../../recorded_data/yolov8/torch/'
+
+num_images_per_set=10
+pixel_threshold=1e4
+percentage_threshold=0.5
+img_counts=100
+#radmonly select 10 images from each set
+for data_set in data_sets:
+    with open(data_dir+data_set+'/ir_recording.pickle', 'rb') as file:
+        ir_recording = pickle.load(file)
+    #select random frames
+    idxs=np.random.choice(range(3000,len(ir_recording)), num_images_per_set, replace=False)
+    for idx in idxs:
+        ir_torch_tracking=np.rot90(ir_recording[idx], k=-1)
+        pixel_threshold = max(pixel_threshold, percentage_threshold*np.max(ir_torch_tracking))
+        print(pixel_threshold)
+        ir_torch_tracking[ir_torch_tracking>pixel_threshold]=pixel_threshold
+        ir_torch_tracking_normalized = ((ir_torch_tracking - np.min(ir_torch_tracking)) / (np.max(ir_torch_tracking) - np.min(ir_torch_tracking))) * 255
+        #save image
+        cv2.imwrite(output_dir+str(img_counts)+'.png', ir_torch_tracking_normalized)
+        img_counts+=1
+
+
diff --git a/yolov8/data_gen_wire.py b/yolov8/data_gen_wire.py
@@ -0,0 +1,34 @@
+import cv2
+import pickle, sys
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+
+data_dir='../../../recorded_data/'
+data_sets=['ER316L/wallbf_70ipm_v7_70ipm_v7/','ER316L/wallbf_150ipm_v15_150ipm_v15/',
+           'ER316L/trianglebf_100ipm_v10_100ipm_v10','ER316L/cylinderspiral_100ipm_v10/',
+           'ER316L/streaming/cylinderspiral_T19000/','ER316L/streaming/cylinderspiral_T25000/',
+           'ER316L/VPD10/cylinderspiral_50ipm_v5','ER316L/VPD10/cylinderspiral_70ipm_v7',
+           'ER316L/VPD10/cylinderspiral_110ipm_v11','ER316L/VPD10/cylinderspiral_130ipm_v13']
+
+output_dir='../../../recorded_data/yolov8/tip-wire/'
+
+num_images_per_set=10
+img_counts=0
+#radmonly select 10 images from each set
+for data_set in data_sets:
+    with open(data_dir+data_set+'/ir_recording.pickle', 'rb') as file:
+        ir_recording = pickle.load(file)
+    #select random frames
+    idxs=np.random.choice(range(3000,len(ir_recording)), num_images_per_set, replace=False)
+    for idx in idxs:
+        ir_torch_tracking=np.rot90(ir_recording[idx], k=-1)
+        pixel_threshold=0.77*np.max(ir_torch_tracking)
+        ir_torch_tracking[ir_torch_tracking>pixel_threshold]=pixel_threshold
+        ir_torch_tracking_normalized = ((ir_torch_tracking - np.min(ir_torch_tracking)) / (np.max(ir_torch_tracking) - np.min(ir_torch_tracking))) * 255
+        #save image
+        cv2.imwrite(output_dir+str(img_counts)+'.png', ir_torch_tracking_normalized)
+        img_counts+=1
+
+
diff --git a/yolov8/datasets/tip_wire_yolov8_data/data.yaml b/yolov8/datasets/tip_wire_yolov8_data/data.yaml
@@ -0,0 +1,13 @@
+train: ../train/images
+val: ../valid/images
+test: ../test/images
+
+nc: 2
+names: ['tip_wire_left', 'tip_wire_right']
+
+roboflow:
+  workspace: torchyolov8
+  project: tip_wire_yolov8
+  version: 1
+  license: CC BY 4.0
+  url: https://universe.roboflow.com/torchyolov8/tip_wire_yolov8/dataset/1
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