diff --git a/my_models/yolo/utilz.py b/my_models/yolo/utilz.py
index 1e19fedfc564e4721545752d285efbf8a0c21f5e..5870e213269be4ca9fa33ffa03f8fd34cb81d6d0 100644
--- a/my_models/yolo/utilz.py
+++ b/my_models/yolo/utilz.py
@@ -1,161 +1,161 @@
-# from typing import Tuple
-from ultralytics.utils import ops
-import torch
-import numpy as np
-import cv2
-
-try:
- scale_segments = ops.scale_segments
-except AttributeError:
- scale_segments = ops.scale_coords
-
-
-def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=False, scale_fill=False, scaleup=False, stride=32):
- """
- Resize image and padding for detection. Takes image as input,
- resizes image to fit into new shape with saving original aspect ratio and pads it to meet stride-multiple constraints
-
- Parameters:
- img (np.ndarray): image for preprocessing
- new_shape (Tuple(int, int)): image size after preprocessing in format [height, width]
- color (Tuple(int, int, int)): color for filling padded area
- auto (bool): use dynamic input size, only padding for stride constrins applied
- scale_fill (bool): scale image to fill new_shape
- scaleup (bool): allow scale image if it is lower then desired input size, can affect model accuracy
- stride (int): input padding stride
- Returns:
- img (np.ndarray): image after preprocessing
- ratio (Tuple(float, float)): hight and width scaling ratio
- padding_size (Tuple(int, int)): height and width padding size
-
-
- """
- # Resize and pad image while meeting stride-multiple constraints
- shape = img.shape[:2] # current shape [height, width]
- if isinstance(new_shape, int):
- new_shape = (new_shape, new_shape)
-
- # Scale ratio (new / old)
- r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
- if not scaleup: # only scale down, do not scale up (for better test mAP)
- r = min(r, 1.0)
-
- # Compute padding
- ratio = r, r # width, height ratios
- new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
- dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
- if auto: # minimum rectangle
- dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
- elif scale_fill: # stretch
- dw, dh = 0.0, 0.0
- new_unpad = (new_shape[1], new_shape[0])
- ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
-
- dw /= 2 # divide padding into 2 sides
- dh /= 2
-
- if shape[::-1] != new_unpad: # resize
- img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
- top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
- left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
- img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
- return img, ratio, (dw, dh)
-
-
-def preprocess_image_(img0, imgsz):
- """
- Preprocess image according to YOLOv8 input requirements.
- Takes image in np.array format, resizes it to specific size using letterbox resize and changes data layout from HWC to CHW.
-
- Parameters:
- img0 (np.ndarray): image for preprocessing
- Returns:
- img (np.ndarray): image after preprocessing
- """
- # resize
- img = letterbox(img0, new_shape=(imgsz, imgsz))[0]
-
- # Convert HWC to CHW
- img = img.transpose(2, 0, 1)
- img = np.ascontiguousarray(img)
- return img
-
-
-def image_to_tensor_(image):
- """
- Preprocess image according to YOLOv8 input requirements.
- Takes image in np.array format, resizes it to specific size using letterbox resize and changes data layout from HWC to CHW.
-
- Parameters:
- image (np.ndarray): image for preprocessing
- Returns:
- input_tensor (np.ndarray): input tensor in NCHW format with float32 values in [0, 1] range
- """
- input_tensor = image.astype(np.float32) # uint8 to fp32
- input_tensor /= 255.0 # 0 - 255 to 0.0 - 1.0
-
- # add batch dimension
- if input_tensor.ndim == 3:
- input_tensor = np.expand_dims(input_tensor, 0)
- return input_tensor
-
-
-def postprocess_(
- pred_boxes,
- input_hw,
- orig_img,
- min_conf_threshold=0.25,
- nms_iou_threshold=0.7,
- agnosting_nms=False,
- max_detections=300,
- pred_masks=None,
- retina_mask=False,
- nc=80,
-):
- """
- YOLOv8 model postprocessing function. Applied non maximum suppression algorithm to detections and rescale boxes to original image size
- Parameters:
- pred_boxes (np.ndarray): model output prediction boxes
- input_hw (np.ndarray): preprocessed image
- orig_image (np.ndarray): image before preprocessing
- min_conf_threshold (float, *optional*, 0.25): minimal accepted confidence for object filtering
- nms_iou_threshold (float, *optional*, 0.45): minimal overlap score for removing objects duplicates in NMS
- agnostic_nms (bool, *optiona*, False): apply class agnostinc NMS approach or not
- max_detections (int, *optional*, 300): maximum detections after NMS
- pred_masks (np.ndarray, *optional*, None): model ooutput prediction masks, if not provided only boxes will be postprocessed
- retina_mask (bool, *optional*, False): retina mask postprocessing instead of native decoding
- Returns:
- pred (List[Dict[str, np.ndarray]]): list of dictionary with det - detected boxes in format [x1, y1, x2, y2, score, label] and segment - segmentation polygons for each element in batch
- """
- nms_kwargs = {"agnostic": agnosting_nms, "max_det": max_detections}
- # if pred_masks is not None:
- # nms_kwargs["nm"] = 32
- preds = ops.non_max_suppression(
- torch.from_numpy(pred_boxes),
- min_conf_threshold,
- nms_iou_threshold,
- nc=nc,
- **nms_kwargs
- )
- results = []
- proto = torch.from_numpy(pred_masks) if pred_masks is not None else None
-
- for i, pred in enumerate(preds):
- shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape
- if not len(pred):
- results.append({"det": [], "segment": []})
- continue
- if proto is None:
- pred[:, :4] = ops.scale_boxes(input_hw, pred[:, :4], shape).round()
- results.append({"det": pred})
- continue
- if retina_mask:
- pred[:, :4] = ops.scale_boxes(input_hw, pred[:, :4], shape).round()
- masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], shape[:2]) # HWC
- segments = [scale_segments(input_hw, x, shape, normalize=False) for x in ops.masks2segments(masks)]
- else:
- masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], input_hw, upsample=True)
- pred[:, :4] = ops.scale_boxes(input_hw, pred[:, :4], shape).round()
- segments = [scale_segments(input_hw, x, shape, normalize=False) for x in ops.masks2segments(masks)]
- results.append({"det": pred[:, :6].numpy(), "segment": segments})
- return results
+# # from typing import Tuple
+# from ultralytics.utils import ops
+# import torch
+# import numpy as np
+# import cv2
+#
+# try:
+# scale_segments = ops.scale_segments
+# except AttributeError:
+# scale_segments = ops.scale_coords
+#
+#
+# def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=False, scale_fill=False, scaleup=False, stride=32):
+# """
+# Resize image and padding for detection. Takes image as input,
+# resizes image to fit into new shape with saving original aspect ratio and pads it to meet stride-multiple constraints
+#
+# Parameters:
+# img (np.ndarray): image for preprocessing
+# new_shape (Tuple(int, int)): image size after preprocessing in format [height, width]
+# color (Tuple(int, int, int)): color for filling padded area
+# auto (bool): use dynamic input size, only padding for stride constrins applied
+# scale_fill (bool): scale image to fill new_shape
+# scaleup (bool): allow scale image if it is lower then desired input size, can affect model accuracy
+# stride (int): input padding stride
+# Returns:
+# img (np.ndarray): image after preprocessing
+# ratio (Tuple(float, float)): hight and width scaling ratio
+# padding_size (Tuple(int, int)): height and width padding size
+#
+#
+# """
+# # Resize and pad image while meeting stride-multiple constraints
+# shape = img.shape[:2] # current shape [height, width]
+# if isinstance(new_shape, int):
+# new_shape = (new_shape, new_shape)
+#
+# # Scale ratio (new / old)
+# r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+# if not scaleup: # only scale down, do not scale up (for better test mAP)
+# r = min(r, 1.0)
+#
+# # Compute padding
+# ratio = r, r # width, height ratios
+# new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+# dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
+# if auto: # minimum rectangle
+# dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
+# elif scale_fill: # stretch
+# dw, dh = 0.0, 0.0
+# new_unpad = (new_shape[1], new_shape[0])
+# ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
+#
+# dw /= 2 # divide padding into 2 sides
+# dh /= 2
+#
+# if shape[::-1] != new_unpad: # resize
+# img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
+# top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+# left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+# img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
+# return img, ratio, (dw, dh)
+#
+#
+# def preprocess_image_(img0, imgsz):
+# """
+# Preprocess image according to YOLOv8 input requirements.
+# Takes image in np.array format, resizes it to specific size using letterbox resize and changes data layout from HWC to CHW.
+#
+# Parameters:
+# img0 (np.ndarray): image for preprocessing
+# Returns:
+# img (np.ndarray): image after preprocessing
+# """
+# # resize
+# img = letterbox(img0, new_shape=(imgsz, imgsz))[0]
+#
+# # Convert HWC to CHW
+# img = img.transpose(2, 0, 1)
+# img = np.ascontiguousarray(img)
+# return img
+#
+#
+# def image_to_tensor_(image):
+# """
+# Preprocess image according to YOLOv8 input requirements.
+# Takes image in np.array format, resizes it to specific size using letterbox resize and changes data layout from HWC to CHW.
+#
+# Parameters:
+# image (np.ndarray): image for preprocessing
+# Returns:
+# input_tensor (np.ndarray): input tensor in NCHW format with float32 values in [0, 1] range
+# """
+# input_tensor = image.astype(np.float32) # uint8 to fp32
+# input_tensor /= 255.0 # 0 - 255 to 0.0 - 1.0
+#
+# # add batch dimension
+# if input_tensor.ndim == 3:
+# input_tensor = np.expand_dims(input_tensor, 0)
+# return input_tensor
+#
+#
+# def postprocess_(
+# pred_boxes,
+# input_hw,
+# orig_img,
+# min_conf_threshold=0.25,
+# nms_iou_threshold=0.7,
+# agnosting_nms=False,
+# max_detections=300,
+# pred_masks=None,
+# retina_mask=False,
+# nc=80,
+# ):
+# """
+# YOLOv8 model postprocessing function. Applied non maximum suppression algorithm to detections and rescale boxes to original image size
+# Parameters:
+# pred_boxes (np.ndarray): model output prediction boxes
+# input_hw (np.ndarray): preprocessed image
+# orig_image (np.ndarray): image before preprocessing
+# min_conf_threshold (float, *optional*, 0.25): minimal accepted confidence for object filtering
+# nms_iou_threshold (float, *optional*, 0.45): minimal overlap score for removing objects duplicates in NMS
+# agnostic_nms (bool, *optiona*, False): apply class agnostinc NMS approach or not
+# max_detections (int, *optional*, 300): maximum detections after NMS
+# pred_masks (np.ndarray, *optional*, None): model ooutput prediction masks, if not provided only boxes will be postprocessed
+# retina_mask (bool, *optional*, False): retina mask postprocessing instead of native decoding
+# Returns:
+# pred (List[Dict[str, np.ndarray]]): list of dictionary with det - detected boxes in format [x1, y1, x2, y2, score, label] and segment - segmentation polygons for each element in batch
+# """
+# nms_kwargs = {"agnostic": agnosting_nms, "max_det": max_detections}
+# # if pred_masks is not None:
+# # nms_kwargs["nm"] = 32
+# preds = ops.non_max_suppression(
+# torch.from_numpy(pred_boxes),
+# min_conf_threshold,
+# nms_iou_threshold,
+# nc=nc,
+# **nms_kwargs
+# )
+# results = []
+# proto = torch.from_numpy(pred_masks) if pred_masks is not None else None
+#
+# for i, pred in enumerate(preds):
+# shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape
+# if not len(pred):
+# results.append({"det": [], "segment": []})
+# continue
+# if proto is None:
+# pred[:, :4] = ops.scale_boxes(input_hw, pred[:, :4], shape).round()
+# results.append({"det": pred})
+# continue
+# if retina_mask:
+# pred[:, :4] = ops.scale_boxes(input_hw, pred[:, :4], shape).round()
+# masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], shape[:2]) # HWC
+# segments = [scale_segments(input_hw, x, shape, normalize=False) for x in ops.masks2segments(masks)]
+# else:
+# masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], input_hw, upsample=True)
+# pred[:, :4] = ops.scale_boxes(input_hw, pred[:, :4], shape).round()
+# segments = [scale_segments(input_hw, x, shape, normalize=False) for x in ops.masks2segments(masks)]
+# results.append({"det": pred[:, :6].numpy(), "segment": segments})
+# return results
diff --git a/train_bb_detector.py b/train_bb_detector.py
index bad733c4cc2f4c010491ae8a1e62009f73300804..638f306bba8e5c43cc11135c91f3a9836f2bab31 100644
--- a/train_bb_detector.py
+++ b/train_bb_detector.py
@@ -28,3 +28,12 @@ if __name__ == '__main__':
args = parser.parse_args()
+ train_file = args.initial_cv
+ train_home = args.images_path
+ check_path(train_home)
+
+ check_path(train_file)
+ train_pd = pd.read_csv(train_file)
+ print("Initial available:", train_pd.shape)
+
+
\ No newline at end of file