charlie880624
7716a0060f
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- Add golf1/2/4/7/8 dataset classes for semantic segmentation - Add kneron-specific configs (meconfig series, kn_stdc1_golf4class) - Organize scripts into tools/check/ and tools/kneron/ - Add kneron_preprocessing module - Update README with quick-start guide - Update .gitignore to exclude data dirs, onnx, nef outputs Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
188 lines
7.9 KiB
Python
188 lines
7.9 KiB
Python
import numpy as np
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from PIL import Image
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from .utils import str2bool, str2int, str2float
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from .Runner_base import Runner_base, Param_base
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class General(Param_base):
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type = ''
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pad_val = ''
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padded_w = ''
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padded_h = ''
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pad_l = ''
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pad_r = ''
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pad_t = ''
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pad_b = ''
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padding_ch = 3
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padding_ch_type = 'RGB'
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def update(self, **dic):
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self.type = dic['type']
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self.pad_val = dic['pad_val']
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self.padded_w = str2int(dic['padded_w'])
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self.padded_h = str2int(dic['padded_h'])
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self.pad_l = str2int(dic['pad_l'])
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self.pad_r = str2int(dic['pad_r'])
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self.pad_t = str2int(dic['pad_t'])
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self.pad_b = str2int(dic['pad_b'])
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def __str__(self):
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str_out = [
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', type:',str(self.type),
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', pad_val:',str(self.pad_val),
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', pad_l:',str(self.pad_l),
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', pad_r:',str(self.pad_r),
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', pad_r:',str(self.pad_t),
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', pad_b:',str(self.pad_b),
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', padding_ch:',str(self.padding_ch)]
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return(' '.join(str_out))
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class Hw(Param_base):
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radix = 8
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normalize_type = 'floating'
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def update(self, **dic):
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self.radix = dic['radix']
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self.normalize_type = dic['normalize_type']
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def __str__(self):
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str_out = [
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', radix:', str(self.radix),
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', normalize_type:',str(self.normalize_type)]
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return(' '.join(str_out))
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class runner(Runner_base):
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## overwrite the class in Runner_base
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general = General()
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hw = Hw()
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def __str__(self):
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return('<Padding>')
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def update(self, **kwargs):
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super().update(**kwargs)
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## update pad type & pad length
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if (self.general.pad_l != 0) or (self.general.pad_r != 0) or (self.general.pad_t != 0) or (self.general.pad_b != 0):
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self.general.type = 'specific'
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assert(self.general.pad_l >= 0)
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assert(self.general.pad_r >= 0)
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assert(self.general.pad_t >= 0)
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assert(self.general.pad_b >= 0)
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elif(self.general.type != 'specific'):
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if self.general.padded_w == 0 or self.general.padded_h == 0:
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self.general.padded_w = self.common.model_size[0]
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self.general.padded_h = self.common.model_size[1]
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assert(self.general.padded_w > 0)
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assert(self.general.padded_h > 0)
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assert(self.general.type.lower() in ['CENTER', 'Center', 'center', 'CORNER', 'Corner', 'corner'])
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else:
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assert(self.general.type == 'specific')
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## decide pad_val & padding ch
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# if numerical_type is floating
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if (self.common.numerical_type == 'floating'):
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if self.general.pad_val != 'edge':
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self.general.pad_val = str2float(self.general.pad_val)
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self.general.padding_ch = 3
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self.general.padding_ch_type = 'RGB'
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# if numerical_type is 520 or 720
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else:
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if self.general.pad_val == '':
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if self.hw.normalize_type.lower() in ['TF', 'Tf', 'tf']:
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self.general.pad_val = np.uint8(-128 >> (7 - self.hw.radix))
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elif self.hw.normalize_type.lower() in ['YOLO', 'Yolo', 'yolo']:
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self.general.pad_val = np.uint8(0 >> (8 - self.hw.radix))
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elif self.hw.normalize_type.lower() in ['KNERON', 'Kneron', 'kneron']:
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self.general.pad_val = np.uint8(-128 >> (8 - self.hw.radix))
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else:
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self.general.pad_val = np.uint8(0 >> (8 - self.hw.radix))
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else:
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self.general.pad_val = str2int(self.general.pad_val)
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self.general.padding_ch = 4
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self.general.padding_ch_type = 'RGBA'
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def run(self, image_data):
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# init
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shape = image_data.shape
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w = shape[1]
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h = shape[0]
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if len(shape) < 3:
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self.general.padding_ch = 1
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self.general.padding_ch_type = 'L'
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else:
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if shape[2] == 3 and self.general.padding_ch == 4:
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image_data = np.concatenate((image_data, np.zeros((h, w, 1), dtype=np.uint8) ), axis=2)
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## padding
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if self.general.type.lower() in ['CENTER', 'Center', 'center']:
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img_pad = self._padding_center(image_data, w, h)
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elif self.general.type.lower() in ['CORNER', 'Corner', 'corner']:
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img_pad = self._padding_corner(image_data, w, h)
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else:
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img_pad = self._padding_sp(image_data, w, h)
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# print info
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if str2bool(self.common.print_info):
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self.print_info()
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# output
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info = {}
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return img_pad, info
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## protect fun
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def _padding_center(self, img, ori_w, ori_h):
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# img_pad = Image.new(self.general.padding_ch_type, (self.general.padded_w, self.general.padded_h), int(self.general.pad_val[0]))
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# img = Image.fromarray(img)
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# img_pad.paste(img, ((self.general.padded_w-ori_w)//2, (self.general.padded_h-ori_h)//2))
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# return img_pad
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padH = self.general.padded_h - ori_h
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padW = self.general.padded_w - ori_w
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self.general.pad_t = padH // 2
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self.general.pad_b = (padH // 2) + (padH % 2)
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self.general.pad_l = padW // 2
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self.general.pad_r = (padW // 2) + (padW % 2)
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if self.general.pad_l < 0 or self.general.pad_r <0 or self.general.pad_t <0 or self.general.pad_b<0:
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return img
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img_pad = self._padding_sp(img,ori_w,ori_h)
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return img_pad
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def _padding_corner(self, img, ori_w, ori_h):
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# img_pad = Image.new(self.general.padding_ch_type, (self.general.padded_w, self.general.padded_h), self.general.pad_val)
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# img_pad.paste(img, (0, 0))
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self.general.pad_l = 0
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self.general.pad_r = self.general.padded_w - ori_w
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self.general.pad_t = 0
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self.general.pad_b = self.general.padded_h - ori_h
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if self.general.pad_l < 0 or self.general.pad_r <0 or self.general.pad_t <0 or self.general.pad_b<0:
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return img
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img_pad = self._padding_sp(img,ori_w,ori_h)
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return img_pad
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def _padding_sp(self, img, ori_w, ori_h):
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# block_t = np.zeros((self.general.pad_t, self.general.pad_l + self.general.pad_r + ori_w, self.general.padding_ch), dtype=np.float)
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# block_l = np.zeros((ori_h, self.general.pad_l, self.general.padding_ch), dtype=np.float)
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# block_r = np.zeros((ori_h, self.general.pad_r, self.general.padding_ch), dtype=np.float)
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# block_b = np.zeros((self.general.pad_b, self.general.pad_l + self.general.pad_r + ori_w, self.general.padding_ch), dtype=np.float)
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# for i in range(self.general.padding_ch):
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# block_t[:, :, i] = np.ones(block_t[:, :, i].shape, dtype=np.float) * self.general.pad_val
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# block_l[:, :, i] = np.ones(block_l[:, :, i].shape, dtype=np.float) * self.general.pad_val
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# block_r[:, :, i] = np.ones(block_r[:, :, i].shape, dtype=np.float) * self.general.pad_val
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# block_b[:, :, i] = np.ones(block_b[:, :, i].shape, dtype=np.float) * self.general.pad_val
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# padded_image_hor = np.concatenate((block_l, img, block_r), axis=1)
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# padded_image = np.concatenate((block_t, padded_image_hor, block_b), axis=0)
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# return padded_image
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if self.general.padding_ch == 1:
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pad_range = ( (self.general.pad_t, self.general.pad_b),(self.general.pad_l, self.general.pad_r) )
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else:
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pad_range = ((self.general.pad_t, self.general.pad_b),(self.general.pad_l, self.general.pad_r),(0,0))
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if isinstance(self.general.pad_val, str):
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if self.general.pad_val == 'edge':
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padded_image = np.pad(img, pad_range, mode="edge")
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else:
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padded_image = np.pad(img, pad_range, mode="constant",constant_values=0)
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else:
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padded_image = np.pad(img, pad_range, mode="constant",constant_values=self.general.pad_val)
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return padded_image
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