yolov5 face onnx推理代码

from enum import IntEnum from typing import Tuple, Union import cv2 import numexpr as ne import numpy as np class ImageProcessor: """ Generic image processor for numpy images arguments img np.ndarray HW (2 ndim) HWC (3 ndim) NHWC (4 ndim) """ def __init__(self, img : np.ndarray, copy=False): if copy: img = img.copy() ndim = img.ndim if ndim not in [2,3,4]: raise ValueError(f'img.ndim must be 2,3,4, not {ndim}.') # Make internal image as NHWC if ndim == 2: N, (H,W), C = 0, img.shape, 0 img = img[None,:,:,None] elif ndim == 3: N, (H,W,C) = 0, img.shape img = img[None,...] else: N,H,W,C = img.shape self._img : np.ndarray = img def copy(self) -> 'ImageProcessor': """ """ ip = ImageProcessor.__new__(ImageProcessor) ip._img = self._img.copy() return ip def get_dims(self) -> Tuple[int,int,int,int]: """ returns dimensions of current working image returns N,H,W,C (ints) , each >= 1 """ return self._img.shape def get_dtype(self): return self._img.dtype def gamma(self, red : float, green : float, blue : float, mask=None) -> 'ImageProcessor': dtype = self.get_dtype() self.to_ufloat32() img = orig_img = self._img img = np.power(img, np.array([1.0 / blue, 1.0 / green, 1.0 / red], np.float32) ) np.clip(img, 0, 1.0, out=img) if mask is not None: mask = self._check_normalize_mask(mask) img = ne.evaluate('orig_img*(1-mask) + img*mask') self._img = img self.to_dtype(dtype) return self def apply(self, func, mask=None) -> 'ImageProcessor': """ apply your own function on internal image image has NHWC format. Do not change format, but dims can be changed. func callable (img) -> img example: .apply( lambda img: img-[102,127,63] ) """ img = orig_img = self._img img = func(img).astype(orig_img.dtype) if img.ndim != 4: raise Exception('func used in ImageProcessor.apply changed format of image') if mask is not None: mask = self._check_normalize_mask(mask) img = ne.evaluate('orig_img*(1-mask) + img*mask').astype(orig_img.dtype) self._img = img return self def fit_in (self, TW = None, TH = None, pad_to_target : bool = False, allow_upscale : bool = False, interpolation : 'ImageProcessor.Interpolation' = None) -> float: """ fit image in w,h keeping aspect ratio TW,TH int/None target width,height pad_to_target bool pad remain area with zeros allow_upscale bool if image smaller than TW,TH it will be upscaled interpolation ImageProcessor.Interpolation. value returns scale float value """ #if interpolation is None: # interpolation = ImageProcessor.Interpolation.LINEAR img = self._img N,H,W,C = img.shape if TW is not None and TH is None: scale = TW / W elif TW is None and TH is not None: scale = TH / H elif TW is not None and TH is not None: SW = W / TW SH = H / TH scale = 1.0 if SW > 1.0 or SH > 1.0 or (SW < 1.0 and SH < 1.0): scale /= max(SW, SH) else: raise ValueError('TW or TH should be specified') if not allow_upscale and scale > 1.0: scale = 1.0 if scale != 1.0: img = img.transpose( (1,2,0,3) ).reshape( (H,W,N*C) ) img = cv2.resize (img, ( int(W*scale), int(H*scale) ), interpolation=ImageProcessor.Interpolation.LINEAR) H,W = img.shape[0:2] img = img.reshape( (H,W,N,C) ).transpose( (2,0,1,3) ) if pad_to_target: w_pad = (TW-W) if TW is not None else 0 h_pad = (TH-H) if TH is not None else 0 if w_pad != 0 or h_pad != 0: img = np.pad(img, ( (0,0), (0,h_pad), (0,w_pad), (0,0) )) self._img = img return scale def clip(self, min, max) -> 'ImageProcessor': np.clip(self._img, min, max, out=self._img) return self def clip2(self, low_check, low_val, high_check, high_val) -> 'ImageProcessor': img = self._img l, h = img < low_check, img > high_check img[l] = low_val img[h] = high_val return self def reresize(self, power : float, interpolation : 'ImageProcessor.Interpolation' = None, mask = None) -> 'ImageProcessor': """ power float 0 .. 1.0 """ power = min(1, max(0, power)) if power == 0: return self if interpolation is None: interpolation = ImageProcessor.Interpolation.LINEAR img = orig_img = self._img N,H,W,C = img.shape W_lr = max(4, int(W*(1.0-power))) H_lr = max(4, int(H*(1.0-power))) img = img.transpose( (1,2,0,3) ).reshape( (H,W,N*C) ) img = cv2.resize (img, (W_lr,H_lr), interpolation=_cv_inter[interpolation]) img = cv2.resize (img, (W,H) , interpolation=_cv_inter[interpolation]) img = img.reshape( (H,W,N,C) ).transpose( (2,0,1,3) ) if mask is not None: mask = self._check_normalize_mask(mask) img = ne.evaluate('orig_img*(1-mask) + img*mask').astype(orig_img.dtype) self._img = img return self def box_sharpen(self, size : int, power : float, mask = None) -> 'ImageProcessor': """ size int kernel size power float 0 .. 1.0 (or higher) """ power = max(0, power) if power == 0: return self if size % 2 == 0: size += 1 dtype = self.get_dtype() self.to_ufloat32() img = orig_img = self._img N,H,W,C = img.shape img = img.transpose( (1,2,0,3) ).reshape( (H,W,N*C) ) kernel = np.zeros( (size, size), dtype=np.float32) kernel[ size//2, size//2] = 1.0 box_filter = np.ones( (size, size), dtype=np.float32) / (size**2) kernel = kernel + (kernel - box_filter) * (power) img = cv2.filter2D(img, -1, kernel) img = np.clip(img, 0, 1, out=img) img = img.reshape( (H,W,N,C) ).transpose( (2,0,1,3) ) if mask is not None: mask = self._check_normalize_mask(mask) img = ne.evaluate('orig_img*(1-mask) + img*mask') self._img = img self.to_dtype(dtype) return self def gaussian_sharpen(self, sigma : float, power : float, mask = None) -> 'ImageProcessor': """ sigma float power float 0 .. 1.0 and higher """ sigma = max(0, sigma) if sigma == 0: return self dtype = self.get_dtype() self.to_ufloat32() img = orig_img = self._img N,H,W,C = img.shape img = img.transpose( (1,2,0,3) ).reshape( (H,W,N*C) ) img = cv2.addWeighted(img, 1.0 + power, cv2.GaussianBlur(img, (0, 0), sigma), -power, 0) img = np.clip(img, 0, 1, out=img) img = img.reshape( (H,W,N,C) ).transpose( (2,0,1,3) ) if mask is not None: mask = self._check_normalize_mask(mask) img = ne.evaluate('orig_img*(1-mask) + img*mask') self._img = img self.to_dtype(dtype) return self def gaussian_blur(self, sigma : float, opacity : float = 1.0, mask = None) -> 'ImageProcessor': """