162 lines
6.0 KiB
Python
162 lines
6.0 KiB
Python
# coding: utf-8
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import torch
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import cv2
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import numpy as np
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import math
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import time
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import kneron_preprocessing
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kneron_preprocessing.API.set_default_as_520()
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torch.backends.cudnn.deterministic = True
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img_formats = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.dng']
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def make_divisible(x, divisor):
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# Returns x evenly divisble by divisor
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return math.ceil(x / divisor) * divisor
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def check_img_size(img_size, s=32):
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# Verify img_size is a multiple of stride s
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new_size = make_divisible(img_size, int(s)) # ceil gs-multiple
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if new_size != img_size:
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print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size))
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return new_size
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def letterbox_ori(img, new_shape=(640, 640), color=(0, 0, 0), auto=True, scaleFill=False, scaleup=True):
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# Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
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shape = img.shape[:2] # current shape [height, width]
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if isinstance(new_shape, int):
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new_shape = (new_shape, new_shape)
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# Scale ratio (new / old)
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r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
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if not scaleup: # only scale down, do not scale up (for better test mAP)
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r = min(r, 1.0)
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# Compute padding
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ratio = r, r # width, height ratios
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new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) # width, height
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dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
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dw /= 2 # divide padding into 2 sides
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dh /= 2
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if shape[::-1] != new_unpad: # resize
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img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
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#img = kneron_preprocessing.API.resize(img,size=new_unpad, keep_ratio = False)
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top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
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left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
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# top, bottom = int(0), int(round(dh + 0.1))
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# left, right = int(0), int(round(dw + 0.1))
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img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
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#img = kneron_preprocessing.API.pad(img, left, right, top, bottom, 0)
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return img, ratio, (dw, dh)
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def letterbox(img, new_shape=(640, 640), color=(0, 0, 0), auto=True, scaleFill=False, scaleup=True):
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# Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
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shape = img.shape[:2] # current shape [height, width]
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if isinstance(new_shape, int):
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new_shape = (new_shape, new_shape)
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# Scale ratio (new / old)
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r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
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if not scaleup: # only scale down, do not scale up (for better test mAP)
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r = min(r, 1.0)
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# Compute padding
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ratio = r, r # width, height ratios
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new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) # width, height
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dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
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# dw /= 2 # divide padding into 2 sides
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# dh /= 2
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if shape[::-1] != new_unpad: # resize
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#img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
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img = kneron_preprocessing.API.resize(img,size=new_unpad, keep_ratio = False)
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# top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
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# left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
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top, bottom = int(0), int(round(dh + 0.1))
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left, right = int(0), int(round(dw + 0.1))
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#img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
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img = kneron_preprocessing.API.pad(img, left, right, top, bottom, 0)
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return img, ratio, (dw, dh)
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def letterbox_test(img, new_shape=(640, 640), color=(0, 0, 0), auto=True, scaleFill=False, scaleup=True):
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ratio = 1.0, 1.0
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dw, dh = 0, 0
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img = kneron_preprocessing.API.resize(img, size=(480, 256), keep_ratio=False, type='bilinear')
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return img, ratio, (dw, dh)
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def LoadImages(path,img_size): #_rgb # for inference
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if isinstance(path, str):
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img0 = cv2.imread(path) # BGR
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else:
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img0 = path # BGR
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# Padded resize
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img = letterbox(img0, new_shape=img_size)[0]
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# Convert
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img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
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img = np.ascontiguousarray(img)
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return img, img0
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def LoadImages_yyy(path,img_size): #_yyy # for inference
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if isinstance(path, str):
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img0 = cv2.imread(path) # BGR
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else:
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img0 = path # BGR
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yvu = cv2.cvtColor(img0, cv2.COLOR_BGR2YCrCb)
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y, v, u = cv2.split(yvu)
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img0 = np.stack((y,)*3, axis=-1)
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# Padded resize
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img = letterbox(img0, new_shape=img_size)[0]
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# Convert
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img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
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img = np.ascontiguousarray(img)
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return img, img0
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def LoadImages_yuv420(path,img_size): #_yuv420 # for inference
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if isinstance(path, str):
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img0 = cv2.imread(path) # BGR
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else:
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img0 = path # BGR
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img_h, img_w = img0.shape[:2]
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img_h = (img_h // 2) * 2
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img_w = (img_w // 2) * 2
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img = img0[:img_h,:img_w,:]
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yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV_I420)
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img0= cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR_I420) #yuv420
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# Padded resize
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img = letterbox(img0, new_shape=img_size)[0]
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# Convert
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img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
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img = np.ascontiguousarray(img)
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return img, img0
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def Yolov5_preprocess(image_path, device, imgsz_h, imgsz_w) :
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model_stride_max = 32
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imgsz_h = check_img_size(imgsz_h, s=model_stride_max) # check img_size
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imgsz_w = check_img_size(imgsz_w, s=model_stride_max) # check img_size
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img, im0 = LoadImages(image_path, img_size=(imgsz_h,imgsz_w))
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img = kneron_preprocessing.API.norm(img) #path1
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#print('img',img.shape)
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img = torch.from_numpy(img).to(device) #path1,path2
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# img = img.float() # uint8 to fp16/32 #path2
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# img /= 255.0#256.0 - 0.5 # 0 - 255 to -0.5 - 0.5 #path2
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if img.ndimension() == 3:
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img = img.unsqueeze(0)
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return img, im0
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