#!/usr/bin/env python3 # -*- coding:utf-8 -*- import os import os.path as osp import math from tqdm import tqdm import numpy as np import cv2 import torch from PIL import ImageFont from yolov6.utils.events import LOGGER, load_yaml from yolov6.layers.common import DetectBackend from yolov6.data.data_augment import letterbox from yolov6.utils.nms import non_max_suppression from yolov6.utils.torch_utils import get_model_info class Inferer: def __init__(self, source, weights, device, yaml, img_size, half): import glob from yolov6.data.datasets import IMG_FORMATS self.__dict__.update(locals()) # Init model self.device = device self.img_size = img_size cuda = self.device != 'cpu' and torch.cuda.is_available() self.device = torch.device('cuda:0' if cuda else 'cpu') self.model = DetectBackend(weights, device=self.device) self.stride = self.model.stride self.class_names = load_yaml(yaml)['names'] self.img_size = self.check_img_size(self.img_size, s=self.stride) # check image size # Half precision if half & (self.device.type != 'cpu'): self.model.model.half() else: self.model.model.float() half = False if self.device.type != 'cpu': self.model(torch.zeros(1, 3, *self.img_size).to(self.device).type_as(next(self.model.model.parameters()))) # warmup # Load data if os.path.isdir(source): img_paths = sorted(glob.glob(os.path.join(source, '*.*'))) # dir elif os.path.isfile(source): img_paths = [source] # files else: raise Exception(f'Invalid path: {source}') self.img_paths = [img_path for img_path in img_paths if img_path.split('.')[-1].lower() in IMG_FORMATS] # Switch model to deploy status self.model_switch(self.model, self.img_size) def model_switch(self, model, img_size): ''' Model switch to deploy status ''' from yolov6.layers.common import RepVGGBlock for layer in model.modules(): if isinstance(layer, RepVGGBlock): layer.switch_to_deploy() LOGGER.info("Switch model to deploy modality.") def infer(self, conf_thres, iou_thres, classes, agnostic_nms, max_det, save_dir, save_txt, save_img, hide_labels, hide_conf): ''' Model Inference and results visualization ''' for img_path in tqdm(self.img_paths): img, img_src = self.precess_image(img_path, self.img_size, self.stride, self.half) img = img.to(self.device) if len(img.shape) == 3: img = img[None] # expand for batch dim pred_results = self.model(img) det = non_max_suppression(pred_results, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)[0] save_path = osp.join(save_dir, osp.basename(img_path)) # im.jpg txt_path = osp.join(save_dir, 'labels', osp.splitext(osp.basename(img_path))[0]) gn = torch.tensor(img_src.shape)[[1, 0, 1, 0]] # normalization gain whwh img_ori = img_src # check image and font assert img_ori.data.contiguous, 'Image needs to be contiguous. Please apply to input images with np.ascontiguousarray(im).' self.font_check() if len(det): det[:, :4] = self.rescale(img.shape[2:], det[:, :4], img_src.shape).round() for *xyxy, conf, cls in reversed(det): if save_txt: # Write to file xywh = (self.box_convert(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh line = (cls, *xywh, conf) with open(txt_path + '.txt', 'a') as f: f.write(('%g ' * len(line)).rstrip() % line + '\n') if save_img: class_num = int(cls) # integer class label = None if hide_labels else (self.class_names[class_num] if hide_conf else f'{self.class_names[class_num]} {conf:.2f}') self.plot_box_and_label(img_ori, max(round(sum(img_ori.shape) / 2 * 0.003), 2), xyxy, label, color=self.generate_colors(class_num, True)) img_src = np.asarray(img_ori) # Save results (image with detections) if save_img: cv2.imwrite(save_path, img_src) @staticmethod def precess_image(path, img_size, stride, half): '''Process image before image inference.''' try: img_src = cv2.imread(path) assert img_src is not None, f'Invalid image: {path}' except Exception as e: LOGGER.warning(e) image = letterbox(img_src, img_size, stride=stride)[0] # Convert image = image.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB image = torch.from_numpy(np.ascontiguousarray(image)) image = image.half() if half else image.float() # uint8 to fp16/32 image /= 255 # 0 - 255 to 0.0 - 1.0 return image, img_src @staticmethod def rescale(ori_shape, boxes, target_shape): '''Rescale the output to the original image shape''' ratio = min(ori_shape[0] / target_shape[0], ori_shape[1] / target_shape[1]) padding = (ori_shape[1] - target_shape[1] * ratio) / 2, (ori_shape[0] - target_shape[0] * ratio) / 2 boxes[:, [0, 2]] -= padding[0] boxes[:, [1, 3]] -= padding[1] boxes[:, :4] /= ratio boxes[:, 0].clamp_(0, target_shape[1]) # x1 boxes[:, 1].clamp_(0, target_shape[0]) # y1 boxes[:, 2].clamp_(0, target_shape[1]) # x2 boxes[:, 3].clamp_(0, target_shape[0]) # y2 return boxes def check_img_size(self, img_size, s=32, floor=0): """Make sure image size is a multiple of stride s in each dimension, and return a new shape list of image.""" if isinstance(img_size, int): # integer i.e. img_size=640 new_size = max(self.make_divisible(img_size, int(s)), floor) elif isinstance(img_size, list): # list i.e. img_size=[640, 480] new_size = [max(self.make_divisible(x, int(s)), floor) for x in img_size] else: raise Exception(f"Unsupported type of img_size: {type(img_size)}") if new_size != img_size: print(f'WARNING: --img-size {img_size} must be multiple of max stride {s}, updating to {new_size}') return new_size if isinstance(img_size,list) else [new_size]*2 def make_divisible(self, x, divisor): # Upward revision the value x to make it evenly divisible by the divisor. return math.ceil(x / divisor) * divisor @staticmethod def plot_box_and_label(image, lw, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)): # Add one xyxy box to image with label p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3])) cv2.rectangle(image, p1, p2, color, thickness=lw, lineType=cv2.LINE_AA) if label: tf = max(lw - 1, 1) # font thickness w, h = cv2.getTextSize(label, 0, fontScale=lw / 3, thickness=tf)[0] # text width, height outside = p1[1] - h - 3 >= 0 # label fits outside box p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3 cv2.rectangle(image, p1, p2, color, -1, cv2.LINE_AA) # filled cv2.putText(image, label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2), 0, lw / 3, txt_color, thickness=tf, lineType=cv2.LINE_AA) @staticmethod def font_check(font='./yolov6/utils/Arial.ttf', size=10): # Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary assert osp.exists(font), f'font path not exists: {font}' try: return ImageFont.truetype(str(font) if font.exists() else font.name, size) except Exception as e: # download if missing return ImageFont.truetype(str(font), size) @staticmethod def box_convert(x): # Convert boxes with shape [n, 4] from [x1, y1, x2, y2] to [x, y, w, h] where x1y1=top-left, x2y2=bottom-right y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center y[:, 2] = x[:, 2] - x[:, 0] # width y[:, 3] = x[:, 3] - x[:, 1] # height return y @staticmethod def generate_colors(i, bgr=False): hex = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB', '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7') palette = [] for iter in hex: h = '#' + iter palette.append(tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))) num = len(palette) color = palette[int(i) % num] return (color[2], color[1], color[0]) if bgr else color