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| #!/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 | |
| 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] | |
| 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.basename(img_path).split('.')[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) | |
| 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 | |
| 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 | |
| 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) | |
| 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) | |
| 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 | |
| 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 | |