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- """
- A script to evaluate the model's performance using pre-trained weights using COCO API.
- Example usage: python evaluate_on_coco.py -dir D:\cocoDataset\val2017\val2017 -gta D:\cocoDataset\annotatio
- ns_trainval2017\annotations\instances_val2017.json -c cfg/yolov4-smaller-input.cfg -g 0
- Explanation: set where your images can be found using -dir, then use -gta to point to the ground truth annotations file
- and finally -c to point to the config file you want to use to load the network using.
- """
- import argparse
- import datetime
- import json
- import logging
- import os
- import sys
- import time
- from collections import defaultdict
- import numpy as np
- import torch
- from PIL import Image, ImageDraw
- from easydict import EasyDict as edict
- from pycocotools.coco import COCO
- from pycocotools.cocoeval import COCOeval
- from cfg import Cfg
- from tool.darknet2pytorch import Darknet
- from tool.utils import load_class_names
- from tool.torch_utils import do_detect
- def get_class_name(cat):
- class_names = load_class_names("./data/coco.names")
- if cat >= 1 and cat <= 11:
- cat = cat - 1
- elif cat >= 13 and cat <= 25:
- cat = cat - 2
- elif cat >= 27 and cat <= 28:
- cat = cat - 3
- elif cat >= 31 and cat <= 44:
- cat = cat - 5
- elif cat >= 46 and cat <= 65:
- cat = cat - 6
- elif cat == 67:
- cat = cat - 7
- elif cat == 70:
- cat = cat - 9
- elif cat >= 72 and cat <= 82:
- cat = cat - 10
- elif cat >= 84 and cat <= 90:
- cat = cat - 11
- return class_names[cat]
- def convert_cat_id_and_reorientate_bbox(single_annotation):
- cat = single_annotation['category_id']
- bbox = single_annotation['bbox']
- x, y, w, h = bbox
- x1, y1, x2, y2 = x - w / 2, y - h / 2, x + w / 2, y + h / 2
- if 0 <= cat <= 10:
- cat = cat + 1
- elif 11 <= cat <= 23:
- cat = cat + 2
- elif 24 <= cat <= 25:
- cat = cat + 3
- elif 26 <= cat <= 39:
- cat = cat + 5
- elif 40 <= cat <= 59:
- cat = cat + 6
- elif cat == 60:
- cat = cat + 7
- elif cat == 61:
- cat = cat + 9
- elif 62 <= cat <= 72:
- cat = cat + 10
- elif 73 <= cat <= 79:
- cat = cat + 11
- single_annotation['category_id'] = cat
- single_annotation['bbox'] = [x1, y1, w, h]
- return single_annotation
- def myconverter(obj):
- if isinstance(obj, np.integer):
- return int(obj)
- elif isinstance(obj, np.floating):
- return float(obj)
- elif isinstance(obj, np.ndarray):
- return obj.tolist()
- elif isinstance(obj, datetime.datetime):
- return obj.__str__()
- else:
- return obj
- def evaluate_on_coco(cfg, resFile):
- annType = "bbox" # specify type here
- with open(resFile, 'r') as f:
- unsorted_annotations = json.load(f)
- sorted_annotations = list(sorted(unsorted_annotations, key=lambda single_annotation: single_annotation["image_id"]))
- sorted_annotations = list(map(convert_cat_id_and_reorientate_bbox, sorted_annotations))
- reshaped_annotations = defaultdict(list)
- for annotation in sorted_annotations:
- reshaped_annotations[annotation['image_id']].append(annotation)
- with open('temp.json', 'w') as f:
- json.dump(sorted_annotations, f)
- cocoGt = COCO(cfg.gt_annotations_path)
- cocoDt = cocoGt.loadRes('temp.json')
- with open(cfg.gt_annotations_path, 'r') as f:
- gt_annotation_raw = json.load(f)
- gt_annotation_raw_images = gt_annotation_raw["images"]
- gt_annotation_raw_labels = gt_annotation_raw["annotations"]
- rgb_label = (255, 0, 0)
- rgb_pred = (0, 255, 0)
- for i, image_id in enumerate(reshaped_annotations):
- image_annotations = reshaped_annotations[image_id]
- gt_annotation_image_raw = list(filter(
- lambda image_json: image_json['id'] == image_id, gt_annotation_raw_images
- ))
- gt_annotation_labels_raw = list(filter(
- lambda label_json: label_json['image_id'] == image_id, gt_annotation_raw_labels
- ))
- if len(gt_annotation_image_raw) == 1:
- image_path = os.path.join(cfg.dataset_dir, gt_annotation_image_raw[0]["file_name"])
- actual_image = Image.open(image_path).convert('RGB')
- draw = ImageDraw.Draw(actual_image)
- for annotation in image_annotations:
- x1_pred, y1_pred, w, h = annotation['bbox']
- x2_pred, y2_pred = x1_pred + w, y1_pred + h
- cls_id = annotation['category_id']
- label = get_class_name(cls_id)
- draw.text((x1_pred, y1_pred), label, fill=rgb_pred)
- draw.rectangle([x1_pred, y1_pred, x2_pred, y2_pred], outline=rgb_pred)
- for annotation in gt_annotation_labels_raw:
- x1_truth, y1_truth, w, h = annotation['bbox']
- x2_truth, y2_truth = x1_truth + w, y1_truth + h
- cls_id = annotation['category_id']
- label = get_class_name(cls_id)
- draw.text((x1_truth, y1_truth), label, fill=rgb_label)
- draw.rectangle([x1_truth, y1_truth, x2_truth, y2_truth], outline=rgb_label)
- actual_image.save("./data/outcome/predictions_{}".format(gt_annotation_image_raw[0]["file_name"]))
- else:
- print('please check')
- break
- if (i + 1) % 100 == 0: # just see first 100
- break
- imgIds = sorted(cocoGt.getImgIds())
- cocoEval = COCOeval(cocoGt, cocoDt, annType)
- cocoEval.params.imgIds = imgIds
- cocoEval.evaluate()
- cocoEval.accumulate()
- cocoEval.summarize()
- def test(model, annotations, cfg):
- if not annotations["images"]:
- print("Annotations do not have 'images' key")
- return
- images = annotations["images"]
- # images = images[:10]
- resFile = 'data/coco_val_outputs.json'
- if torch.cuda.is_available():
- use_cuda = 1
- else:
- use_cuda = 0
- # do one forward pass first to circumvent cold start
- throwaway_image = Image.open('data/dog.jpg').convert('RGB').resize((model.width, model.height))
- do_detect(model, throwaway_image, 0.5, 80, 0.4, use_cuda)
- boxes_json = []
- for i, image_annotation in enumerate(images):
- logging.info("currently on image: {}/{}".format(i + 1, len(images)))
- image_file_name = image_annotation["file_name"]
- image_id = image_annotation["id"]
- image_height = image_annotation["height"]
- image_width = image_annotation["width"]
- # open and resize each image first
- img = Image.open(os.path.join(cfg.dataset_dir, image_file_name)).convert('RGB')
- sized = img.resize((model.width, model.height))
- if use_cuda:
- model.cuda()
- start = time.time()
- boxes = do_detect(model, sized, 0.0, 80, 0.4, use_cuda)
- finish = time.time()
- if type(boxes) == list:
- for box in boxes:
- box_json = {}
- category_id = box[-1]
- score = box[-2]
- bbox_normalized = box[:4]
- box_json["category_id"] = int(category_id)
- box_json["image_id"] = int(image_id)
- bbox = []
- for i, bbox_coord in enumerate(bbox_normalized):
- modified_bbox_coord = float(bbox_coord)
- if i % 2:
- modified_bbox_coord *= image_height
- else:
- modified_bbox_coord *= image_width
- modified_bbox_coord = round(modified_bbox_coord, 2)
- bbox.append(modified_bbox_coord)
- box_json["bbox_normalized"] = list(map(lambda x: round(float(x), 2), bbox_normalized))
- box_json["bbox"] = bbox
- box_json["score"] = round(float(score), 2)
- box_json["timing"] = float(finish - start)
- boxes_json.append(box_json)
- # print("see box_json: ", box_json)
- with open(resFile, 'w') as outfile:
- json.dump(boxes_json, outfile, default=myconverter)
- else:
- print("warning: output from model after postprocessing is not a list, ignoring")
- return
- # namesfile = 'data/coco.names'
- # class_names = load_class_names(namesfile)
- # plot_boxes(img, boxes, 'data/outcome/predictions_{}.jpg'.format(image_id), class_names)
- with open(resFile, 'w') as outfile:
- json.dump(boxes_json, outfile, default=myconverter)
- evaluate_on_coco(cfg, resFile)
- def get_args(**kwargs):
- cfg = kwargs
- parser = argparse.ArgumentParser(description='Test model on test dataset',
- formatter_class=argparse.ArgumentDefaultsHelpFormatter)
- parser.add_argument('-f', '--load', dest='load', type=str, default=None,
- help='Load model from a .pth file')
- parser.add_argument('-g', '--gpu', metavar='G', type=str, default='-1',
- help='GPU', dest='gpu')
- parser.add_argument('-dir', '--data-dir', type=str, default=None,
- help='dataset dir', dest='dataset_dir')
- parser.add_argument('-gta', '--ground_truth_annotations', type=str, default='instances_val2017.json',
- help='ground truth annotations file', dest='gt_annotations_path')
- parser.add_argument('-w', '--weights_file', type=str, default='weights/yolov4.weights',
- help='weights file to load', dest='weights_file')
- parser.add_argument('-c', '--model_config', type=str, default='cfg/yolov4.cfg',
- help='model config file to load', dest='model_config')
- args = vars(parser.parse_args())
- for k in args.keys():
- cfg[k] = args.get(k)
- return edict(cfg)
- def init_logger(log_file=None, log_dir=None, log_level=logging.INFO, mode='w', stdout=True):
- """
- log_dir: 日志文件的文件夹路径
- mode: 'a', append; 'w', 覆盖原文件写入.
- """
- import datetime
- def get_date_str():
- now = datetime.datetime.now()
- return now.strftime('%Y-%m-%d_%H-%M-%S')
- fmt = '%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s: %(message)s'
- if log_dir is None:
- log_dir = '~/temp/log/'
- if log_file is None:
- log_file = 'log_' + get_date_str() + '.txt'
- if not os.path.exists(log_dir):
- os.makedirs(log_dir)
- log_file = os.path.join(log_dir, log_file)
- # 此处不能使用logging输出
- print('log file path:' + log_file)
- logging.basicConfig(level=logging.DEBUG,
- format=fmt,
- filename=log_file,
- filemode=mode)
- if stdout:
- console = logging.StreamHandler(stream=sys.stdout)
- console.setLevel(log_level)
- formatter = logging.Formatter(fmt)
- console.setFormatter(formatter)
- logging.getLogger('').addHandler(console)
- return logging
- if __name__ == "__main__":
- logging = init_logger(log_dir='log')
- cfg = get_args(**Cfg)
- os.environ["CUDA_VISIBLE_DEVICES"] = cfg.gpu
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- logging.info(f'Using device {device}')
- model = Darknet(cfg.model_config)
- model.print_network()
- model.load_weights(cfg.weights_file)
- model.eval() # set model away from training
- if torch.cuda.device_count() > 1:
- model = torch.nn.DataParallel(model)
- model.to(device=device)
- annotations_file_path = cfg.gt_annotations_path
- with open(annotations_file_path) as annotations_file:
- try:
- annotations = json.load(annotations_file)
- except:
- print("annotations file not a json")
- exit()
- test(model=model,
- annotations=annotations,
- cfg=cfg, )
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