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detect.py
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import argparse
from sys import platform
import time
import os
import glob
from model.models import Darknet
from model.model_utils import attempt_download,parse_data_cfg
from utils.datasets import LoadImages
from utils.utils import *
from utils.datasets import letterbox
from utils.nms.nms import non_max_suppression
from utils.ICDAR.icdar_utils import xywha2icdar, zip_dir
# 我自己写的还快些,FPS更高,性能没有下降
def multi_detect(save_txt=True, save_img=True, hyp=None, multi_scale = False):
img_size = opt.img_size # (320, 192) or (416, 256) or (608, 352) for (height, width)
out, source, weights, half, view_img = opt.output, opt.source, opt.weights, opt.half, opt.view_img
# Setting txt result folder
save_type = 'ICDAR'
if save_txt and save_type == 'ICDAR':
out_txt = 'icdar_result'
if os.path.exists(out_txt):
shutil.rmtree(out_txt) # delete output folder
os.makedirs(out_txt) # make new output folder
webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
# Initialize
device = torch_utils.select_device(opt.device)
if os.path.exists(out):
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder
# Get classes and colors
classes = load_classes(parse_data_cfg(opt.data)['names']) # .data文件解析成dict并索引类别名的name文件地址
colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(classes))] # 配置颜色
# build model
model = Darknet(opt.cfg, hyp) # 搭建模型（不连接计算图），只调用构造函数
## Load weights
attempt_download(weights)
if weights.endswith('.pt'): # pytorch format
model.load_state_dict(torch.load(weights, map_location=device)['model'])
## Eval mode
model.to(device).eval()
# Half precision
half = half and device.type != 'cpu' # half precision only supported on CUDA
if half:
model.half() # pytorch原生支持fp16训练
# multi-scale
if multi_scale:
size_num = 2
img_sz_min = round(img_size / 32 / 1.5) + 1
img_sz_max = round(img_size / 32 * 1.3) - 1
img_sizes = np.random.choice(range(img_sz_min * 32, (img_sz_max+1) * 32, 32), size_num, False)
img_sizes = [int(x) for x in img_sizes]
# img_sizes = [608]
print('use scale: {}'.format(img_sizes))
else:
img_sizes = [img_size]
# load images
t0 = time.time()
img_paths = sorted(glob.glob(os.path.join(source, '*.jpg')))
for id, img_path in enumerate(img_paths):
img0 = cv2.imread(img_path)
p, s, = img_path, ''
save_path = str(Path(out) / Path(p).name)
assert img0 is not None, 'Image Not Found ' + path
print('image %g/%g %s: ' % (id, len(img_paths), img_path), end='')
# multi-scale inference
all_pre = []
t = time.time()
for img_size in img_sizes:
img, *_ = letterbox(img0, new_shape=img_size)
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB
img = np.ascontiguousarray(img, dtype=np.float16 if opt.half else np.float32) # uint8 to fp16/fp32
img /= 255.0
# Get detections
img = torch.from_numpy(img).to(device)
if img.ndimension() == 3: # 查看数据维度是否为三维，等价于len(img.shape)
img = img.unsqueeze(0) # 加个第0维bs，但是detect实际没用
pred, _ = model(img) # forward
# nms
det = non_max_suppression(pred, opt.conf_thres, 0.95)[0] # bs只允许1,不支持视频
if det is not None and len(det):
# Rescale boxes from img_size to im0 size 将预测的bbox坐标(前四维)从缩放图放大回原图尺度
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0.shape).round()
all_pre.append(det)
all_pre = list(filter(is_None,all_pre))
if all_pre is not None and len(all_pre):
merged_dets = torch.cat(all_pre, 0)
final_dets = non_max_suppression(merged_dets.unsqueeze(0), opt.conf_thres, opt.nms_thres)[0]
# Print results： 统计各类物体出现的次数
if final_dets is not None and len(final_dets):
for c in final_dets[:, -1].unique(): # 取出最后一维类别并去重排序
n = (final_dets[:, -1] == c).sum() # detections per class
s += '%g %ss, ' % (n, classes[int(c)]) # s添加检测物体统计
# Write results
for *box, conf, _, cls in final_dets:
if save_txt and save_type == 'ICDAR': # Write to file
save_icdar_path = str(Path(out_txt)/ ('res_'+os.path.splitext(Path(p).name)[0]+'.txt'))
with open(save_icdar_path , 'a') as file:
file.write(xywha2icdar(box))
elif save_txt:
with open(save_path + '.txt', 'a') as file:
file.write(('%g ' * 7 + '\n') % (*box, cls, conf))
if save_img or view_img: # Add bbox to image
label = '%s %.2f' % (classes[int(cls)], conf)
plot_one_box(box, img0, label=label, color=colors[int(cls)])
print('%sDone. (%.3fs)' % (s, time.time() - t))
# Stream results
if view_img:
cv2.imshow(p, img0)
# Save results (image with detections)
if save_img:
cv2.imwrite(save_path, img0)
if save_txt or save_img:
print('Results saved to %s' % os.getcwd() + os.sep + out)
if platform == 'darwin': # MacOS
os.system('open ' + out + ' ' + save_path)
print('Done. (%.3fs)' % (time.time() - t0))
zip_dir(out_txt, 'icdar_result.zip')
shutil.rmtree(out_txt)
def detect(save_txt = True, save_img = False, hyp = None):
img_size = opt.img_size # (320, 192) or (416, 256) or (608, 352) for (height, width)
out, source, weights, half, view_img = opt.output, opt.source, opt.weights, opt.half, opt.view_img
# Setting txt result folder
save_type = 'ICDAR'
if save_txt and save_type == 'ICDAR':
out_txt = 'detections'
if os.path.exists(out_txt):
shutil.rmtree(out_txt) # delete output folder
os.makedirs(out_txt) # make new output folder
webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
# Initialize
device = torch_utils.select_device(opt.device)
if os.path.exists(out):
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder
# Initialize model
model = Darknet(opt.cfg, hyp ) # 搭建模型（不连接计算图），只调用构造函数
# Load weights
attempt_download(weights)
if weights.endswith('.pt'): # pytorch format
model.load_state_dict(torch.load(weights, map_location=device)['model'])
else: # darknet format
_ = load_darknet_weights(model, weights)
# Fuse Conv2d + BatchNorm2d layers
# model.fuse()
# Eval mode
model.to(device).eval()
# Half precision
half = half and device.type != 'cpu' # half precision only supported on CUDA
if half:
model.half() # pytorch原生支持fp16
# Set Dataloader
vid_path, vid_writer = None, None
if webcam:
view_img = True
torch.backends.cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=img_size, half=half)
else:
save_img = True
dataset = LoadImages(source, img_size=img_size, half=half) # source是测试的文件夹路径，返回的dataset是一个迭代器
# Get classes and colors
classes = load_classes(parse_data_cfg(opt.data)['names']) # .data文件解析成dict并索引类别名的name文件地址
colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(classes))] # 配置颜色
# Run inference
t0 = time.time()
for path, img, im0s, vid_cap in dataset: # im0s为原图(hwc)，im0s为缩放+padding之后的图(chw)
t = time.time()
# Get detections
img = torch.from_numpy(img).to(device)
if img.ndimension() == 3: # 查看数据维度是否为三维，等价于len(img.shape)
img = img.unsqueeze(0) # 加个第0维bs，但是detect实际没用
# 只用到io的结果，不用p；io有三个维度：bs(1),num_proposal(每个yolo层预测其特征图的w*h*3个proposal),num_params(5+classes)
pred, _ = model(img) # forward
# NMS后返回的张量维度：[(num_detections,7),...] (7=(x1, y1, x2, y2, object_conf, class_conf, class)) (len=bs)
# 遍历时det是每张图片的bbox属性： (num_detections,7)
# 实际上在图像中遍历只会执行一次,这里的i=0就跳出了
for i, det in enumerate(non_max_suppression(pred, opt.conf_thres, opt.nms_thres)): # detections per image
if webcam: # batch_size >= 1
p, s, im0 = path[i], '%g: ' % i, im0s[i]
else:
p, s, im0 = path, '', im0s
save_path = str(Path(out) / Path(p).name)
# s 是最后检测打印的字符串，会通过字符串拼接逐渐添加项
s += '%gx%g ' % img.shape[2:] # s添加缩放后的图像尺度，如 320x416
if det is not None and len(det):
# Rescale boxes from img_size to im0 size 将预测的bbox坐标(前四维)从缩放图放大回原图尺度
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Print results： 统计各类物体出现的次数
for c in det[:, -1].unique(): # 取出最后一维类别并去重排序
n = (det[:, -1] == c).sum() # detections per class
s += '%g %ss, ' % (n, classes[int(c)]) # s添加检测物体统计
# Write results
for *box, conf, _, cls in det:
if save_txt and save_type == 'ICDAR': # Write to file
save_icdar_path = str(Path(out_txt)/ ('res_'+os.path.splitext(Path(p).name)[0]+'.txt'))
with open(save_icdar_path , 'a') as file:
file.write(xywha2icdar(box))
elif save_txt:
with open(save_path + '.txt', 'a') as file:
file.write(('%g ' * 7 + '\n') % (*box, cls, conf))
if save_img or view_img: # Add bbox to image
label = '%s %.2f' % (classes[int(cls)], conf)
plot_one_box(box, im0, label=label, color=colors[int(cls)])
# plot_one_box(box, im0, label=label, color=[0,0,255], line_thickness=1)
print('%sDone. (%.3fs)' % (s, time.time() - t))
# Stream results
if view_img:
cv2.imshow(p, im0)
# Save results (image with detections)
if save_img:
if dataset.mode == 'images':
cv2.imwrite(save_path, im0)
else:
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release() # release previous video writer
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*opt.fourcc), fps, (w, h))
vid_writer.write(im0)
if save_txt or save_img:
print('Results saved to %s' % os.getcwd() + os.sep + out)
if platform == 'darwin': # MacOS
os.system('open ' + out + ' ' + save_path)
print('Done. (%.3fs)' % (time.time() - t0))
zip_dir(out_txt, 'icdar_result.zip')
shutil.rmtree(out_txt)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--hyp', type=str, default='cfg/ICDAR/hyp.py', help='hyper-parameter path')
parser.add_argument('--cfg', type=str, default='cfg/ICDAR/yolov3_608_dh_o8_ga.cfg', help='cfg file path')
parser.add_argument('--data', type=str, default='data/tiny.data', help='*.data file path')
# parser.add_argument('--data', type=str, default='data/icdar13+15.data', help='coco.data file path')
# parser.add_argument('--data', type=str, default='data/single.data', help='*.data file path')
parser.add_argument('--weights', type=str, default='weights/last.pt', help='path to weights file')
parser.add_argument('--source', type=str, default='data/tiny/test', help='source') # input file/folder, 0 for webcam
parser.add_argument('--output', type=str, default='output', help='output folder') # output folder
parser.add_argument('--img-size', type=int, default=608, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
parser.add_argument('--nms-thres', type=float, default=0.3, help='iou threshold for non-maximum suppression')
parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)')
parser.add_argument('--half', action='store_true', help='half precision FP16 inference')
parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
parser.add_argument('--view-img', action='store_true', help='display results')
parser.add_argument('--multi-scale', action='store_true', help='multi-scale testing')
opt = parser.parse_args()
print(opt)
hyp = hyp_parse(opt.hyp)
with torch.no_grad():
if opt.multi_scale:
multi_detect(hyp=hyp, multi_scale = True)
else:
# multi_detect(hyp=hyp)
detect(hyp=hyp)