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detect.py
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import argparse
from sys import platform
from cellyolomodels import * # set ONNX_EXPORT in cellyolomodels.py
from cellyolo.utils import *
def segmentation(opt):
img_size = (320, 192) if ONNX_EXPORT else opt.img_size # (320, 192) or (416, 256) or (608, 352) for (height, width)
out, source, weights, half, view_img, save_txt = opt.output, opt.source, opt.weights, opt.half, opt.view_img, opt.save_txt
webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
# Initialize
device = torch_utils.select_device(device='cpu' if ONNX_EXPORT else 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, img_size)
# 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)
# Second-stage classifier
classify = False
if classify:
modelc = torch_utils.load_classifier(name='resnet101', n=2) # initialize
modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']) # load weights
modelc.to(device).eval()
# Fuse Conv2d + BatchNorm2d layers
# model.fuse()
# Eval mode
model.to(device).eval()
# Export mode
if ONNX_EXPORT:
img = torch.zeros((1, 3) + img_size) # (1, 3, 320, 192)
torch.onnx.export(model, img, 'weights/export.onnx', verbose=False, opset_version=10)
# Validate exported model
import onnx
model = onnx.load('weights/export.onnx') # Load the ONNX model
onnx.checker.check_model(model) # Check that the IR is well formed
print(onnx.helper.printable_graph(model.graph)) # Print a human readable representation of the graph
return
# Half precision
half = half and device.type != 'cpu' # half precision only supported on CUDA
if half:
model.half()
# 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)
# Get names and colors
names = load_classes(opt.names)
colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
# Run inference
t0 = time.time()
for path, img, im0s, vid_cap in dataset:
t = time.time()
# Get detections
img = torch.from_numpy(img).to(device)
if img.ndimension() == 3:
img = img.unsqueeze(0)
pred = model(img)[0]
if opt.half:
pred = pred.float()
# Apply NMS
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes)
# Apply Classifier
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
print(len(pred))
# Process detections
for i, det in enumerate(pred): # 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 += '%gx%g ' % img.shape[2:] # print string
if det is not None and len(det):
print(len(det))
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += '%g %ss, ' % (n, names[int(c)]) # add to string
# for mutil detections
for i in range(len(det)):
if i <= len(det) - 2:
# print(det[i][0:4])
det[i][0:4] = GBCIOU(det[i][0:4], det[i + 1][0:4], x1y1x2y2=True)[0]
det[i + 1][0:4] = GBCIOU(det[i][0:4], det[i + 1][0:4], x1y1x2y2=True)[1]
else:
pass
# Write results
for *xyxy, conf, cls in det:
if save_txt: # Write to file
savepath = str(Path(out) / Path(p).name).split('.')[0]
with open(savepath + '.txt', 'a') as file:
box_x_min = int(xyxy[0]) # 左上角横坐标
box_y_min = int(xyxy[1]) # 左上角纵坐标
box_x_max = int(xyxy[2]) # 右下角横坐标
box_y_max = int(xyxy[3]) # 右下角纵坐标
# 转成相对位置和宽高#转换成yolov3的标签格式，需要归一化到（0-1）的范围内
x_center = round((box_x_min + box_x_max) / (2 * im0.shape[1]), 4)
y_center = round((box_y_min + box_y_max) / (2 * im0.shape[0]), 4)
width = round((box_x_max - box_x_min) / im0.shape[1], 4)
height = round((box_y_max - box_y_min) / im0.shape[0], 4)
file.write(('%g ' * 5 + '\n') % (0, x_center, y_center, width, height))
if save_img or view_img: # Add bbox to image
label = '%s %.2f' % (names[int(cls)], conf)
# to view of the box detection
#plot_one_box(xyxy, im0, label=label, color=colors[int(cls)])
# to see of the confidence score
#im0=plot_one_cycle(xyxy, im0, label=label, color=colors[int(cls)])
# to save only circle segment after cellyolo
im0 = plot_one_cycle(xyxy, im0, color=colors[int(cls)])
# Print time (inference + NMS)
print('%sDone. (%.3fs)' % (s, time.time() - t))
# Stream results
if view_img:
cv2.imshow(p, im0)
if cv2.waitKey(1) == ord('q'): # q to quit
raise StopIteration
# 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))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', type=str, default='cellyolo/cfg/yolov3-tiny-train.cfg', help='*.cfg path')
parser.add_argument('--names', type=str, default='cellyolo/data/coco.names', help='*.names path')
parser.add_argument('--weights', type=str, default='cellyolo/weights/best.pt', help='path to weights file')
parser.add_argument('--source', type=str, default='cellyolo/5000imagesforretraincellyolo', help='source') # input file/folder, 0 for webcam
parser.add_argument('--output', type=str, default='cellyolo/output', help='output folder') # output folder
parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS')
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', default=0, help='display results')
parser.add_argument('--save-txt', action='store_true', default=0, help='display results')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class')
opt = parser.parse_args()
print(opt)
with torch.no_grad():
detect()