# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
"""
Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
Usage - sources:
$ python detect.py --weights yolov5s.pt --source 0 # webcam
img.jpg # image
vid.mp4 # video
screen # screenshot
path/ # directory
list.txt # list of images
list.streams # list of streams
'path/*.jpg' # glob
'https://youtu.be/LNwODJXcvt4' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
Usage - formats:
$ python detect.py --weights yolov5s.pt # PyTorch
yolov5s.torchscript # TorchScript
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov5s_openvino_model # OpenVINO
yolov5s.engine # TensorRT
yolov5s.mlmodel # CoreML (macOS-only)
yolov5s_saved_model # TensorFlow SavedModel
yolov5s.pb # TensorFlow GraphDef
yolov5s.tflite # TensorFlow Lite
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
yolov5s_paddle_model # PaddlePaddle
"""
import argparse
import csv
import os
import platform
import sys
from pathlib import Path
import torch
import copy
import torch.nn.functional as F
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
from ultralytics.utils.plotting import Annotator, colors, save_one_box
from models.common import DetectMultiBackend
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh,get_fixed_xyxy)
from utils.torch_utils import select_device, smart_inference_mode
from utils.my_model import MyCNN
from torchvision.ops import roi_align
@smart_inference_mode()
def run(
weights=ROOT / "yolov5s.pt", # model path or triton URL
source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam)
data=ROOT / "data/coco128.yaml", # dataset.yaml path
imgsz=(640, 640), # inference size (height, width)
conf_thres=0.25, # confidence threshold
iou_thres=0.45, # NMS IOU threshold
max_det=1000, # maximum detections per image
device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
view_img=False, # show results
save_txt=False, # save results to *.txt
save_csv=False, # save results in CSV format
save_conf=False, # save confidences in --save-txt labels
save_crop=False, # save cropped prediction boxes
nosave=False, # do not save images/videos
classes=None, # filter by class: --class 0, or --class 0 2 3
agnostic_nms=False, # class-agnostic NMS
augment=False, # augmented inference
visualize=False, # visualize features
update=False, # update all models
project=ROOT / "runs/detect", # save results to project/name
name="exp", # save results to project/name
exist_ok=False, # existing project/name ok, do not increment
line_thickness=3, # bounding box thickness (pixels)
hide_labels=False, # hide labels
hide_conf=False, # hide confidences
half=False, # use FP16 half-precision inference
dnn=False, # use OpenCV DNN for ONNX inference
vid_stride=1, # video frame-rate stride
):
source = str(source)
save_img = not nosave and not source.endswith('.txt') # save inference images
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
screenshot = source.lower().startswith('screen')
if is_url and is_file:
source = check_file(source) # download
# Directories
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Load model
device = select_device(device)
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
stride, names, pt = model.stride, model.names, model.pt
# stride = 16
imgsz = check_img_size(imgsz, s=stride) # check image size
# Dataloader
bs = 1 # batch_size
if webcam:
view_img = check_imshow(warn=True)
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
bs = len(dataset)
elif screenshot:
dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
else:
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=False, vid_stride=vid_stride)
vid_path, vid_writer = [None] * bs, [None] * bs
# Run inference
model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup
seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device))
for path, im, im0s, vid_cap, s, orig_img in dataset:
with dt[0]:
im = torch.from_numpy(im).to(model.device)
im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
if len(im.shape) == 3:
im = im[None] # expand for batch dim
if model.xml and im.shape[0] > 1:
ims = torch.chunk(im, im.shape[0], 0)
# Inference
with dt[1]:
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
if model.xml and im.shape[0] > 1:
pred = None
for image in ims:
if pred is None:
pred,int_feats = model(image, augment=augment, visualize=visualize).unsqueeze(0)
else:
pred, int_feats = torch.cat((pred, model(image, augment=augment, visualize=visualize).unsqueeze(0)), dim=0)
pred = [pred, None]
else:
pred,int_feats = model(im, augment=augment, visualize=visualize)
# NMS
with dt[2]:
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
# int_feats_p3= int_feats[0][0,:,:,:].to(torch.float32)
# int_feats_p3 = int_feats_p3.unsqueeze(0)#.unsqueeze(0)
int_feats_p2 = int_feats[0][0].to(torch.float32).unsqueeze(0)
int_feats_p3 = int_feats[1][0].to(torch.float32).unsqueeze(0)
# concat_feat = torch.cat([int_feats_p2,int_feats_p3],dim=1)
in_channels = int_feats_p2.shape[1]+int_feats_p3.shape[1]
cell_attribute_model= MyCNN(num_classes=12, dropout_prob=0.5, in_channels=in_channels).to(device)
folder_name = 'data/WBC_dataset_sample/Attribute_model'
custom_weights_path = f"{folder_name}/last_weights.pth"
custom_weights = torch.load(custom_weights_path)
cell_attribute_model.load_state_dict(custom_weights)
cell_attribute_model.eval().to(device)
# int_feats_p5= int_feats[1][0,:,:,:].to(torch.float32)
# int_feats_p5 = int_feats_p5.unsqueeze(0)#.unsqueeze(0)
torch.cuda.empty_cache()
# del int_feats
# resized_int_feats_p5 = F.interpolate(int_feats_p5, size=(int_feats[0].size(2), int_feats[0].size(3)), mode='bilinear', align_corners=False)
# concatenated_features = torch.cat([resized_int_feats_p5,int_feats_p3],dim=1)
if (len(pred)>0):
all_top_indices_cell_pred = []
top_indices_cell_pred = []
pred_Nuclear_Chromatin_array = []
pred_Nuclear_Shape_array = []
pred_Nucleus_array = []
pred_Cytoplasm_array = []
pred_Cytoplasmic_Basophilia_array = []
pred_Cytoplasmic_Vacuoles_array = []
for i in range(len(pred[0])):
if pred[0][i].numel() > 0: # Check if the tensor is not empty
pred_tensor = pred[0][i][0:4]
if pred[0][i][5] != 0:
img_shape_tensor = torch.tensor([im.shape[2], im.shape[3],im.shape[2],im.shape[3]]).to(device)
normalized_xyxy=pred_tensor / img_shape_tensor
p2_feature_shape_tensor = torch.tensor([int_feats[0].shape[1], int_feats[0].shape[2],int_feats[0].shape[1],int_feats[0].shape[2]]).to(device) # reduce_channels_layer = torch.nn.Conv2d(1280, 250, kernel_size=1).to(device)
p3_feature_shape_tensor = torch.tensor([int_feats[1].shape[1], int_feats[1].shape[2],int_feats[1].shape[1],int_feats[1].shape[2]]).to(device) # reduce_channels_layer = torch.nn.Conv2d(1280, 250, kernel_size=1).to(device)
p2_normalized_xyxy = normalized_xyxy*p2_feature_shape_tensor
p3_normalized_xyxy = normalized_xyxy*p3_feature_shape_tensor
p2_x_min, p2_y_min, p2_x_max, p2_y_max = get_fixed_xyxy(p2_normalized_xyxy,int_feats_p2)
p3_x_min, p3_y_min, p3_x_max, p3_y_max = get_fixed_xyxy(p3_normalized_xyxy,int_feats_p3)
p2_roi = torch.tensor([p2_x_min, p2_y_min, p2_x_max, p2_y_max], device=device).float()
p3_roi = torch.tensor([p3_x_min, p3_y_min, p3_x_max, p3_y_max], device=device).float()
batch_index = torch.tensor([0], dtype=torch.float32, device = device)
# Concatenate the batch index to the bounding box coordinates
p2_roi_with_batch_index = torch.cat([batch_index, p2_roi])
p3_roi_with_batch_index = torch.cat([batch_index, p3_roi])
p2_resized_object = roi_align(int_feats_p2, p2_roi_with_batch_index.unsqueeze(0).to(device), output_size=(24, 30))
p3_resized_object = roi_align(int_feats_p3, p3_roi_with_batch_index.unsqueeze(0).to(device), output_size=(24, 30))
concat_box = torch.cat([p2_resized_object,p3_resized_object],dim=1)
output_cell_prediction= cell_attribute_model(concat_box)
output_cell_prediction_prob = F.softmax(output_cell_prediction.view(6,2), dim=1)
top_indices_cell_pred = torch.argmax(output_cell_prediction_prob, dim=1)
pred_Nuclear_Chromatin_array.append(top_indices_cell_pred[0].item())
pred_Nuclear_Shape_array.append(top_indices_cell_pred[1].item())
pred_Nucleus_array.append(top_indices_cell_pred[2].item())
pred_Cytoplasm_array.append(top_indices_cell_pred[3].item())
pred_Cytoplasmic_Basophilia_array.append(top_indices_cell_pred[4].item())
pred_Cytoplasmic_Vacuoles_array.append(top_indices_cell_pred[5].item())
# all_top_indices_cell_pred.append(top_indices_cell_pred.item())
else:
# top_indices_cell_pred = torch.tensor([0,0,0,0,0,0]).to(device)
pred_Nuclear_Chromatin_array.append(0)
pred_Nuclear_Shape_array.append(0)
pred_Nucleus_array.append(0)
pred_Cytoplasm_array.append(0)
pred_Cytoplasmic_Basophilia_array.append(0)
pred_Cytoplasmic_Vacuoles_array.append(0)
# Second-stage classifier (optional)
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
# Define the path for the CSV file
csv_path = save_dir / 'predictions.csv'
# # Create or append to the CSV file
# def write_to_csv(name, predicts, confid,pred_NC,pred_NS,
# pred_N,pred_C,pred_CB,
# pred_CV,x_min,y_min,x_max,y_max):
# data = {'Image Name': name, 'Prediction': predicts, 'Confidence': confid, 'Nuclear Chromatin':pred_NC,
# 'Nuclear Shape':pred_NS,'Nucleus':pred_N,'Cytoplasm':pred_C,
# 'Cytoplasmic Basophilia': pred_CB, 'Cytoplasmic Vacuoles': pred_CV,
# 'x_min':x_min,'y_min':y_min,'x_max':x_max,'y_max':y_max}
# with open(csv_path, mode='a', newline='') as f:
# writer = csv.DictWriter(f, fieldnames=data.keys())
# if not csv_path.is_file():
# writer.writeheader()
# writer.writerow(data)
# Create or append to the CSV file
def write_to_csv(name, predicts, confid, pred_NC, pred_NS,
pred_N, pred_C, pred_CB, pred_CV,
x_min, y_min, x_max, y_max):
data = {'Image Name': name, 'Prediction': predicts, 'Confidence': confid, 'Nuclear Chromatin': pred_NC,
'Nuclear Shape': pred_NS, 'Nucleus': pred_N, 'Cytoplasm': pred_C,
'Cytoplasmic Basophilia': pred_CB, 'Cytoplasmic Vacuoles': pred_CV,
'x_min': x_min, 'y_min': y_min, 'x_max': x_max, 'y_max': y_max}
# Check if the CSV file exists
if not os.path.isfile(csv_path):
with open(csv_path, mode='w', newline='') as f:
writer = csv.DictWriter(f, fieldnames=data.keys())
writer.writeheader()
# Append data to CSV file
with open(csv_path, mode='a', newline='') as f:
writer = csv.DictWriter(f, fieldnames=data.keys())
writer.writerow(data)
# Process predictions
for i, det in enumerate(pred): # per image
seen += 1
if webcam: # batch_size >= 1
p, im0, frame = path[i], im0s[i].copy(), dataset.count
s += f'{i}: '
else:
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
p = Path(p) # to Path
save_path = str(save_dir / p.name) # im.jpg
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt
s += '%gx%g ' % im.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
imc = im0.copy() if save_crop else im0 # for save_crop
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
# Print results
for c in det[:, 5].unique():
n = (det[:, 5] == c).sum() # detections per class
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
# Write results
for count, (*xyxy, conf, cls) in enumerate(det):
c = int(cls) # integer class
label = names[c] if hide_conf else f'{names[c]}'
confidence = float(conf)
confidence_str = f'{confidence:.2f}'
if save_csv:
x_min,y_min,x_max,y_max = xyxy
# Scaling factors
scale_width = orig_img.shape[1] / 640
scale_height = orig_img.shape[0] / 640
# Convert bounding box coordinates to 800x448 image
x_min_new = int(x_min * scale_width)
y_min_new = int(y_min * scale_height)
x_max_new = int(x_max * scale_width)
y_max_new = int(y_max * scale_height)
write_to_csv(p.name, label, confidence_str,
pred_Nuclear_Chromatin_array[count],pred_Nuclear_Shape_array[count],
pred_Nucleus_array[count],pred_Cytoplasm_array[count],pred_Cytoplasmic_Basophilia_array[count],
pred_Cytoplasmic_Vacuoles_array[count],
int(x_min_new),int(y_min_new),
int(x_max_new),int(y_max_new))
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
with open(f'{txt_path}.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or save_crop or view_img: # Add bbox to image
c = int(cls) # integer class
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
annotator.box_label(xyxy, label, color=colors(c, True))
# annotator.my_box_label(xyxy, label, color=colors(c, True), att1=pred_Nuclear_Chromatin_array[0],
# att2 = pred_Nuclear_Shape_array[0], att3 = pred_Nucleus_array[0],
# att4 = pred_Cytoplasm_array[0], att5 = pred_Cytoplasmic_Basophilia_array[0],
# att6 = pred_Cytoplasmic_Vacuoles_array[0]
# )
if save_crop:
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
# Stream results
im0 = annotator.result()
if view_img:
if platform.system() == 'Linux' and p not in windows:
windows.append(p)
cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
cv2.imshow(str(p), im0)
cv2.waitKey(1) # 1 millisecond
# Save results (image with detections)
if save_img:
if dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else: # 'video' or 'stream'
if vid_path[i] != save_path: # new video
vid_path[i] = save_path
if isinstance(vid_writer[i], cv2.VideoWriter):
vid_writer[i].release() # release previous video writer
if vid_cap: # video
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))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
vid_writer[i].write(im0)
# Print time (inference-only)
LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
# Print results
t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
if save_txt or save_img:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
if update:
strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'runs/train/yolov5x_300Epochs_training/weights/best.pt', help='model path or triton URL')
parser.add_argument('--source', type=str, default='/home/iml/Desktop/bc_experiment/HCM_V3/HCM_840_attribute/images/test/', help='file/dir/URL/glob/screen/0(webcam)')
parser.add_argument('--data', type=str, default=ROOT / 'data/WBC_v1.yaml', help='(optional) dataset.yaml path')
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', action='store_true', help='show results')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-csv', action='store_true', help='save results in CSV format')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--visualize', action='store_true', help='visualize features')
parser.add_argument('--update', action='store_true', help='update all models')
parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
parser.add_argument('--name', default='exp', help='save results to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
opt = parser.parse_args()
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
print_args(vars(opt))
return opt
def main(opt):
check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
run(**vars(opt))
if __name__ == '__main__':
opt = parse_opt()
main(opt)
Datasets
Models
