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# YOLOv5 π by Ultralytics, GPL-3.0 license | |
""" | |
Run inference on images, videos, directories, streams, etc. | |
Usage: | |
$ python path/to/detect.py --weights yolov5s.pt --source 0 # webcam | |
img.jpg # image | |
vid.mp4 # video | |
path/ # directory | |
path/*.jpg # glob | |
'https://youtu.be/Zgi9g1ksQHc' # YouTube | |
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream | |
""" | |
import argparse | |
import os | |
import platform | |
import sys | |
from pathlib import Path | |
import cv2 | |
import numpy as np | |
import torch | |
import torch.backends.cudnn as cudnn | |
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 models.experimental import attempt_load | |
from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams | |
from utils.general import (LOGGER, apply_classifier, check_file, check_img_size, check_imshow, check_requirements, | |
check_suffix, colorstr, increment_path, non_max_suppression, print_args, scale_coords, | |
strip_optimizer, xyxy2xywh) | |
from utils.plots import Annotator, colors | |
from utils.torch_utils import load_classifier, select_device, time_sync | |
import yaml | |
def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s) | |
source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam | |
imgsz=640, # inference size (pixels) | |
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_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 | |
): | |
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('.txt') or (is_url and not is_file) | |
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 | |
# Initialize | |
device = select_device(device) | |
half &= device.type != 'cpu' # half precision only supported on CUDA | |
# Load model | |
w = str(weights[0] if isinstance(weights, list) else weights) | |
classify, suffix, suffixes = False, Path(w).suffix.lower(), ['.pt', '.onnx', '.tflite', '.pb', ''] | |
check_suffix(w, suffixes) # check weights have acceptable suffix | |
pt, onnx, tflite, pb, saved_model = (suffix == x for x in suffixes) # backend booleans | |
stride, names = 64, [f'class{i}' for i in range(1000)] # assign defaults | |
if pt: | |
model = torch.jit.load(w) if 'torchscript' in w else attempt_load(weights, map_location=device) | |
stride = int(model.stride.max()) # model stride | |
names = model.module.names if hasattr(model, 'module') else model.names # get class names | |
if half: | |
model.half() # to FP16 | |
if classify: # second-stage classifier | |
modelc = load_classifier(name='resnet50', n=2) # initialize | |
modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval() | |
elif onnx: | |
if dnn: | |
check_requirements(('opencv-python>=4.5.4',)) | |
net = cv2.dnn.readNetFromONNX(w) | |
else: | |
check_requirements(('onnx', 'onnxruntime-gpu' if torch.has_cuda else 'onnxruntime')) | |
import onnxruntime | |
session = onnxruntime.InferenceSession(w, None) | |
else: # TensorFlow models | |
import tensorflow as tf | |
if pb: # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt | |
def wrap_frozen_graph(gd, inputs, outputs): | |
x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped import | |
return x.prune(tf.nest.map_structure(x.graph.as_graph_element, inputs), | |
tf.nest.map_structure(x.graph.as_graph_element, outputs)) | |
graph_def = tf.Graph().as_graph_def() | |
graph_def.ParseFromString(open(w, 'rb').read()) | |
frozen_func = wrap_frozen_graph(gd=graph_def, inputs="x:0", outputs="Identity:0") | |
elif saved_model: | |
model = tf.keras.models.load_model(w) | |
elif tflite: | |
if "edgetpu" in w: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python | |
import tflite_runtime.interpreter as tflri | |
delegate = {'Linux': 'libedgetpu.so.1', # install libedgetpu https://coral.ai/software/#edgetpu-runtime | |
'Darwin': 'libedgetpu.1.dylib', | |
'Windows': 'edgetpu.dll'}[platform.system()] | |
interpreter = tflri.Interpreter(model_path=w, experimental_delegates=[tflri.load_delegate(delegate)]) | |
else: | |
interpreter = tf.lite.Interpreter(model_path=w) # load TFLite model | |
interpreter.allocate_tensors() # allocate | |
input_details = interpreter.get_input_details() # inputs | |
output_details = interpreter.get_output_details() # outputs | |
int8 = input_details[0]['dtype'] == np.uint8 # is TFLite quantized uint8 model | |
imgsz = check_img_size(imgsz, s=stride) # check image size | |
# Dataloader | |
if webcam: | |
view_img = check_imshow() | |
cudnn.benchmark = True # set True to speed up constant image size inference | |
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt) | |
bs = len(dataset) # batch_size | |
else: | |
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt) | |
bs = 1 # batch_size | |
vid_path, vid_writer = [None] * bs, [None] * bs | |
# Run inference | |
if pt and device.type != 'cpu': | |
model(torch.zeros(1, 3,imgsz,imgsz).to(device).type_as(next(model.parameters()))) # run once | |
dt, seen = [0.0, 0.0, 0.0], 0 | |
counter = -1 | |
data = {} | |
for path, img, im0s, vid_cap, s in dataset: | |
counter += 1 | |
coordinates = list() | |
t1 = time_sync() | |
if onnx: | |
img = img.astype('float32') | |
else: | |
img = torch.from_numpy(img).to(device) | |
img = img.half() if half else img.float() # uint8 to fp16/32 | |
img /= 255 # 0 - 255 to 0.0 - 1.0 | |
if len(img.shape) == 3: | |
img = img[None] # expand for batch dim | |
t2 = time_sync() | |
dt[0] += t2 - t1 | |
# Inference | |
if pt: | |
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False | |
pred = model(img, augment=augment, visualize=visualize)[0] | |
elif onnx: | |
if dnn: | |
net.setInput(img) | |
pred = torch.tensor(net.forward()) | |
else: | |
pred = torch.tensor(session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: img})) | |
else: # tensorflow model (tflite, pb, saved_model) | |
imn = img.permute(0, 2, 3, 1).cpu().numpy() # image in numpy | |
if pb: | |
pred = frozen_func(x=tf.constant(imn)).numpy() | |
elif saved_model: | |
pred = model(imn, training=False).numpy() | |
elif tflite: | |
if int8: | |
scale, zero_point = input_details[0]['quantization'] | |
imn = (imn / scale + zero_point).astype(np.uint8) # de-scale | |
interpreter.set_tensor(input_details[0]['index'], imn) | |
interpreter.invoke() | |
pred = interpreter.get_tensor(output_details[0]['index']) | |
if int8: | |
scale, zero_point = output_details[0]['quantization'] | |
pred = (pred.astype(np.float32) - zero_point) * scale # re-scale | |
pred[..., 0] *= imgsz[1] # x | |
pred[..., 1] *= imgsz[0] # y | |
pred[..., 2] *= imgsz[1] # w | |
pred[..., 3] *= imgsz[0] # h | |
pred = torch.tensor(pred) | |
t3 = time_sync() | |
dt[1] += t3 - t2 | |
# NMS | |
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) | |
dt[2] += time_sync() - t3 | |
# Second-stage classifier (optional) | |
if classify: | |
pred = apply_classifier(pred, modelc, img, im0s) | |
# 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) # img.jpg | |
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt | |
s += '%gx%g ' % img.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_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 += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string | |
# Write results | |
for *xyxy, conf, cls in reversed(det): | |
if save_txt: # Write to file | |
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh | |
coordinates.append(xywh) | |
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format | |
with open(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)) | |
data.update({f"img{counter}": {"name": p.name, "coordinates": coordinates}}) | |
# Print time (inference-only) | |
LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)') | |
# Stream results | |
im0 = annotator.result() | |
if view_img: | |
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 += '.mp4' | |
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) | |
vid_writer[i].write(im0) | |
import json | |
with open('test.json', 'w', encoding='utf-8') as f: | |
json.dump(data, f, ensure_ascii=False, indent=4) | |
# Print results | |
t = tuple(x / 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) # update model (to fix SourceChangeWarning) | |
def parse_opt(): | |
parser = argparse.ArgumentParser() | |
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)') | |
parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam') | |
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-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') | |
opt = parser.parse_args() | |
params = None | |
with open("params.yaml", 'r') as fd: | |
params = yaml.safe_load(fd) | |
opt.imgsz = params['test']['image_size'] | |
opt.conf_thres = params['test']['conf'] | |
opt.imgsz *= 2 if len(str(opt.imgsz)) == 1 else 1 # expand | |
print_args(FILE.stem, opt) | |
return opt | |
def main(opt): | |
check_requirements(exclude=('tensorboard', 'thop')) | |
run(**vars(opt)) | |
if __name__ == "__main__": | |
opt = parse_opt() | |
main(opt) |