Spaces:
Runtime error
Runtime error
import argparse | |
import sys | |
from pathlib import Path | |
import cv2 | |
import numpy as np | |
import torch | |
import torch.backends.cudnn as cudnn | |
from models.experimental import attempt_load | |
from utils.datasets import LoadImages, LoadStreams | |
from utils.general import ( | |
apply_classifier, | |
check_img_size, | |
check_imshow, | |
check_requirements, | |
check_suffix, | |
colorstr, | |
increment_path, | |
is_ascii, | |
non_max_suppression, | |
save_one_box, | |
scale_coords, | |
set_logging, | |
strip_optimizer, | |
xyxy2xywh, | |
) | |
from utils.plots import Annotator, colors | |
from utils.torch_utils import load_classifier, select_device, time_sync | |
# 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 | |
def run_yolo_v5( | |
weights="yolov5s.pt", # model.pt path(s) | |
source="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="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 | |
): | |
save_img = not nosave and not source.endswith( | |
".txt" | |
) # save inference images | |
webcam = ( | |
source.isnumeric() | |
or source.endswith(".txt") | |
or source.lower().startswith( | |
("rtsp://", "rtmp://", "http://", "https://") | |
) | |
) | |
# 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 | |
set_logging() | |
device = select_device(device) | |
half &= device.type != "cpu" # half precision only supported on CUDA | |
# Load model | |
w = 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 = attempt_load(weights, map_location=device) # load FP32 model | |
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: | |
check_requirements(("onnx", "onnxruntime")) | |
import onnxruntime | |
session = onnxruntime.InferenceSession(w, None) | |
else: # TensorFlow models | |
check_requirements(("tensorflow>=2.4.1",)) | |
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: | |
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 | |
ascii = is_ascii(names) # names are ascii (use PIL for UTF-8) | |
# Dataloader | |
print("Loading data from the source", source) | |
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) | |
.to(device) | |
.type_as(next(model.parameters())) | |
) # run once | |
dt, seen = [0.0, 0.0, 0.0], 0 | |
results = [] | |
for path, img, im0s, vid_cap in dataset: | |
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 = img / 255.0 # 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: | |
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, s, im0, frame = ( | |
path[i], | |
f"{i}: ", | |
im0s[i].copy(), | |
dataset.count, | |
) | |
else: | |
p, s, 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, pil=not ascii | |
) | |
if len(det): | |
# Rescale boxes from img_size to im0 size | |
det[:, :4] = scale_coords( | |
img.shape[2:], det[:, :4], im0.shape | |
).round() | |
results.append((im0, det)) | |
# 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 | |
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)) | |
if save_crop: | |
save_one_box( | |
xyxy, | |
imc, | |
file=save_dir | |
/ "crops" | |
/ names[c] | |
/ f"{p.stem}.jpg", | |
BGR=True, | |
) | |
# Print time (inference-only) | |
print(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) | |
# Print results | |
t = tuple(x / seen * 1e3 for x in dt) # speeds per image | |
print( | |
f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" | |
% t | |
) | |
return results | |
# 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 '' | |
# print(f"Results saved to {colorstr('bold', save_dir)}{s}") | |
# if update: | |
# strip_optimizer(weights) # update model (to fix SourceChangeWarning) | |