plano_lit / yolo_inference_util.py
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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
@torch.no_grad()
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)