add: detectModif
Gilbert Zaini
parent
0797106a98
commit
3b02e75ee0
|
|
@ -0,0 +1,351 @@
|
|||
# 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
|
||||
from flask import jsonify
|
||||
import cv2
|
||||
import base64
|
||||
import numpy as np
|
||||
from io import BytesIO
|
||||
from PIL import Image
|
||||
import json
|
||||
|
||||
import torch
|
||||
|
||||
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,
|
||||
)
|
||||
from utils.torch_utils import select_device, smart_inference_mode
|
||||
|
||||
|
||||
@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
|
||||
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=pt, 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))
|
||||
total_detections = 0
|
||||
for path, im, im0s, vid_cap, s 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 = model(image, augment=augment, visualize=visualize).unsqueeze(0)
|
||||
else:
|
||||
pred = torch.cat((pred, model(image, augment=augment, visualize=visualize).unsqueeze(0)), dim=0)
|
||||
pred = [pred, None]
|
||||
else:
|
||||
pred = 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)
|
||||
|
||||
# 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(image_name, prediction, confidence):
|
||||
"""Writes prediction data for an image to a CSV file, appending if the file exists."""
|
||||
data = {"Image Name": image_name, "Prediction": prediction, "Confidence": confidence}
|
||||
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)
|
||||
|
||||
# Process predictions
|
||||
for i, det in enumerate(pred): # per image
|
||||
seen += 1
|
||||
if len(det):
|
||||
total_detections += len(det)
|
||||
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 *xyxy, conf, cls in reversed(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:
|
||||
write_to_csv(p.name, label, confidence_str)
|
||||
|
||||
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))
|
||||
if save_crop:
|
||||
save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True)
|
||||
|
||||
# Stream results
|
||||
# cv2.putText(im0, f'Total detected objects: {total_detections}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
|
||||
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)
|
||||
|
||||
LOGGER.info(f"Total detected objects: {total_detections}")
|
||||
|
||||
pil_image = Image.fromarray(cv2.cvtColor(im0, cv2.COLOR_BGR2RGB))
|
||||
|
||||
# Convert the PIL Image to bytes
|
||||
buffered = BytesIO()
|
||||
pil_image.save(buffered, format="PNG")
|
||||
|
||||
# Encode the bytes to base64
|
||||
img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
|
||||
|
||||
# res = jsonify({
|
||||
# "total": total_detections,
|
||||
# "image": img_str
|
||||
# })
|
||||
# return res
|
||||
# Replace jsonify with a simple dictionary
|
||||
|
||||
res = {
|
||||
"total": total_detections,
|
||||
"image": img_str
|
||||
}
|
||||
|
||||
# Write the result to a file
|
||||
# with open(str(save_dir / "result.json"), "w") as f:
|
||||
# json.dump(res, f)
|
||||
|
||||
print(json.dumps(res)) # Print JSON to standard output
|
||||
|
||||
def parse_opt():
|
||||
"""Parses command-line arguments for YOLOv5 detection, setting inference options and model configurations."""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model path or triton URL")
|
||||
parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)")
|
||||
parser.add_argument("--data", type=str, default=ROOT / "data/coco128.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):
|
||||
"""Executes YOLOv5 model inference with given options, checking requirements before running the model."""
|
||||
check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
|
||||
run(**vars(opt))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
Loading…
Reference in New Issue