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ml_pothole_detection_down.py
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# Modified version to save label location in pothole.csv
"""
Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
"""
import os
import platform
import sys
from pathlib import Path
import numpy as np
import torch
import pandas as pd
# Initializa parameters and flags
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.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.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, smart_inference_mode
@smart_inference_mode()
def detect_pothole(
weights=ROOT / 'vTwo.pt', # model path or triton URL
source= 3, # file/dir/URL/glob/screen/0(webcam)
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
save_txt=True, # save results to *.txt
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
project=ROOT / '', # 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
vid_stride=1, # video frame-rate stride
):
source = str(source)
# 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
txt_path = str(ROOT / Path('pothole_detected%s.csv'% source))
print("text path at:", txt_path)
# f = open(txt_path, "w")
# f.write("0,0,0,0")
# f.close()
# Load model
device = select_device(device)
model = DetectMultiBackend(weights)
stride, names, pt = model.stride, model.names, model.pt
imgsz = check_img_size(imgsz, s=stride) # check image size
# Dataloader
bs = 1 # batch_size
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
bs = len(dataset)
# initialize data
xywh = pd.DataFrame([[0,0,0,0]], columns =['x', 'y', 'w','h'])
xywh_pd = pd.DataFrame(xywh)
xywh_pd.to_csv(txt_path, float_format = '%g', header = False, index = False)
# Run inference
model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup
seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
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
# Inference
with dt[1]:
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
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)
# Process predictions
for i, det in enumerate(pred): # per image
seen += 1
p, im0, frame = path[i], im0s[i].copy(), dataset.count
s += f'{i}: '
p = Path(p) # to Path
s += '%gx%g ' % im.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
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
# f = open(txt_path, 'w')
for *xyxy, conf, cls in reversed(det):
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
# Write to file
# width = abs(xyxy[2] - xyxy[0])
# height = abs(xyxy[3] - xyxy[1])
# xCenter = (xyxy[2] - xyxy[0])//2
# yCenter = (xyxy[3] - xyxy[1])//2
xCenter = xywh[0]
yCenter = xywh[1]
xywh_new = [[xywh[0], xywh[1], xywh[2], xywh[3]]]
xywh_pd = pd.DataFrame(xywh_new, columns =['x', 'y', 'w','h'])
if xywh[2]*xywh[3] < 0.25:
xywh_pd = pd.DataFrame([[0,0,0,0]],columns =['x', 'y', 'w','h'])
print("Pothole Too Small. Area %f"%(xywh[2]*xywh[3]))
xywh_pd.to_csv(txt_path, float_format = '%g',mode="a", header = False, index = False)
# f.write("%i, %i, %i, %i"%(xCenter, yCenter, xywh[2], xywh[3]))
# line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
# f.write(('%g ' * len(line)).rstrip() % line + '\n')
# 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))
else:
xywh = pd.DataFrame([[0,0,0,0]],columns =['x', 'y', 'w','h'])
xywh_pd = pd.DataFrame(xywh, columns =['x', 'y', 'w','h'])
xywh_pd.to_csv(txt_path, float_format = '%g', mode="a", header = False, index = False)
# f.close()
# if not detected, clear file
# else:
# f = open(txt_path, "w")
# f.write("0, 0, 0, 0")
# f.close()
# Stream result
im0 = annotator.result()
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
# Print time (inference-only)
if len(det):
LOGGER.info(f"{s}{len(det)} at x={xCenter} y={yCenter} Time{dt[1].dt * 1E3:.1f}ms")
return xywh
model = ROOT / 'vTwo.pt' # Jaime's more robust? version
# model = ROOT / 'v3.pt'
# model = ROOT / 'PotholeYolo5s.pt' # Ivy's Initial Model
webcam = 2
detect_pothole(weights = model, source = webcam)