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Highlight the closest target with a different color in predictions #13018

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Add a function to find the nearest target
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50 changes: 34 additions & 16 deletions detect.py
Original file line number Diff line number Diff line change
Expand Up @@ -35,6 +35,7 @@
import sys
from pathlib import Path

import numpy as np
import torch

FILE = Path(__file__).resolve()
Expand Down Expand Up @@ -131,30 +132,20 @@ def run(

# 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))
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
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)
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)
Expand All @@ -167,7 +158,6 @@ def run(

# 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())
Expand All @@ -191,10 +181,37 @@ def write_to_csv(image_name, prediction, confidence):
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))

# Calculate the center of the image
img_center = np.array([im0.shape[1] // 2, im0.shape[0] // 2])
min_distance = None
closest_box = None

if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()

# Calculate the center of the image
img_center = np.array([im0.shape[1] // 2, im0.shape[0] // 2])

# Calculate centers of all detection boxes and find the closest one to the image center
centers = np.array(
[
[(xyxy[0].cpu() + xyxy[2].cpu()) / 2, (xyxy[1].cpu() + xyxy[3].cpu()) / 2]
for *xyxy, _, _ in reversed(det)
]
)
distances = np.linalg.norm(centers - img_center, axis=1)
closest_idx = np.argmin(distances)

# Draw boxes, marking the closest one in green
for j, (*xyxy, conf, cls) in enumerate(reversed(det)):
color = (0, 255, 0) if j == closest_idx else colors(int(cls), True)
annotator.box_label(xyxy, f"{names[int(cls)]} {conf:.2f}", color=color)

# 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
Expand Down Expand Up @@ -266,7 +283,6 @@ def write_to_csv(image_name, prediction, confidence):


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)")
Expand Down Expand Up @@ -303,11 +319,13 @@ def parse_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))


# python detect.py --weights runs/train/exp10/weights/best.pt --source project/test
# python detect.py --weights runs/train/exp10/weights/best.pt --source project/test

if __name__ == "__main__":
opt = parse_opt()
main(opt)
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