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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
import math
import os.path as osp
import cv2
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from PIL import Image, ImageFont
from yolov6.data.data_augment import letterbox
from yolov6.layers.common import DetectBackend
from yolov6.utils.events import LOGGER, load_yaml
from yolov6.utils.nms import non_max_suppression
class Inferer:
def __init__(self, model_id, device="cpu", yaml="coco.yaml", img_size=640, half=False):
self.__dict__.update(locals())
# Init model
self.img_size = img_size
cuda = device != "cpu" and torch.cuda.is_available()
self.device = torch.device("cuda:0" if cuda else "cpu")
self.model = DetectBackend(hf_hub_download(model_id, "model.pt"), device=self.device)
self.stride = self.model.stride
self.class_names = load_yaml(yaml)["names"]
self.img_size = self.check_img_size(self.img_size, s=self.stride) # check image size
# Half precision
if half & (self.device.type != "cpu"):
self.model.model.half()
else:
self.model.model.float()
half = False
if self.device.type != "cpu":
self.model(
torch.zeros(1, 3, *self.img_size).to(self.device).type_as(next(self.model.model.parameters()))
) # warmup
# Switch model to deploy status
self.model_switch(self.model, self.img_size)
def model_switch(self, model, img_size):
"""Model switch to deploy status"""
from yolov6.layers.common import RepVGGBlock
for layer in model.modules():
if isinstance(layer, RepVGGBlock):
layer.switch_to_deploy()
LOGGER.info("Switch model to deploy modality.")
def __call__(
self,
path_or_image,
conf_thres=0.25,
iou_thres=0.45,
classes=None,
agnostic_nms=False,
max_det=1000,
hide_labels=False,
hide_conf=False,
):
"""Model Inference and results visualization"""
img, img_src = self.precess_image(path_or_image, self.img_size, self.stride, self.half)
img = img.to(self.device)
if len(img.shape) == 3:
img = img[None]
# expand for batch dim
pred_results = self.model(img)
det = non_max_suppression(pred_results, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)[0]
gn = torch.tensor(img_src.shape)[[1, 0, 1, 0]] # normalization gain whwh
img_ori = img_src
# check image and font
assert (
img_ori.data.contiguous
), "Image needs to be contiguous. Please apply to input images with np.ascontiguousarray(im)."
self.font_check()
if len(det):
det[:, :4] = self.rescale(img.shape[2:], det[:, :4], img_src.shape).round()
for *xyxy, conf, cls in reversed(det):
class_num = int(cls) # integer class
label = (
None
if hide_labels
else (self.class_names[class_num] if hide_conf else f"{self.class_names[class_num]} {conf:.2f}")
)
self.plot_box_and_label(
img_ori,
max(round(sum(img_ori.shape) / 2 * 0.003), 2),
xyxy,
label,
color=self.generate_colors(class_num, True),
)
img_src = np.asarray(img_ori)
return img_src
@staticmethod
def precess_image(path_or_image, img_size, stride, half):
"""Process image before image inference."""
if isinstance(path_or_image, str):
try:
img_src = cv2.imread(path_or_image)
assert img_src is not None, f"Invalid image: {path_or_image}"
except Exception as e:
LOGGER.warning(e)
elif isinstance(path_or_image, np.ndarray):
img_src = path_or_image
elif isinstance(path_or_image, Image.Image):
img_src = np.array(path_or_image)
image = letterbox(img_src, img_size, stride=stride)[0]
# Convert
image = image.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
image = torch.from_numpy(np.ascontiguousarray(image))
image = image.half() if half else image.float() # uint8 to fp16/32
image /= 255 # 0 - 255 to 0.0 - 1.0
return image, img_src
@staticmethod
def rescale(ori_shape, boxes, target_shape):
"""Rescale the output to the original image shape"""
ratio = min(ori_shape[0] / target_shape[0], ori_shape[1] / target_shape[1])
padding = (ori_shape[1] - target_shape[1] * ratio) / 2, (ori_shape[0] - target_shape[0] * ratio) / 2
boxes[:, [0, 2]] -= padding[0]
boxes[:, [1, 3]] -= padding[1]
boxes[:, :4] /= ratio
boxes[:, 0].clamp_(0, target_shape[1]) # x1
boxes[:, 1].clamp_(0, target_shape[0]) # y1
boxes[:, 2].clamp_(0, target_shape[1]) # x2
boxes[:, 3].clamp_(0, target_shape[0]) # y2
return boxes
def check_img_size(self, img_size, s=32, floor=0):
"""Make sure image size is a multiple of stride s in each dimension, and return a new shape list of image."""
if isinstance(img_size, int): # integer i.e. img_size=640
new_size = max(self.make_divisible(img_size, int(s)), floor)
elif isinstance(img_size, list): # list i.e. img_size=[640, 480]
new_size = [max(self.make_divisible(x, int(s)), floor) for x in img_size]
else:
raise Exception(f"Unsupported type of img_size: {type(img_size)}")
if new_size != img_size:
print(f"WARNING: --img-size {img_size} must be multiple of max stride {s}, updating to {new_size}")
return new_size if isinstance(img_size, list) else [new_size] * 2
def make_divisible(self, x, divisor):
# Upward revision the value x to make it evenly divisible by the divisor.
return math.ceil(x / divisor) * divisor
@staticmethod
def plot_box_and_label(image, lw, box, label="", color=(128, 128, 128), txt_color=(255, 255, 255)):
# Add one xyxy box to image with label
p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
cv2.rectangle(image, p1, p2, color, thickness=lw, lineType=cv2.LINE_AA)
if label:
tf = max(lw - 1, 1) # font thickness
w, h = cv2.getTextSize(label, 0, fontScale=lw / 3, thickness=tf)[0] # text width, height
outside = p1[1] - h - 3 >= 0 # label fits outside box
p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
cv2.rectangle(image, p1, p2, color, -1, cv2.LINE_AA) # filled
cv2.putText(
image,
label,
(p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
0,
lw / 3,
txt_color,
thickness=tf,
lineType=cv2.LINE_AA,
)
@staticmethod
def font_check(font="./yolov6/utils/Arial.ttf", size=10):
# Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary
assert osp.exists(font), f"font path not exists: {font}"
try:
return ImageFont.truetype(str(font) if font.exists() else font.name, size)
except Exception as e: # download if missing
return ImageFont.truetype(str(font), size)
@staticmethod
def box_convert(x):
# Convert boxes with shape [n, 4] from [x1, y1, x2, y2] to [x, y, w, h] where x1y1=top-left, x2y2=bottom-right
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center
y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center
y[:, 2] = x[:, 2] - x[:, 0] # width
y[:, 3] = x[:, 3] - x[:, 1] # height
return y
@staticmethod
def generate_colors(i, bgr=False):
hex = (
"FF3838",
"FF9D97",
"FF701F",
"FFB21D",
"CFD231",
"48F90A",
"92CC17",
"3DDB86",
"1A9334",
"00D4BB",
"2C99A8",
"00C2FF",
"344593",
"6473FF",
"0018EC",
"8438FF",
"520085",
"CB38FF",
"FF95C8",
"FF37C7",
)
palette = []
for iter in hex:
h = "#" + iter
palette.append(tuple(int(h[1 + i : 1 + i + 2], 16) for i in (0, 2, 4)))
num = len(palette)
color = palette[int(i) % num]
return (color[2], color[1], color[0]) if bgr else color
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