import cv2
|
import numpy as np
|
import yaml
|
from openvino.runtime import Core
|
|
|
class SAFETY_DETECT:
|
|
def __init__(self, path, conf_thres=0.35, iou_thres=0.5):
|
self.conf_threshold = conf_thres
|
self.iou_threshold = iou_thres
|
|
# Initialize model
|
self.initialize_model(path)
|
self.color_palette = [(np.random.randint(0, 255), np.random.randint(0, 255), np.random.randint(0, 255)) for _ in
|
range(100)]
|
|
def __call__(self, image):
|
return self.detect_objects(image)
|
|
def read_config(self, path):
|
file_path = path+'/metadata.yaml'
|
with open(file_path, 'r', encoding="utf-8") as file:
|
config = yaml.safe_load(file)
|
return config
|
|
def initialize_model(self, path):
|
model_path = path + '/best.xml'
|
# Initialize OpenVINO Runtime
|
self.core = Core()
|
# Load the model
|
self.model = self.core.read_model(model=model_path)
|
# Compile the model
|
self.compiled_model = self.core.compile_model(model=self.model, device_name="CPU")
|
# Get input and output layers
|
self.input_layer = self.compiled_model.input(0)
|
self.output_layer = self.compiled_model.output(0)
|
# Get class names
|
self.class_names = CLASSES = self.read_config(path)['names']
|
|
def detect_objects(self, frame):
|
# 获得模型输出节点
|
|
ir = self.compiled_model.create_infer_request()
|
[height, width, _] = frame.shape
|
length = max((height, width))
|
image = np.zeros((length, length, 3), np.uint8)
|
image[0:height, 0:width] = frame
|
self.scale = length / 640
|
|
blob = cv2.dnn.blobFromImage(image, scalefactor=1 / 255, size=(640, 640), swapRB=True)
|
outputs = ir.infer(blob)[self.output_layer]
|
outputs = np.array([cv2.transpose(outputs[0])])
|
rows = outputs.shape[1]
|
|
boxes = []
|
scores = []
|
class_ids = []
|
|
for i in range(rows):
|
classes_scores = outputs[0][i][4:]
|
(minScore, maxScore, minClassLoc, (x, maxClassIndex)) = cv2.minMaxLoc(classes_scores)
|
if maxScore >= 0.25:
|
box = [outputs[0][i][0] - (0.5 * outputs[0][i][2]), outputs[0][i][1] - (0.5 * outputs[0][i][3]),
|
outputs[0][i][2], outputs[0][i][3]]
|
boxes.append(box)
|
scores.append(maxScore)
|
class_ids.append(maxClassIndex)
|
|
|
|
return boxes, scores, class_ids
|
|
def draw_bounding_box(self, img, class_id, confidence, x, y, x_plus_w, y_plus_h):
|
|
label = f'{self.class_names[class_id]} ({confidence:.2f})'
|
color = self.color_palette[class_id]
|
cv2.rectangle(img, (x, y), (x_plus_w, y_plus_h), color, 2)
|
cv2.putText(img, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
|
|
def draw_detections(self, frame, class_ids, scores, boxes):
|
det_img = frame.copy()
|
result_boxes = cv2.dnn.NMSBoxes(boxes, scores, 0.25, 0.45, 0.5)
|
|
for i in range(len(result_boxes)):
|
index = result_boxes[i]
|
box = boxes[index]
|
self.draw_bounding_box(det_img, class_ids[index], scores[index], round(box[0] * self.scale), round(box[1] * self.scale),
|
round((box[0] + box[2]) * self.scale), round((box[1] + box[3]) * self.scale))
|
return det_img
|