目录
1 预测单张图像
2 摄像头调用模型
1 预测单张图像
要把py文件放在与darknet.py相同的路径下
import darknetdef classify(net, meta, im):out = darknet.predict_image(net, im)res = []for i in range(meta.classes):res.append((meta.names[i], out[i]))res = sorted(res, key=lambda x: -x[1])return resnet = darknet.load_net("/home/suyu/darknet/custom_classification/my.cfg".encode("ascii"), "/home/suyu/darknet/custom_classification/trained_models/my_final.weights".encode("ascii"), 0)
meta = darknet.load_meta("/home/suyu/darknet/custom_classification/custom_training.data".encode("ascii"))im = darknet.load_image("/home/suyu/darknet/custom_classification/dataset/airplane/airplane_149.jpg".encode("ascii"), 0, 0)r = classify(net, meta, im)
print(r[:10])
最后的r是个数组，按照置信度排序
上面是输入图像路径预测的，下面是用opencv读图后预测的
import darknetdef classify(net, meta, im):out = darknet.predict_image(net, im)res = []for i in range(meta.classes):res.append((meta.names[i], out[i]))res = sorted(res, key=lambda x: -x[1])return resnet = darknet.load_net("/home/suyu/darknet/custom_classification/my.cfg".encode("ascii"), "/home/suyu/darknet/custom_classification/trained_models/my_final.weights".encode("ascii"), 0)
meta = darknet.load_meta("/home/suyu/darknet/custom_classification/custom_training.data".encode("ascii"))import cv2width = darknet.network_width(net)
height = darknet.network_height(net)
im = darknet.make_image(width, height, 3)
img = cv2.imread('/home/hdkj/darknet/custom_classification/dataset/airplane/airplane_149.jpg')
darknet.copy_image_from_bytes(im,img.tobytes())r = classify(net, meta, im)
print(r[:10])经测试效果一样
2 摄像头调用模型
要把py文件放在与darknet.py相同的路径下
import cv2
import numpy as np
import queue
import threading
import time
import darknetdef classify(net, meta, im):out = darknet.predict_image(net, im)res = []for i in range(meta.classes):res.append((meta.names[i], out[i]))res = sorted(res, key=lambda x: -x[1])return resnet = darknet.load_net("/home/suyu/darknet/custom_classification/my.cfg".encode("ascii"), "/home/suyu/darknet/custom_classification/trained_models/my_final.weights".encode("ascii"), 0)
meta = darknet.load_meta("/home/suyu/darknet/custom_classification/custom_training.data".encode("ascii"))width = darknet.network_width(net)
height = darknet.network_height(net)
darknet_image = darknet.make_image(width, height, 3)cap = cv2.VideoCapture(0)frame_queue = queue.Queue(maxsize=1)
detection_result_queue = queue.Queue(maxsize=1)def video_capture():while cap.isOpened():ret, frame = cap.read()if ret:image_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)image_resized = cv2.resize(image_rgb, (width, height), interpolation=cv2.INTER_LINEAR)frame_queue.put(image_resized)cap.release()def predict():while cap.isOpened():start_time = time.time()image_resized = frame_queue.get()darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())detections = classify(net, meta, darknet_image)# darknet.free_image(darknet_image)#print(time.time() - start_time)print(detections)detection_result_queue.put(detections)cap.release()def draw():while cap.isOpened():draw_frame = frame_queue.get()try:detections = detection_result_queue.get(block=False)label = detections[0][0].decode()confidence = round(detections[0][1],2)if confidence < 0.9:label = 'no_detection'draw_frame = cv2.putText(draw_frame, label + ' ' + str(confidence), (20,30), cv2.FONT_HERSHEY_SIMPLEX,1,(255, 0, 0),1,lineType=cv2.LINE_AA)except:draw_frame = cv2.putText(draw_frame, 'please wait', (20, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 1,lineType=cv2.LINE_AA)passdraw_frame = cv2.cvtColor(draw_frame, cv2.COLOR_BGR2RGB)cv2.imshow('draw_frame', draw_frame)cv2.waitKey(1)cap.release()threading.Thread(target=video_capture).start()
threading.Thread(target=predict).start()
threading.Thread(target=draw).start()目标分类的特点是无论你给一张什么图，模型都能给你一个结果，但这个结果置信率不高。这个时候我们可以利用低置信率手动生成一个 什么都不是 的种类
经测试效果可以，在nx上0.07-0.08秒能识别一帧图像，预测效果流畅
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