opencv 头盔检测

只能检测头盔，不能检测人头，不能判断是否带头盔

https://github.com/BlcaKHat/yolov3-Helmet-Detection/blob/master/Helmet_detection_YOLOV3.py

权重：

https://github.com/rezabonyadi/Helmet_Detection_YOLO

  

   

    

     

    
    

     

      from time import sleep
     
    
   

    

     

    
    

     

      import cv2 as cv
     
    
   

    

     

    
    

     

      import argparse
     
    
   

    

     

    
    

     

      import sys
     
    
   

    

     

    
    

     

      import numpy as np
     
    
   

    

     

    
    

     

      import os.path
     
    
   

    

     

    
    

     

      from glob import glob
     
    
   

    

     

    
    

     

      #from PIL import image
     
    
   

    

     

    
    

     

      frame_count = 0 # used in mainloop where we're extracting images., and then to drawPred( called by post process)
     
    
   

    

     

    
    

     

      frame_count_out=0 # used in post process loop, to get the no of specified class value.
     
    
   

    

     

    
    

     

      # Initialize the parameters
     
    
   

    

     

    
    

     

      confThreshold = 0.5  #Confidence threshold
     
    
   

    

     

    
    

     

      nmsThreshold = 0.4   #Non-maximum suppression threshold
     
    
   

    

     

    
    

     

      inpWidth = 416 #Width of network's input image
     
    
   

    

     

    
    

     

      inpHeight = 416 #Height of network's input image
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Load names of classes
     
    
   

    

     

    
    

     

      classesFile = "obj.names";
     
    
   

    

     

    
    

     

      classes = None
     
    
   

    

     

    
    

     

      with open(classesFile, 'rt') as f:
     
    
   

    

     

    
    

     

       classes = f.read().rstrip('\n').split('\n')
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Give the configuration and weight files for the model and load the network using them.
     
    
   

    

     

    
    

     

      modelConfiguration = "yolov3-obj.cfg";
     
    
   

    

     

    
    

     

      modelWeights = "yolov3-obj_2400.weights";
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      net = cv.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
     
    
   

    

     

    
    

     

      net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
     
    
   

    

     

    
    

     

      net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Get the names of the output layers
     
    
   

    

     

    
    

     

      def getOutputsNames(net):
     
    
   

    

     

    
    

     
 # Get the names of all the layers in the network
     
    
   

    

     

    
    

     

       layersNames = net.getLayerNames()
     
    
   

    

     

    
    

     
 # Get the names of the output layers, i.e. the layers with unconnected outputs
     
    
   

    

     

    
    

     
 return [layersNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Draw the predicted bounding box
     
    
   

    

     

    
    

     

      def drawPred(classId, conf, left, top, right, bottom):
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 global frame_count
     
    
   

    

     

    
    

     

      # Draw a bounding box.
     
    
   

    

     

    
    

     

       cv.rectangle(frame, (left, top), (right, bottom), (255, 178, 50), 3)
     
    
   

    

     

    
    

     

       label = '%.2f' % conf
     
    
   

    

     

    
    

     
 # Get the label for the class name and its confidence
     
    
   

    

     

    
    

     
 if classes:
     
    
   

    

     

    
    

     
 assert(classId < len(classes))
     
    
   

    

     

    
    

     

       label = '%s:%s' % (classes[classId], label)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 #Display the label at the top of the bounding box
     
    
   

    

     

    
    

     

       labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
     
    
   

    

     

    
    

     

       top = max(top, labelSize[1])
     
    
   

    

     

    
    

     
 #print(label) #testing
     
    
   

    

     

    
    

     
 #print(labelSize) #testing
     
    
   

    

     

    
    

     
 #print(baseLine) #testing
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       label_name,label_conf = label.split(':') #spliting into class & confidance. will compare it with person.
     
    
   

    

     

    
    

     
 if label_name == 'Helmet':
     
    
   

    

     

    
    

     
 #will try to print of label have people.. or can put a counter to find the no of people occurance.
     
    
   

    

     

    
    

     
 #will try if it satisfy the condition otherwise, we won't print the boxes or leave it.
     
    
   

    

     

    
    

     

       cv.rectangle(frame, (left, top - round(1.5*labelSize[1])), (left + round(1.5*labelSize[0]), top + baseLine), (255, 255, 255), cv.FILLED)
     
    
   

    

     

    
    

     

       cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.75, (0,0,0), 1)
     
    
   

    

     

    
    

     

       frame_count+=1
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 #print(frame_count)
     
    
   

    

     

    
    

     
 if(frame_count> 0):
     
    
   

    

     

    
    

     
 return frame_count
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Remove the bounding boxes with low confidence using non-maxima suppression
     
    
   

    

     

    
    

     

      def postprocess(frame, outs):
     
    
   

    

     

    
    

     

       frameHeight = frame.shape[0]
     
    
   

    

     

    
    

     

       frameWidth = frame.shape[1]
     
    
   

    

     

    
    

     
 global frame_count_out
     
    
   

    

     

    
    

     

       frame_count_out=0
     
    
   

    

     

    
    

     

       classIds = []
     
    
   

    

     

    
    

     

       confidences = []
     
    
   

    

     

    
    

     

       boxes = []
     
    
   

    

     

    
    

     
 # Scan through all the bounding boxes output from the network and keep only the
     
    
   

    

     

    
    

     
 # ones with high confidence scores. Assign the box's class label as the class with the highest score.
     
    
   

    

     

    
    

     

       classIds = [] #have to fins which class have hieghest confidence........=====>>><<<<=======
     
    
   

    

     

    
    

     

       confidences = []
     
    
   

    

     

    
    

     

       boxes = []
     
    
   

    

     

    
    

     
 for out in outs:
     
    
   

    

     

    
    

     
 for detection in out:
     
    
   

    

     

    
    

     

       scores = detection[5:]
     
    
   

    

     

    
    

     

       classId = np.argmax(scores)
     
    
   

    

     

    
    

     

       confidence = scores[classId]
     
    
   

    

     

    
    

     
 if confidence > confThreshold:
     
    
   

    

     

    
    

     

       center_x = int(detection[0] * frameWidth)
     
    
   

    

     

    
    

     

       center_y = int(detection[1] * frameHeight)
     
    
   

    

     

    
    

     

       width = int(detection[2] * frameWidth)
     
    
   

    

     

    
    

     

       height = int(detection[3] * frameHeight)
     
    
   

    

     

    
    

     

       left = int(center_x - width / 2)
     
    
   

    

     

    
    

     

       top = int(center_y - height / 2)
     
    
   

    

     

    
    

     

       classIds.append(classId)
     
    
   

    

     

    
    

     
 #print(classIds)
     
    
   

    

     

    
    

     

       confidences.append(float(confidence))
     
    
   

    

     

    
    

     

       boxes.append([left, top, width, height])
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 # Perform non maximum suppression to eliminate redundant overlapping boxes with
     
    
   

    

     

    
    

     
 # lower confidences.
     
    
   

    

     

    
    

     

       indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
     
    
   

    

     

    
    

     

       count_person=0 # for counting the classes in this loop.
     
    
   

    

     

    
    

     
 for i in indices:
     
    
   

    

     

    
    

     

       i = i[0]
     
    
   

    

     

    
    

     

       box = boxes[i]
     
    
   

    

     

    
    

     

       left = box[0]
     
    
   

    

     

    
    

     

       top = box[1]
     
    
   

    

     

    
    

     

       width = box[2]
     
    
   

    

     

    
    

     

       height = box[3]
     
    
   

    

     

    
    

     
 #this function in loop is calling drawPred so, try pushing one test counter in parameter , so it can calculate it.
     
    
   

    

     

    
    

     

       frame_count_out = drawPred(classIds[i], confidences[i], left, top, left + width, top + height)
     
    
   

    

     

    
    

     
 #increase test counter till the loop end then print...
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 #checking class, if it is a person or not
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       my_class='Helmet' #======================================== mycode .....
     
    
   

    

     

    
    

     

       unknown_class = classes[classId]
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 if my_class == unknown_class:
     
    
   

    

     

    
    

     

       count_person += 1
     
    
   

    

     

    
    

     
 #if(frame_count_out > 0):
     
    
   

    

     

    
    

     

       print(frame_count_out)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 if count_person >= 1:
     
    
   

    

     

    
    

     

       path = 'test_out/'
     
    
   

    

     

    
    

     

       frame_name=os.path.basename(fn) # trimm the path and give file name.
     
    
   

    

     

    
    

     

       cv.imwrite(str(path)+frame_name, frame) # writing to folder.
     
    
   

    

     

    
    

     
 #print(type(frame))
     
    
   

    

     

    
    

     

       cv.imshow('img',frame)
     
    
   

    

     

    
    

     

       cv.waitKey(800)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 #cv.imwrite(frame_name, frame)
     
    
   

    

     

    
    

     
 #======================================mycode.........
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Process inputs
     
    
   

    

     

    
    

     

      winName = 'Deep learning object detection in OpenCV'
     
    
   

    

     

    
    

     

      cv.namedWindow(winName, cv.WINDOW_NORMAL)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      for fn in glob('images/*.jpg'):
     
    
   

    

     

    
    

     

       frame = cv.imread(fn)
     
    
   

    

     

    
    

     

       frame_count =0
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 # Create a 4D blob from a frame.
     
    
   

    

     

    
    

     

       blob = cv.dnn.blobFromImage(frame, 1/255, (inpWidth, inpHeight), [0,0,0], 1, crop=False)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 # Sets the input to the network
     
    
   

    

     

    
    

     

       net.setInput(blob)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 # Runs the forward pass to get output of the output layers
     
    
   

    

     

    
    

     

       outs = net.forward(getOutputsNames(net))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 # Remove the bounding boxes with low confidence
     
    
   

    

     

    
    

     

       postprocess(frame, outs)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 # Put efficiency information. The function getPerfProfile returns the overall time for inference(t) and the timings for each of the layers(in layersTimes)
     
    
   

    

     

    
    

     

       t, _ = net.getPerfProfile()
     
    
   

    

     

    
    

     
 #print(t)
     
    
   

    

     

    
    

     

       label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
     
    
   

    

     

    
    

     
 #print(label)
     
    
   

    

     

    
    

     

       cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
     
    
   

    

     

    
    

     
 #print(label)
     
    
  
 

文章来源: blog.csdn.net，作者：网奇，版权归原作者所有，如需转载，请联系作者。

原文链接：blog.csdn.net/jacke121/article/details/90647925