第30天了終於

無Ｂuffter版會快一點

參考
Non-maximum suppression threshold

基本的 車子，機車，人物等形狀可以辨識

#可在這裡做判斷 那邊寫自己的判斷式

#For CV2 > 4.5.1
import cv2, queue, threading, time
print(cv2.__version__)
import numpy as np
classesFile = "YOLOweight/coco.names"
modelConfiguration = "YOLOweight/yolov4.cfg"
modelWeights = "YOLOweight/yolov4.weights"
# Initialize the parameters
confThreshold = 0.5  #Confidence threshold
nmsThreshold = 0.4   #Non-maximum suppression threshold，機率門檻，0.4以下不顯示在畫面
inpWidth = 640      #Width of network's input image，改为320*320更快
inpHeight = 480      #Height of network's input image，改为608*608更准

# bufferless VideoCapture
class VideoCapture:
  def __init__(self, name):
    self.cap = cv2.VideoCapture(name)
    self.q = queue.Queue()
    t = threading.Thread(target=self._reader)
    t.daemon = True
    t.start()
  # read frames as soon as they are available, keeping only most recent one
  def _reader(self):
    while True:
      ret, frame = self.cap.read()
      if not ret:
        break
      if not self.q.empty():
        try:
          self.q.get_nowait()   # discard previous (unprocessed) frame
        except queue.Empty:
          pass
      self.q.put(frame)
  def read(self):
    return self.q.get()

target=0 #"https://cctv6.kctmc.nat.gov.tw/1dd04532/"
cap = VideoCapture(target)     

# Load names of classes
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.
net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
net.setPreferableTarget(cv2.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 - 1] for i in net.getUnconnectedOutLayers()]

# Draw the predicted bounding box
def drawPred(classId, conf, left, top, right, bottom):
    # Draw a bounding box.
    cv2.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 = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
    top = max(top, labelSize[1])
    cv2.rectangle(frame, (left, top - round(1.5*labelSize[1])), (left + round(1.5*labelSize[0]), top + baseLine), (255, 255, 255), cv2.FILLED)
    cv2.putText(frame, label, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0,0,0), 1)

# Remove the bounding boxes with low confidence using non-maxima suppression
def postprocess(frame, outs):
    frameHeight = frame.shape[0]
    frameWidth = frame.shape[1]

    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 = []
    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)
                confidences.append(float(confidence))
                boxes.append([left, top, width, height])

    # Perform non maximum suppression to eliminate redundant overlapping boxes with
    # lower confidences.
    indices = cv2.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
    for i in indices:
        i = i
        box = boxes[i]
        left = box[0]
        top = box[1]
        width = box[2]
        height = box[3]
        drawPred(classIds[i], confidences[i], left, top, left + width, top + height)
        #---------可在這裡做判斷----------------

while True:

  # get frame from the video
  frame = cap.read()

  # Create a 4D blob from a frame.
  blob = cv2.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()
  label = 'Inference time: %.2f ms' % (t * 1000.0 / cv2.getTickFrequency())
  cv2.putText(frame, label, (0, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))

  cv2.imshow("frame", frame)
  key=cv2.waitKey(1) #等候使用者按鍵盤指令
  if key==ord('a'):  #a拍照
    cv2.imwrite('webcam.jpg',frame) #拍照
  if key==ord('q'):  #q退出
    break