2020年大三下学期第十七周学习心得

# It is based on the OpenCV project.
import cv2 as cv
import argparse
import sys
import numpy as np
import os.path
# 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

parser = argparse.ArgumentParser(description='Object Detection using YOLO in OPENCV')
parser.add_argument('--image', help='Path to image file.')
parser.add_argument('--video',default='test3_video.mp4', help='Path to video file.')
args = parser.parse_args()

# Load names of classes
classesFile = "coco.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.cfg";
modelWeights = "yolov3.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):
    # Draw a bounding box.
    # print(classId)
    cv.rectangle(frame, (left, top), (right, bottom), (int(classId)*66+10, int(classId)*33+int(classId)*10-10+33, int(classId)*50+85), 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])
    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)


# Remove the bounding boxes with low confidence using nms
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 nms to eliminate redundant overlapping boxes with
    # lower confidences.
    indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
    for i in indices:
        i = i[0]
        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)

def postprocess2(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 nms to eliminate redundant overlapping boxes with
        # lower confidences.
        indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
        num=[0,0,0,0,0,0]
        person = 0
        car=0
        motorbike=0
        bus=0
        bicycle=0
        for i in indices:
            i = i[0]
            box = boxes[i]
            left = box[0]
            top = box[1]
            width = box[2]
            height = box[3]
            print(str(classes[classIds[i]]))
            if(str(classes[classIds[i]])=="person"):
                person+=1
                num=[person,car,motorbike,bus,bicycle]
            if(str(classes[classIds[i]])=="car"):
                car+=1
                num=[person,car,motorbike,bus,bicycle]
            if (str(classes[classIds[i]]) == "motorbike"):
                motorbike += 1
                num = [person, car, motorbike, bus,bicycle]
            if (str(classes[classIds[i]]) == "bus"):
                bus += 1
                num = [person, car, motorbike, bus,bicycle]
            if (str(classes[classIds[i]]) == "bicycle"):
                bicycle += 1
                num = [person, car, motorbike, bus,bicycle]
        return num
# Process inputs
winName = 'Deep learning object detection in OpenCV'
cv.namedWindow(winName, cv.WINDOW_NORMAL)

outputFile = "yolo_out_py.avi"
if (args.image):
    # Open the image file
    if not os.path.isfile(args.image):
        print("Input image file ", args.image, " doesn't exist")
        sys.exit(1)
    cap = cv.VideoCapture(args.image)
    outputFile = args.image[:-4] + '_yolo_out_py.jpg'
elif (args.video):
    # Open the video file
    if not os.path.isfile(args.video):
        print("Input video file ", args.video, " doesn't exist")
        sys.exit(1)
    cap = cv.VideoCapture(args.video)
    outputFile = args.video[:-4] + 'test_video_out.avi'
else:
    # Webcam input
    cap = cv.VideoCapture(0)

# Get the video writer initialized to save the output video
if (not args.image):
    vid_writer = cv.VideoWriter(outputFile, cv.VideoWriter_fourcc('M', 'J', 'P', 'G'), 30,
                                (round(cap.get(cv.CAP_PROP_FRAME_WIDTH)), round(cap.get(cv.CAP_PROP_FRAME_HEIGHT))))

while cv.waitKey(1) < 0:

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

    # Stop the program if reached end of video
    if not hasFrame:
        print("Done processing !!!")
        print("Output file is stored as ", outputFile)
        cv.waitKey(3000)
        # Release device
        cap.release()
        break

    # 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()
    label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
    cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
    label = 'Person num:%s' % (postprocess2(frame,outs)[0])
    cv.putText(frame, label, (0, 30), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0))
    label = 'Car num:%s' % (postprocess2(frame, outs)[1])
    cv.putText(frame, label, (0, 45), cv.FONT_HERSHEY_SIMPLEX, 0.5, (240, 255, 255))
    label = 'Motorbike num:%s' % (postprocess2(frame, outs)[2])
    cv.putText(frame, label, (0, 60), cv.FONT_HERSHEY_SIMPLEX, 0.5, (245, 245, 245))
    label = 'Bus num:%s' % (postprocess2(frame, outs)[3])
    cv.putText(frame, label, (0, 75), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 97, 0))
    label = 'Bicycle num:%s' % (postprocess2(frame, outs)[4])
    cv.putText(frame, label, (0, 90), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 230, 201))
    # Write the frame with the detection boxes
    if (args.image):
        cv.imwrite(outputFile, frame.astype(np.uint8));
    else:
        vid_writer.write(frame.astype(np.uint8))
    cv.imshow(winName, frame)

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