Show_image.py

import numpy as np
import cv2
import argparse
import sys
import os.path

# # Sample: call a picture
# img = cv2.imread('/Users/jiarongj/Pictures/devon.jpg')
# cv2.imshow('Image', img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
#
#
# # Sample: call a video
# # Leave the imread(0) when input is from your webcam
# cap = cv2.VideoCapture('/Users/jiarongj/Pictures/Sphere.mov')
# if cap.isOpened() == False:
#     print("Can't open file or video stream")
#
# while True:
#     ret, frame = cap.read()
#     if ret == True:
#         cv2.imshow('Frame', frame)
#         if cv2.waitKey(25) & 0xFF == 27:
#             break
#     else:
#         break
#
# cap.release()
# cv2.destroyAllWindows()


# Yolo video object-detection
confThreshold = 0.5
nmsThreshold = 0.4
parser = argparse.ArgumentParser(description='Object-detection using Yolo')
parser.add_argument('--image', help='Path to image file')
parser.add_argument('--video', help='Path to video file')
args = parser.parse_args()

#load names from coco

# Load names of classes
classesFile = "/Users/jiarongj/PycharmProjects/untitled1/archive/coco.names"
classes = None
with open(classesFile, 'rt') as f:
    classes = f.read().rstrip('\n').split('\n')

net = cv2.dnn.readNetFromDarknet('/Users/jiarongj/PycharmProjects/untitled1/archive/yolov3.cfg', '/Users/jiarongj/PycharmProjects/untitled1/archive/yolov3.weights')
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)

#Return names for output layers
def getOutputsNames(net):
    layersNames = net.getLayerNames()
    # print(net.getUnconnectedOutLayers())
    return [layersNames[i-1] for i in net.getUnconnectedOutLayers()]

#Draw the bounding box for prediction
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]

    # 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:
        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)


# Process inputs
winName = 'Deep learning object detection in OpenCV'
cv2.namedWindow(winName, cv2.WINDOW_NORMAL)

outputFile = "YOLOv3_output.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 = cv2.VideoCapture(args.image)
    outputFile = args.image[:-4] + '_YOLOv3_output.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 = cv2.VideoCapture(args.video)
    outputFile = args.video[:-4] + '_YOLOv3_output.avi'
else:
    # Webcam input
    cap = cv2.VideoCapture(0)

# Get the video writer initialized to save the output video
if (not args.image):
    vid_writer = cv2.VideoWriter(outputFile, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 30,
                                (round(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), round(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))))

while cv2.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)
        cv2.waitKey(5000)
        break


    # Create a 4D blob from a frame.
    blob = cv2.dnn.blobFromImage(frame, 1 / 255, (416, 416), [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))

    # Write the frame with the detection boxes
    if (args.image):
        cv2.imwrite(outputFile, frame.astype(np.uint8));
    else:
        vid_writer.write(frame.astype(np.uint8))

    cv2.imshow(winName, frame)