test.py

# import required packages
import cv2
import argparse
import numpy as np
import os
import time

# use gpu
# os.environ["OPENCV_DNN_OPENCL_ALLOW_ALL_DEVICES"] = "1"

# handle command line arguments
ap = argparse.ArgumentParser()
ap.add_argument('-i', '--image', required=True,
                help='path to input image')
ap.add_argument('-c', '--config', required=True,
                help='path to yolo config file')
ap.add_argument('-w', '--weights', required=True,
                help='path to yolo pre-trained weights')
ap.add_argument('-cl', '--classes', required=True,
                help='path to text file containing class names')
args = ap.parse_args()


# read class names from text file
classes = None
with open(args.classes, 'r') as f:
    classes = [line.strip() for line in f.readlines()]
# generate different colors for different classes
COLORS = np.random.uniform(0, 255, size=(len(classes), 3))

# function to get the output layer names
# in the architecture


def get_output_layers(net):

    layer_names = net.getLayerNames()

    output_layers = [layer_names[i[0] - 1]
                     for i in net.getUnconnectedOutLayers()]

    return output_layers

# function to draw bounding box on the detected object with class name


def draw_bounding_box(img, class_id, confidence, x, y, x_plus_w, y_plus_h):

    label = str(classes[class_id])

    color = COLORS[class_id]

    cv2.rectangle(img, (x, y), (x_plus_w, y_plus_h), color, 2)

    cv2.putText(img, label, (x-10, y-10),
                cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)


def detect_and_draw(image, net):
    Width = image.shape[1]
    Height = image.shape[0]
    scale = 0.00392

    # create input blob
    blob = cv2.dnn.blobFromImage(
        image, scale, (416, 416), (0, 0, 0), True, crop=False)

    # set input blob for the network
    net.setInput(blob)

    # run inference through the network
    # and gather predictions from output layers
    outs = net.forward(get_output_layers(net))

    # initialization
    class_ids = []
    confidences = []
    boxes = []
    conf_threshold = 0.5
    nms_threshold = 0.4

    # for each detetion from each output layer
    # get the confidence, class id, bounding box params
    # and ignore weak detections (confidence < 0.5)
    for out in outs:
        for detection in out:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > 0.5:
                center_x = int(detection[0] * Width)
                center_y = int(detection[1] * Height)
                w = int(detection[2] * Width)
                h = int(detection[3] * Height)
                x = center_x - w / 2
                y = center_y - h / 2
                class_ids.append(class_id)
                confidences.append(float(confidence))
                boxes.append([x, y, w, h])

    # apply non-max suppression
    indices = cv2.dnn.NMSBoxes(
        boxes, confidences, conf_threshold, nms_threshold)

    # go through the detections remaining
    # after nms and draw bounding box
    for i in indices:
        i = i[0]
        box = boxes[i]
        x = box[0]
        y = box[1]
        w = box[2]
        h = box[3]

        draw_bounding_box(image, class_ids[i], confidences[i], round(
            x), round(y), round(x+w), round(y+h))


# read pre-trained model and config file
net = cv2.dnn.readNet(args.weights, args.config)

# read input image
image = cv2.imread(args.image)

start_time = time.time()
detect_and_draw(image, net)
use_time = time.time()-start_time
print('cost %f seconds' % use_time)
# display output image
# cv2.imshow("object detection", image)

# wait until any key is pressed
# cv2.waitKey()

# save output image to disk
cv2.imwrite("output.jpg", image)

# release resources
cv2.destroyAllWindows()