OpenCV3与深度学习实例-使用YOLOV3进行物体检测

import cv2
import argparse
import numpy as np

ap = argparse.ArgumentParser()
ap.add_argument('-i', '--image', required=False,default='datas/images/people.jpg',
help = 'path to input image')
ap.add_argument('-c', '--config', required=False,default='datas/models/yolov3/yolov3.cfg',
help = 'path to yolo config file')
ap.add_argument('-w', '--weights', required=False,default='datas/models/yolov3/yolov3.weights',
help = 'path to yolo pre-trained weights')
ap.add_argument('-cl', '--classes', required=False,default='datas/models/yolov3/yolov3.txt',
help = 'path to text file containing class names')
args = ap.parse_args()

def get_output_layers(net):

layer_names = net.getLayerNames()

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

return output_layers

def draw_prediction(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)

image = cv2.imread(args.image)

Width = image.shape[1]
Height = image.shape[0]
scale = 0.00392

classes = None

with open(args.classes, 'r') as f:
classes = [line.strip() for line in f.readlines()]

COLORS = np.random.uniform(0, 255, size=(len(classes), 3))

net = cv2.dnn.readNet(args.weights, args.config)

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

net.setInput(blob)

outs = net.forward(get_output_layers(net))

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

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])

indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)

for i in indices:
i = i[0]
box = boxes[i]
x = box[0]
y = box[1]
w = box[2]
h = box[3]
draw_prediction(image, class_ids[i], confidences[i], round(x), round(y), round(x+w), round(y+h))

cv2.imshow("object detection", image)
cv2.waitKey()

# cv2.imwrite("object-detection.jpg", image)
cv2.destroyAllWindows()