关于物体检测，目前深度学习网络的优势很明显，也是当前的热门应用。
目前，主要有三种主要的方法：
- Faster R-CNNs (Girshick et al., 2015)
- You Only Look Once (YOLO) (Redmon and Farhadi, 2015)
- Single Shot Detectors (SSDs) (Liu et al., 2015)
FR-CNNs慢但是准确性高，YOLO很快但是精度差点。SSDs平衡了这两种算法。如果再结合Google提出的MobileNets优化网络结构，可以得到很理想的效果。
下面贴出源码：
import numpy as np
import cv2
imagefile = 'images/test02.jpg'
deploy_prototxt = 'MobileNetSSD_deploy.prototxt'
model = 'MobileNetSSD_deploy.caffemodel'
confidence_default = 0.2
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
"sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
print("[info] loading model...")
net = cv2.dnn.readNetFromCaffe(deploy_prototxt, model)
image = cv2.imread(imagefile)
(h, w) = image.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 0.007843, (300, 300), 127.5)
print("[info] computing object detections...")
net.setInput(blob)
detections = net.forward()
for i in np.arange(0, detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > confidence_default:
idx = int(detections[0, 0, i, 1])
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
print("[info] {}".format(label))
cv2.rectangle(image, (startX, startY), (endX, endY), COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(image, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
cv2.imshow("output", image)
cv2.waitKey(0)
OpenCV需要3.3以上支持，因为代码中用到了cv2.dnn模块。
检测效果：
output
