提交 c00a9070 编写于 作者: 幻灰龙's avatar 幻灰龙

Merge branch 'hhhhhhhhhhwwwwwwwwww-master-patch-57115' into 'master'

上传新文件deep_learning_object_detection.py

See merge request !27
import numpy as np
import cv2
if __name__=="__main__":
image_name = '11.jpg'
prototxt = 'MobileNetSSD_deploy.prototxt.txt'
model_path = 'MobileNetSSD_deploy.caffemodel'
confidence_ta = 0.2
# 初始化MobileNet SSD训练的类标签列表
# 检测,然后为每个类生成一组边界框颜色
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))
# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(prototxt, model_path)
# 加载输入图像并为图像构造一个输入blob
# 将大小调整为固定的300x300像素。
# (注意:SSD模型的输入是300x300像素)
image = cv2.imread(image_name)
(h, w) = image.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 0.007843,
(300, 300), 127.5)
# 通过网络传递blob并获得检测结果和
# 预测
print("[INFO] computing object detections...")
net.setInput(blob)
detections = net.forward()
# 循环检测结果
for i in np.arange(0, detections.shape[2]):
# 提取与数据相关的置信度(即概率)
# 预测
confidence = detections[0, 0, i, 2]
# 通过确保“置信度”来过滤掉弱检测
# 大于最小置信度
if confidence > confidence_ta:
# 从`detections`中提取类标签的索引,
# 然后计算物体边界框的 (x, y) 坐标
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")
# 显示预测
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)
# show the output image
cv2.imshow("Output", image)
cv2.imwrite("output.jpg", image)
cv2.waitKey(0)
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