From 2e3c3dfbf1c5f820de76bf6c89e6be368bcaac92 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?AI=E6=B5=A9?= <1090220084@qq.com> Date: Tue, 14 Dec 2021 13:12:00 +0800 Subject: [PATCH] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E6=96=B0=E6=96=87=E4=BB=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../deep_learning_object_detection.md" | 152 ++++++++++++++++++ 1 file changed, 152 insertions(+) create mode 100644 "data/1.OpenCV\345\210\235\351\230\266/7.OpenCV\344\270\255\347\232\204\346\267\261\345\272\246\345\255\246\344\271\240/2.\347\233\256\346\240\207\346\243\200\346\265\213/deep_learning_object_detection.md" diff --git "a/data/1.OpenCV\345\210\235\351\230\266/7.OpenCV\344\270\255\347\232\204\346\267\261\345\272\246\345\255\246\344\271\240/2.\347\233\256\346\240\207\346\243\200\346\265\213/deep_learning_object_detection.md" "b/data/1.OpenCV\345\210\235\351\230\266/7.OpenCV\344\270\255\347\232\204\346\267\261\345\272\246\345\255\246\344\271\240/2.\347\233\256\346\240\207\346\243\200\346\265\213/deep_learning_object_detection.md" new file mode 100644 index 0000000..6d26b8d --- /dev/null +++ "b/data/1.OpenCV\345\210\235\351\230\266/7.OpenCV\344\270\255\347\232\204\346\267\261\345\272\246\345\255\246\344\271\240/2.\347\233\256\346\240\207\346\243\200\346\265\213/deep_learning_object_detection.md" @@ -0,0 +1,152 @@ +# opencv实现目标检测功能 + +早在 2017 年 8 月，OpenCV 3.3 正式发布，带来了高度改进的“深度神经网络”（dnn）模块。 该模块支持多种深度学习框架，包括 Caffe、TensorFlow 和 Torch/PyTorch。这次我们使用Opencv深度学习的功能实现目标检测的功能，模型选用MobileNetSSD_deploy.caffemodel。 + +我已经实现了模型加载和预测的代码，但是如何将结果取出来并绘制到图片上呢？如下图： + +![output](https://gitee.com/wanghao1090220084/cloud-image/raw/master/output.jpg) + +# 框架代码 + +```Python +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 + + # show the output image + cv2.imshow("Output", image) + cv2.imwrite("output.jpg", image) + cv2.waitKey(0) + + +``` + +# 答案： + +```Python +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) +``` + +# 选项 + +## 宽和高颠倒 + +```Python +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([h, w, h, w]) + (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) +``` + +## box框顺序错误 + +``` +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, endX, startY, 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) +``` + +## 置信度设置成大于 + +``` +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) +``` + -- GitLab