利用Python+opencv模块的dnn实现Faster R-CNN（一）
利用Python+opencv模块的dnn实现Faster R-CNN（一）
- 开发环境
-
- faster r-cnn学习
- -*- coding: utf-8 -*-
- -*- coding: utf-8 -*-
-
- 出现的问题
开发环境
Pycham，opencv（3.4.2）
faster r-cnn学习
opencv实现Faster R-CNN
文章中的代码全部来源于这篇文章，但是运行后出现的错误不知道怎么解决。
Faster R-CNN详解
-- coding: utf-8 --
import cv2
import os
import matplotlib.pyplot as plt
import time
class general_faster_rcnn(object):
def init(self, modelpath):
self.conf_threshold = 0.3 # Confidence threshold
self.nms_threshold = 0.4 # Non-maximum suppression threshold
self.net_width = 416 # 300 # Width of network’s input image
self.net_height = 416 # 300 # Height of network’s input image
self.classes = self.get_coco_names()
self.faster_rcnn_model = self.get_faster_rcnn_model(modelpath)
self.outputs_names = self.get_outputs_names()
def get_coco_names(self):
classes = ["person", "bicycle", "car", "motorcycle", "airplane",
"bus", "train", "truck", "boat", "traffic light",
"fire hydrant", "background", "stop sign", "parking meter",
"bench", "bird", "cat", "dog", "horse", "sheep", "cow",
"elephant", "bear", "zebra", "giraffe", "background",
"backpack", "umbrella", "background", "background",
"handbag", "tie", "suitcase", "frisbee", "skis",
"snowboard", "sports ball", "kite", "baseball bat",
"baseball glove", "skateboard", "surfboard", "tennis racket",
"bottle", "background", "wine glass", "cup", "fork", "knife",
"spoon", "bowl", "banana", "apple", "sandwich", "orange",
"broccoli", "carrot", "hot dog", "pizza", "donut", "cake",
"chair", "couch", "potted plant", "bed", "background",
"dining table", "background", "background", "toilet",
"background", "tv", "laptop", "mouse", "remote", "keyboard",
"cell phone", "microwave", "oven", "toaster", "sink",
"refrigerator", "background", "book", "clock", "vase",
"scissors", "teddy bear", "hair drier", "toothbrush",
"background"]
return classes
def get_faster_rcnn_model(self, modelpath):
pb_file = os.path.join(modelpath, "frozen_inference_graph.pb")
pbtxt_file = os.path.join(modelpath, "graph.pbtxt")
net = cv2.dnn.readNetFromTensorflow(pb_file, pbtxt_file)
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
return net
def get_outputs_names(self):
# 网络中所有网络层的名字
layersNames = self.faster_rcnn_model.getLayerNames()
# 网络输出层的名字，如，没有链接输出的网络层.
return [layersNames[i[0] - 1] for i in \
self.faster_rcnn_model.getUnconnectedOutLayers()]
# NMS 处理掉低 confidence 的边界框.
def postprocess(self, img_cv2, outputs):
img_height, img_width, _ = img_cv2.shape
class_ids = []
confidences = []
boxes = []
for output in outputs:
for detection in output[0, 0]:
# [batch_id, class_id, confidence, left, top, right, bottom]
confidence = detection[2]
if confidence > self.conf_threshold:
left = int(detection[3] * img_width)
top = int(detection[4] * img_height)
right = int(detection[5] * img_width)
bottom = int(detection[6] * img_height)
width = right - left + 1
height = bottom - top + 1
class_ids.append(int(detection[1]))
confidences.append(float(confidence))
boxes.append([left, top, width, height])
# NMS 处理
indices = cv2.dnn.NMSBoxes(boxes,
confidences,
self.conf_threshold,
self.nms_threshold)
results = []
for ind in indices:
res_box = {}
res_box["class_id"] = class_ids[ind[0]]
res_box["score"] = confidences[ind[0]]
box = boxes[ind[0]]
res_box["box"] = (box[0], box[1], box[0] + box[2], box[1] + box[3])
results.append(res_box)
return results
def predict(self, img_file):
img_cv2 = cv2.imread(img_file)
# 创建 4D blob.
blob = cv2.dnn.blobFromImage(
img_cv2,
size=(self.net_width, self.net_height),
swapRB=True, crop=False)
# 设置网络的输入 blob
self.faster_rcnn_model.setInput(blob)
# 打印网络的输出层名
print("[INFO]Net output layers: {}".format(self.outputs_names))
# Runs forward
outputs = self.faster_rcnn_model.forward(self.outputs_names)
# NMS
results = self.postprocess(img_cv2, outputs)
return results
def vis_res(self, img_file, results):
img_cv2 = cv2.imread(img_file)
for result in results:
left, top, right, bottom = result["box"]
cv2.rectangle(img_cv2,
(left, top),
(right, bottom),
(255, 178, 50), 3)
# Get the label for the class name and its confidence
label = '%.2f' % result["score"]
if self.classes:
assert (result["class_id"] < len(self.classes))
label = '%s:%s' % (self.classes[result["class_id"]], label)
label_size, baseline = cv2.getTextSize(
label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
top = max(top, label_size[1])
cv2.rectangle(
img_cv2,
(left, top - round(1.5 * label_size[1])),
(left + round(1.5 * label_size[0]), top + baseline),
(255, 0, 0),
cv2.FILLED)
cv2.putText(img_cv2,
label,
(left, top),
cv2.FONT_HERSHEY_SIMPLEX,
0.75, (0, 0, 0), 1)
t, _ = self.faster_rcnn_model.getPerfProfile()
label = 'Inference time: %.2f ms' % \
(t * 1000.0 / cv2.getTickFrequency())
cv2.putText(img_cv2, label, (0, 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
plt.figure(figsize=(10, 8))
plt.imshow(img_cv2[:, :, ::-1])
plt.title("OpenCV DNN Faster RCNN-ResNet50")
plt.axis("off")
plt.show()
if name == ‘main’:
print("[INFO]Faster RCNN object detection in OpenCV.")
img_file = "test.jpg"
start = time.time()
modelpath = "C:/Users/chenying/PycharmProjects/GraduationDesign/Faster_R_Rcnn/faster_rcnn_resnet50_coco_2018_01_28/"
-- coding: utf-8 --
import cv2
import os
import matplotlib.pyplot as plt
import time
class general_faster_rcnn(object):
def init(self, modelpath):
self.conf_threshold = 0.3 # Confidence threshold
self.nms_threshold = 0.4 # Non-maximum suppression threshold
self.net_width = 416 # 300 # Width of network’s input image
self.net_height = 416 # 300 # Height of network’s input image
self.classes = self.get_coco_names()
self.faster_rcnn_model = self.get_faster_rcnn_model(modelpath)
self.outputs_names = self.get_outputs_names()
def get_coco_names(self):
classes = ["person", "bicycle", "car", "motorcycle", "airplane",
"bus", "train", "truck", "boat", "traffic light",
"fire hydrant", "background", "stop sign", "parking meter",
"bench", "bird", "cat", "dog", "horse", "sheep", "cow",
"elephant", "bear", "zebra", "giraffe", "background",
"backpack", "umbrella", "background", "background",
"handbag", "tie", "suitcase", "frisbee", "skis",
"snowboard", "sports ball", "kite", "baseball bat",
"baseball glove", "skateboard", "surfboard", "tennis racket",
"bottle", "background", "wine glass", "cup", "fork", "knife",
"spoon", "bowl", "banana", "apple", "sandwich", "orange",
"broccoli", "carrot", "hot dog", "pizza", "donut", "cake",
"chair", "couch", "potted plant", "bed", "background",
"dining table", "background", "background", "toilet",
"background", "tv", "laptop", "mouse", "remote", "keyboard",
"cell phone", "microwave", "oven", "toaster", "sink",
"refrigerator", "background", "book", "clock", "vase",
"scissors", "teddy bear", "hair drier", "toothbrush",
"background"]
return classes
def get_faster_rcnn_model(self, modelpath):
pb_file = os.path.join(modelpath, "frozen_inference_graph.pb")
pbtxt_file = os.path.join(modelpath, "graph.pbtxt")
net = cv2.dnn.readNetFromTensorflow(pb_file, pbtxt_file)
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
return net
def get_outputs_names(self):
# 网络中所有网络层的名字
layersNames = self.faster_rcnn_model.getLayerNames()
# 网络输出层的名字，如，没有链接输出的网络层.
return [layersNames[i[0] - 1] for i in \
self.faster_rcnn_model.getUnconnectedOutLayers()]
# NMS 处理掉低 confidence 的边界框.
def postprocess(self, img_cv2, outputs):
img_height, img_width, _ = img_cv2.shape
class_ids = []
confidences = []
boxes = []
for output in outputs:
for detection in output[0, 0]:
# [batch_id, class_id, confidence, left, top, right, bottom]
confidence = detection[2]
if confidence > self.conf_threshold:
left = int(detection[3] * img_width)
top = int(detection[4] * img_height)
right = int(detection[5] * img_width)
bottom = int(detection[6] * img_height)
width = right - left + 1
height = bottom - top + 1
class_ids.append(int(detection[1]))
confidences.append(float(confidence))
boxes.append([left, top, width, height])
# NMS 处理
indices = cv2.dnn.NMSBoxes(boxes,
confidences,
self.conf_threshold,
self.nms_threshold)
results = []
for ind in indices:
res_box = {}
res_box["class_id"] = class_ids[ind[0]]
res_box["score"] = confidences[ind[0]]
box = boxes[ind[0]]
res_box["box"] = (box[0], box[1], box[0] + box[2], box[1] + box[3])
results.append(res_box)
return results
def predict(self, img_file: object) -> object:
img_cv2 = cv2.imread(img_file)
# 创建 4D blob.
blob = cv2.dnn.blobFromImage(
img_cv2,
scalefactor=1,
size=(self.net_width, self.net_height),
swapRB=True,
crop=False)
# 设置网络的输入 blob
self.faster_rcnn_model.setInput(blob)
# 打印网络的输出层名
print("[INFO]Net output layers: {}".format(self.outputs_names))
# Runs forward
outputs = self.faster_rcnn_model.forward(self.outputs_names)
# NMS
results = self.postprocess(img_cv2, outputs)
return results
def vis_res(self, img_file, results):
img_cv2 = cv2.imread(img_file)
for result in results:
left, top, right, bottom = result["box"]
cv2.rectangle(img_cv2,
(left, top),
(right, bottom),
(255, 178, 50), 3)
# Get the label for the class name and its confidence
label = '%.2f' % result["score"]
if self.classes:
assert (result["class_id"] < len(self.classes))
label = '%s:%s' % (self.classes[result["class_id"]], label)
label_size, baseline = cv2.getTextSize(
label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
top = max(top, label_size[1])
cv2.rectangle(
img_cv2,
(left, top - round(1.5 * label_size[1])),
(left + round(1.5 * label_size[0]), top + baseline),
(255, 0, 0),
cv2.FILLED)
cv2.putText(img_cv2,
label,
(left, top),
cv2.FONT_HERSHEY_SIMPLEX,
0.75, (0, 0, 0), 1)
t, _ = self.faster_rcnn_model.getPerfProfile()
label = 'Inference time: %.2f ms' % \
(t * 1000.0 / cv2.getTickFrequency())
cv2.putText(img_cv2, label, (0, 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
plt.figure(figsize=(10, 8))
plt.imshow(img_cv2[:, :, ::-1])
plt.title("OpenCV DNN Faster RCNN-ResNet50")
plt.axis("off")
plt.show()
if name == ‘main’:
print("[INFO]Faster RCNN object detection in OpenCV.")
img_file = "2007_000027.jpg"
print(type(img_file))
start = time.time()
modelpath = "C:/Users/chenying/PycharmProjects/GraduationDesign/Faster_R_Rcnn/faster_rcnn_resnet50_coco_2018_01_28/"
faster_rcnn_model = general_faster_rcnn(modelpath)
print(faster_rcnn_model)
print("[INFO]Model loads time: ", time.time() - start)
start = time.time()
print(img_file)
results = faster_rcnn_model.predict(img_file)
print(type(results))
print("[INFO]Model predicts time: ", time.time() - start)
faster_rcnn_model.vis_res(img_file, results)
faster_rcnn_model = general_faster_rcnn(modelpath)
print("[INFO]Model loads time: ", time.time() - start)
start = time.time()
results = faster_rcnn_model.predict(img_file)
print("[INFO]Model predicts time: ", time.time() - start)
faster_rcnn_model.vis_res(img_file, results)
出现的问题
- 在程序语句：
- results = faster_rcnn_model.predict(img_file)
出现问题：
Traceback (most recent call last):
File “C:/Users/chenying/PycharmProjects/GraduationDesign/Faster_R_Rcnn/faster_rcnn_resnet50_coco_2018_01_28/Faster rcnn.py”, line 361, in
results = faster_rcnn_model.predict(img_file)
File “C:/Users/chenying/PycharmProjects/GraduationDesign/Faster_R_Rcnn/faster_rcnn_resnet50_coco_2018_01_28/Faster rcnn.py”, line 296, in predict
outputs = self.faster_rcnn_model.forward(self.outputs_names)
cv2.error: OpenCV(3.4.2) C:\projects\opencv-python\opencv\modules\dnn\src\layers\permute_layer.cpp:137: error: (-215:Assertion failed) (int)_numAxes == inputs[0].size() in function ‘cv::dnn::PermuteLayerImpl::getMemoryShapes’
头大 啊 ！！！！！（第一次写，很没有耐心，临近毕业，愁死了）