質問編集履歴
5
修正
test
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test
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@@ -40,7 +40,7 @@
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inpHeight = 416
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classesFile = "/realsense_yolo_v3_2d/coco.names"
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classesFile = "/home/limlab/realsense_yolo_v3_2d/coco.names"
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# Configure depth and color streams
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@@ -68,6 +68,8 @@
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self.j = 0
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def getOutputsNames(self,net):
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layersNames = net.getLayerNames()
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#検出
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def drawPredicted(self,classId, conf, left, top,
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def drawPredicted(self,classId, conf, left, top,right, bottom, frame,x,y):
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#cv2.rectangle(frame, (left,top), (right,bottom), (255,0,0),3)#囲み
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#cv2.circle(frame,(x,y),radius=1,color=(0,255,0), thickness=5)#・
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classes = None
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with open(classesFile, "rt") as f:
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classes = f.read().rstrip('\n').split('\n')
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cv2.rectangle(frame, (left,top), (right,bottom), (255,0,0),3)#囲み
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cv2.circle(frame,(x,y),radius=1,color=(0,255,0), thickness=5)#・
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label = '%.2f' % conf
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if classes:
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assert(classId < len(classes))
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label = '%s' %(classes[classId])
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cv2.putText(frame, label,(left,top-5), cv2.FONT_HERSHEY_SIMPLEX,0.75,(255,255,0),2)
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global first#グローバル変数firstを定義
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first = True #実行一回目がTrueの場合
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if first == True:
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path = '/home/limlab/realsense_yolo_v3_2d/image/'
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if label == 'bottle':
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#labelがbottleの場合
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self.j += 1
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if self.j == 1:#def__init__()で0と定義している、0の場合実行
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cv2.imwrite(path + 'image1.png',frame)
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print('ペットボトルを検出しました')
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playsound("/home/limlab/programs/bottle.mp3")
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self.j += 1#iの変数が１になる
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sys.exit()
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if label == 'mouse':
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self.j += 1
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if self.j == 1:
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cv2.imwrite(path + 'image1.png',frame)
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print('マウスを検出しました')
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playsound("/home/limlab/programs/mouse.mp3")
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self.j += 1
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sys.exit()
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labelSize= cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
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top = max(top, labelSize[1])
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cv2.putText(frame, label,(left,top-5), cv2.FONT_HERSHEY_SIMPLEX,0.75,(0,255,0),2)
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def process_detection(self,frame, outs):
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frameHeight = frame.shape[0]
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frameWidth = frame.shape[1]
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classIds = []
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confidences = []
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boxes = []
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for out in outs:
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for detection in out:
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scores = detection[5:]
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classId = np.argmax(scores)
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confidence = scores[classId]
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if confidence > confThreshold:
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center_x = int(detection[0]*frameWidth)
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center_y = int(detection[1]*frameHeight)
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width = int(detection[2]*frameWidth)
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height = int(detection[3]*frameHeight)
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left = int(center_x - width/2)
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top = int(center_y - height/2)
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classIds.append(classId)
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indices = cv2.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
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for i in indices:
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i = i[0]
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box = boxes[i]
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left = box[0]
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top = box[1]
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width = box[2]
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height = box[3]
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x = int(left+width/2)
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y = int(top+ height/2)
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self.drawPredicted(classIds[i], confidences[i], left, top,left+width,top+height,frame,x,y)
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def main():
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cam = CAMDEMO()
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modelConfiguration = "/home/limlab/realsense_yolo_v3_2d/yolov3.cfg"
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modelWeights = "/home/limlab/realsense_yolo_v3_2d/yolov3.weights"
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net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
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net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
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net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
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try:
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while True:
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if first == True:
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path = '/realsense_yolo_v3_2d/image/'
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if label == 'bottle':#labelがbottleの場合
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self.j += 1
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if self.j == 1:
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cv2.imwrite(path + 'image1.png',frame)
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print('ペットボトルを検出しました')
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playsound("/programs/bottle.mp3")
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self.j += 1
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sys.exit()
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if label == 'mouse':
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self.j += 1
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if self.j == 1:
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cv2.imwrite(path + 'image1.png',frame)
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print('マウスを検出しました')
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playsound("/programs/mouse.mp3")
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self.j += 1
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sys.exit()
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#labelSize= cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
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#top = max(top, labelSize[1])
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cv2.putText(frame, label,(left,top-5), cv2.FONT_HERSHEY_SIMPLEX,0.75,(0,255,0),2)
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def process_detection(self,frame, outs):
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frameHeight = frame.shape[0]
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frameWidth = frame.shape[1]
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classIds = []
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confidences = []
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boxes = []
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for out in outs:
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for detection in out:
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scores = detection[5:]
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classId = np.argmax(scores)
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confidence = scores[classId]
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if confidence > confThreshold:
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center_x = int(detection[0]*frameWidth)
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center_y = int(detection[1]*frameHeight)
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width = int(detection[2]*frameWidth)
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height = int(detection[3]*frameHeight)
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left = int(center_x - width/2)
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top = int(center_y - height/2)
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classIds.append(classId)
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indices = cv2.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
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for i in indices:
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i = i[0]
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box = boxes[i]
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left = box[0]
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top = box[1]
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width = box[2]
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height = box[3]
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x = int(left+width/2)
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y = int(top+ height/2)
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self.drawPredicted(classIds[i], confidences[i], left, top,frame,x,y)#5left+width, 6top+height
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def main():
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cam = CAMDEMO()
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classes = None
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with open(classesFile, "rt") as f:
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classes = f.read().rstrip('\n').split('\n')
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modelConfiguration = "/realsense_yolo_v3_2d/yolov3.cfg"
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modelWeights = "/realsense_yolo_v3_2d/yolov3.weights"
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net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
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net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
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net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
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try:
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while True:
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# Wait for a coherent pair of frames: depth and color
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frames = pipeline.wait_for_frames()
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@@ -288,4 +294,6 @@
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```
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4
修正
test
CHANGED
|
File without changes
|
test
CHANGED
|
@@ -40,7 +40,7 @@
|
|
|
40
40
|
|
|
41
41
|
inpHeight = 416
|
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42
42
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43
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-
classesFile = "/
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43
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+
classesFile = "/realsense_yolo_v3_2d/coco.names"
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44
44
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45
45
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# Configure depth and color streams
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46
46
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@@ -224,9 +224,9 @@
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classes = f.read().rstrip('\n').split('\n')
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modelConfiguration = "/
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modelConfiguration = "/realsense_yolo_v3_2d/yolov3.cfg"
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modelWeights = "/
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modelWeights = "/realsense_yolo_v3_2d/yolov3.weights"
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net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
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3
修正
test
CHANGED
|
File without changes
|
test
CHANGED
|
@@ -92,9 +92,7 @@
|
|
|
92
92
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93
93
|
label = '%s' %(classes[classId])
|
|
94
94
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95
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-
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95
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+
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96
|
-
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|
97
|
-
top = max(top, labelSize[1])
|
|
98
96
|
|
|
99
97
|
cv2.putText(frame, label,(left,top-5), cv2.FONT_HERSHEY_SIMPLEX,0.75,(255,255,0),2)
|
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100
98
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2
修正
test
CHANGED
|
File without changes
|
test
CHANGED
|
@@ -76,7 +76,7 @@
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76
76
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77
77
|
#検出
|
|
78
78
|
|
|
79
|
-
def drawPredicted(self,classId, conf, left, top, frame
|
|
79
|
+
def drawPredicted(self,classId, conf, left, top, frame):#right, bottom,x ,y
|
|
80
80
|
|
|
81
81
|
#cv2.rectangle(frame, (left,top), (right,bottom), (255,0,0),3)#囲み
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82
82
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1
修正
test
CHANGED
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File without changes
|
test
CHANGED
|
@@ -18,13 +18,55 @@
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18
18
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19
19
|
```ここに言語名を入力
|
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20
20
|
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21
|
+
import pyrealsense2 as rs
|
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22
|
+
|
|
23
|
+
import numpy as np
|
|
24
|
+
|
|
25
|
+
import cv2
|
|
26
|
+
|
|
27
|
+
import sys
|
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28
|
+
|
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29
|
+
from playsound import playsound
|
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30
|
+
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31
|
+
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|
32
|
+
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33
|
+
# Initialize the parameters
|
|
34
|
+
|
|
35
|
+
confThreshold = 0.5
|
|
36
|
+
|
|
37
|
+
nmsThreshold = 0.4
|
|
38
|
+
|
|
39
|
+
inpWidth = 416
|
|
40
|
+
|
|
41
|
+
inpHeight = 416
|
|
42
|
+
|
|
21
|
-
classesFile = "/realsense_yolo_v3_2d/coco.names"
|
|
43
|
+
classesFile = "/home/limlab/realsense_yolo_v3_2d/coco.names"
|
|
44
|
+
|
|
45
|
+
# Configure depth and color streams
|
|
46
|
+
|
|
47
|
+
pipeline = rs.pipeline()
|
|
48
|
+
|
|
49
|
+
config = rs.config()
|
|
50
|
+
|
|
51
|
+
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
|
|
52
|
+
|
|
53
|
+
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
|
|
54
|
+
|
|
55
|
+
# Start streaming
|
|
56
|
+
|
|
57
|
+
pipeline.start(config)
|
|
58
|
+
|
|
59
|
+
|
|
60
|
+
|
|
61
|
+
|
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22
62
|
|
|
23
63
|
class CAMDEMO:
|
|
24
64
|
|
|
25
65
|
|
|
26
66
|
|
|
27
|
-
|
|
67
|
+
def __init__ (self):
|
|
68
|
+
|
|
69
|
+
self.j = 0
|
|
28
70
|
|
|
29
71
|
def getOutputsNames(self,net):
|
|
30
72
|
|
|
@@ -32,9 +74,13 @@
|
|
|
32
74
|
|
|
33
75
|
return [layersNames[i[0] -1] for i in net.getUnconnectedOutLayers()]
|
|
34
76
|
|
|
35
|
-
|
|
77
|
+
#検出
|
|
36
|
-
|
|
78
|
+
|
|
37
|
-
def drawPredicted(self,classId, conf, left, top, frame):
|
|
79
|
+
def drawPredicted(self,classId, conf, left, top, frame,classes):#right, bottom,x ,y
|
|
80
|
+
|
|
81
|
+
#cv2.rectangle(frame, (left,top), (right,bottom), (255,0,0),3)#囲み
|
|
82
|
+
|
|
83
|
+
#cv2.circle(frame,(x,y),radius=1,color=(0,255,0), thickness=5)#・
|
|
38
84
|
|
|
39
85
|
|
|
40
86
|
|
|
@@ -50,7 +96,123 @@
|
|
|
50
96
|
|
|
51
97
|
top = max(top, labelSize[1])
|
|
52
98
|
|
|
53
|
-
cv2.putText(frame, label,(left,top-5), cv2.FONT_HERSHEY_SIMPLEX,0.75,(255,255,0),2)
|
|
99
|
+
cv2.putText(frame, label,(left,top-5), cv2.FONT_HERSHEY_SIMPLEX,0.75,(255,255,0),2)
|
|
100
|
+
|
|
101
|
+
global first
|
|
102
|
+
|
|
103
|
+
first = True
|
|
104
|
+
|
|
105
|
+
|
|
106
|
+
|
|
107
|
+
if first == True:
|
|
108
|
+
|
|
109
|
+
path = '/realsense_yolo_v3_2d/image/'
|
|
110
|
+
|
|
111
|
+
if label == 'bottle':#labelがbottleの場合
|
|
112
|
+
|
|
113
|
+
self.j += 1
|
|
114
|
+
|
|
115
|
+
if self.j == 1:
|
|
116
|
+
|
|
117
|
+
cv2.imwrite(path + 'image1.png',frame)
|
|
118
|
+
|
|
119
|
+
print('ペットボトルを検出しました')
|
|
120
|
+
|
|
121
|
+
playsound("/programs/bottle.mp3")
|
|
122
|
+
|
|
123
|
+
self.j += 1
|
|
124
|
+
|
|
125
|
+
sys.exit()
|
|
126
|
+
|
|
127
|
+
if label == 'mouse':
|
|
128
|
+
|
|
129
|
+
self.j += 1
|
|
130
|
+
|
|
131
|
+
if self.j == 1:
|
|
132
|
+
|
|
133
|
+
cv2.imwrite(path + 'image1.png',frame)
|
|
134
|
+
|
|
135
|
+
print('マウスを検出しました')
|
|
136
|
+
|
|
137
|
+
playsound("/programs/mouse.mp3")
|
|
138
|
+
|
|
139
|
+
self.j += 1
|
|
140
|
+
|
|
141
|
+
sys.exit()
|
|
142
|
+
|
|
143
|
+
#labelSize= cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
|
|
144
|
+
|
|
145
|
+
#top = max(top, labelSize[1])
|
|
146
|
+
|
|
147
|
+
cv2.putText(frame, label,(left,top-5), cv2.FONT_HERSHEY_SIMPLEX,0.75,(0,255,0),2)
|
|
148
|
+
|
|
149
|
+
|
|
150
|
+
|
|
151
|
+
|
|
152
|
+
|
|
153
|
+
|
|
154
|
+
|
|
155
|
+
|
|
156
|
+
|
|
157
|
+
def process_detection(self,frame, outs):
|
|
158
|
+
|
|
159
|
+
frameHeight = frame.shape[0]
|
|
160
|
+
|
|
161
|
+
frameWidth = frame.shape[1]
|
|
162
|
+
|
|
163
|
+
classIds = []
|
|
164
|
+
|
|
165
|
+
confidences = []
|
|
166
|
+
|
|
167
|
+
boxes = []
|
|
168
|
+
|
|
169
|
+
for out in outs:
|
|
170
|
+
|
|
171
|
+
for detection in out:
|
|
172
|
+
|
|
173
|
+
scores = detection[5:]
|
|
174
|
+
|
|
175
|
+
classId = np.argmax(scores)
|
|
176
|
+
|
|
177
|
+
confidence = scores[classId]
|
|
178
|
+
|
|
179
|
+
if confidence > confThreshold:
|
|
180
|
+
|
|
181
|
+
center_x = int(detection[0]*frameWidth)
|
|
182
|
+
|
|
183
|
+
center_y = int(detection[1]*frameHeight)
|
|
184
|
+
|
|
185
|
+
width = int(detection[2]*frameWidth)
|
|
186
|
+
|
|
187
|
+
height = int(detection[3]*frameHeight)
|
|
188
|
+
|
|
189
|
+
left = int(center_x - width/2)
|
|
190
|
+
|
|
191
|
+
top = int(center_y - height/2)
|
|
192
|
+
|
|
193
|
+
classIds.append(classId)
|
|
194
|
+
|
|
195
|
+
indices = cv2.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
|
|
196
|
+
|
|
197
|
+
for i in indices:
|
|
198
|
+
|
|
199
|
+
i = i[0]
|
|
200
|
+
|
|
201
|
+
box = boxes[i]
|
|
202
|
+
|
|
203
|
+
left = box[0]
|
|
204
|
+
|
|
205
|
+
top = box[1]
|
|
206
|
+
|
|
207
|
+
width = box[2]
|
|
208
|
+
|
|
209
|
+
height = box[3]
|
|
210
|
+
|
|
211
|
+
x = int(left+width/2)
|
|
212
|
+
|
|
213
|
+
y = int(top+ height/2)
|
|
214
|
+
|
|
215
|
+
self.drawPredicted(classIds[i], confidences[i], left, top,frame,x,y)#5left+width, 6top+height
|
|
54
216
|
|
|
55
217
|
|
|
56
218
|
|
|
@@ -64,6 +226,68 @@
|
|
|
64
226
|
|
|
65
227
|
classes = f.read().rstrip('\n').split('\n')
|
|
66
228
|
|
|
229
|
+
modelConfiguration = "/home/limlab/realsense_yolo_v3_2d/yolov3.cfg"
|
|
230
|
+
|
|
231
|
+
modelWeights = "/home/limlab/realsense_yolo_v3_2d/yolov3.weights"
|
|
232
|
+
|
|
233
|
+
net = cv2.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
|
|
234
|
+
|
|
235
|
+
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
|
|
236
|
+
|
|
237
|
+
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
|
|
238
|
+
|
|
239
|
+
try:
|
|
240
|
+
|
|
241
|
+
while True:
|
|
242
|
+
|
|
243
|
+
|
|
244
|
+
|
|
245
|
+
# Wait for a coherent pair of frames: depth and color
|
|
246
|
+
|
|
247
|
+
frames = pipeline.wait_for_frames()
|
|
248
|
+
|
|
249
|
+
color_frame = frames.get_color_frame()
|
|
250
|
+
|
|
251
|
+
if not color_frame:
|
|
252
|
+
|
|
253
|
+
continue
|
|
254
|
+
|
|
255
|
+
# Convert images to numpy arrays
|
|
256
|
+
|
|
257
|
+
color_image = np.asanyarray(color_frame.get_data())
|
|
258
|
+
|
|
259
|
+
blob = cv2.dnn.blobFromImage(color_image, 1/255, (inpWidth, inpHeight), [0,0,0],1,crop=False)
|
|
260
|
+
|
|
261
|
+
net.setInput(blob)
|
|
262
|
+
|
|
263
|
+
outs = net.forward(cam.getOutputsNames(net))
|
|
264
|
+
|
|
265
|
+
# Apply colormap on depth image (image must be converted to 8-bit per pixel first)
|
|
266
|
+
|
|
267
|
+
cam.process_detection(color_image,outs)
|
|
268
|
+
|
|
269
|
+
images = color_image
|
|
270
|
+
|
|
271
|
+
# Show images
|
|
272
|
+
|
|
273
|
+
cv2.imshow('Yolo in RealSense made by Tony', images)
|
|
274
|
+
|
|
275
|
+
if cv2.waitKey(1) & 0xFF == ord('q'):
|
|
276
|
+
|
|
277
|
+
break
|
|
278
|
+
|
|
279
|
+
finally:
|
|
280
|
+
|
|
281
|
+
# Stop streaming
|
|
282
|
+
|
|
283
|
+
pipeline.stop()
|
|
284
|
+
|
|
285
|
+
|
|
286
|
+
|
|
287
|
+
if __name__ == "__main__":
|
|
288
|
+
|
|
289
|
+
main()
|
|
290
|
+
|
|
67
291
|
|
|
68
292
|
|
|
69
293
|
```
|