class CameraTest():
def __init__(self):
self.__camera = Camera(PiCamera())
time.sleep(0.2)
def testVideoFeed(self):
i = 0
print(
"check to make sure the camera is capturing the images you want.")
for frames in self.__camera.captureContinousFeed():
image = frames.array[:300][:300]
#if image is inverted you can eliminate this line of code
image = cv2.flip(image, 0)
cv2.imshow("myImage", image)
cv2.waitKey(0)
i = i + 1
if (i == 4):
break
self.__camera.truncate()
def testCaptureImage(self):
print("check to make sure the camera is capturing the one image.")
frame = self.__camera.captureOneImage()
image = frame[:300][:300]
#if image is inverted you can eliminate this line of code
image = cv2.flip(image, 0)
cv2.imshow("myImage", image)
cv2.waitKey(0)
self.__camera.truncate()
class PetEntertainer():
__objectStrategies = {}
__openedLED = LED(14)
def __init__(self):
self.__network = cv2.dnn.readNetFromTensorflow(
'../Network/frozenInferenceGraph.pb',
'../Network/persistedNetwork.pbtxt')
self.__motors = Motors(Servo(20), Servo(21))
self.__objectStrategies["dog"] = DogStrategy(self.__motors,
self.__openedLED)
self.__objectStrategies["person"] = HumanStrategy()
self.__objectStrategies["cat"] = CatStrategy(self.__motors,
self.__openedLED)
self.__camera = Camera(PiCamera())
# All the possible outputs of the neural network
self.__classifications = {
0: 'background',
1: 'person',
2: 'bicycle',
3: 'car',
4: 'motorcycle',
5: 'airplane',
6: 'bus',
7: 'train',
8: 'truck',
9: 'boat',
10: 'traffic light',
11: 'fire hydrant',
13: 'stop sign',
14: 'parking meter',
15: 'bench',
16: 'bird',
17: 'cat',
18: 'dog',
19: 'horse',
20: 'sheep',
21: 'cow',
22: 'elephant',
23: 'bear',
24: 'zebra',
25: 'giraffe',
27: 'backpack',
28: 'umbrella',
31: 'handbag',
32: 'tie',
33: 'suitcase',
34: 'frisbee',
35: 'skis',
36: 'snowboard',
37: 'sports ball',
38: 'kite',
39: 'baseball bat',
40: 'baseball glove',
41: 'skateboard',
42: 'surfboard',
43: 'tennis racket',
44: 'bottle',
46: 'wine glass',
47: 'cup',
48: 'fork',
49: 'knife',
50: 'spoon',
51: 'bowl',
52: 'banana',
53: 'apple',
54: 'sandwich',
55: 'orange',
56: 'broccoli',
57: 'carrot',
58: 'hot dog',
59: 'pizza',
60: 'donut',
61: 'cake',
62: 'chair',
63: 'couch',
64: 'potted plant',
65: 'bed',
67: 'dining table',
70: 'toilet',
72: 'tv',
73: 'laptop',
74: 'mouse',
75: 'remote',
76: 'keyboard',
77: 'cell phone',
78: 'microwave',
79: 'oven',
80: 'toaster',
81: 'sink',
82: 'refrigerator',
84: 'book',
85: 'clock',
86: 'vase',
87: 'scissors',
88: 'teddy bear',
89: 'hair drier',
90: 'toothbrush'
}
# @imageFile: this is the location of the image that we would like to read.
# Runs the neural network on a specific image.
def runOnImage(self, imageFile):
image = cv2.imread(imageFile)
image = cv2.resize(image, (300, 300))
if (not image is None):
self.__network.setInput(
cv2.dnn.blobFromImage(image, size=(300, 300), swapRB=True))
output = self.__network.forward()
#threshold our outputs
listOfObjects = [
detection for detection in output[0, 0, :, :]
if detection[2] > .75
]
for myObject in listOfObjects:
objectClassification = self.__classifications[myObject[1]]
print("looking at : " + objectClassification + " with " +
str(myObject[2]) + " certainty")
if objectClassification in self.__objectStrategies.keys():
self.__objectStrategies[objectClassification].run()
# This is the driver for the Petentertainer, it gets a video feed from
# the camera and inputs into the neural network.
# We have 3 classes currently that we do something for, people, Dogs,
# and Cats.
def run(self):
#make sure the camera is warmed up
time.sleep(1)
try:
for frame in self.__camera.captureContinousFeed():
# truncate the 4 frames extra around the image to ensure
# we have the same number of frames as inputs into the
# convoluted network
image = frame.array[:300][:300]
# Depending on the positions of your camera you can eliminate
# this line of code below. My camera was inverted, so
# we need to flip the image.
image = cv2.flip(image, 0)
#cv2.imshow("what are we looking at",image)
#cv2.waitKey(0)
if (not image is None):
self.__network.setInput(
cv2.dnn.blobFromImage(image,
size=(300, 300),
swapRB=True))
output = self.__network.forward()
#threshold our outputs
listOfObjects = [
detection for detection in output[0, 0, :, :]
if detection[2] > .75
]
for myObject in listOfObjects:
objectClassification = self.__classifications[
myObject[1]]
print("looking at : " + objectClassification +
" with " + str(myObject[2]) + " certainty")
if objectClassification in self.__objectStrategies.keys(
):
self.__objectStrategies[objectClassification].run()
self.__camera.truncate()
# give the camera a second to be ready for the next image.
# This helps to reduce blur in the image.
time.sleep(.3)
#captures the keyboard interupt and moves the camera back into position
except KeyboardInterrupt:
self.__openedLED.off()
self.__motors.moveMiddle()
pass
