Initial commit

README.md

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+# Cat Fountain

TFLite_model/labelmap.txt

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+???
+person
+bicycle
+car
+motorcycle
+airplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+???
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+???
+backpack
+umbrella
+???
+???
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+???
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+couch
+potted plant
+bed
+???
+dining table
+???
+???
+toilet
+???
+tv
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+???
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush

main.py

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+import os
+import cv2
+import numpy as np
+import importlib.util
+from threading import Thread
+import time
+
+IM_WIDTH = 1280
+IM_HEIGHT = 720
+
+camera_type = 'usb'
+
+MODEL_NAME = 'TFLite_model'
+PATH_TO_CKPT = os.path.join(os.getcwd(), MODEL_NAME, 'detect.tflite')
+PATH_TO_LABELS = os.path.join(os.getcwd(), MODEL_NAME, 'labelmap.txt')
+min_conf_threshold = 0.5
+NUM_CLASSES = 90
+
+# Import TensorFlow libraries
+# If tflite_runtime is installed, import interpreter from tflite_runtime, else import from regular tensorflow
+pkg = importlib.util.find_spec('tflite_runtime')
+if pkg:
+    from tflite_runtime.interpreter import Interpreter
+else:
+    from tensorflow.lite.python.interpreter import Interpreter
+
+# Load the label map
+with open(PATH_TO_LABELS, 'r') as f:
+    labels = [line.strip() for line in f.readlines()]
+
+if labels[0] == '???':
+    del(labels[0])
+
+interpreter = Interpreter(model_path=PATH_TO_CKPT)
+interpreter.allocate_tensors()
+
+# Get model details
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+height = input_details[0]['shape'][1]
+width = input_details[0]['shape'][2]
+resW=1280
+resH=720
+
+floating_model = (input_details[0]['dtype'] == np.float32)
+
+class VideoStream:
+    """Camera object that controls video streaming from the Picamera"""
+    def __init__(self,resolution=(640,480),framerate=30):
+        # Initialize the PiCamera and the camera image stream
+        self.stream = cv2.VideoCapture(0)
+        ret = self.stream.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))
+        ret = self.stream.set(3,resolution[0])
+        ret = self.stream.set(4,resolution[1])
+            
+        # Read first frame from the stream
+        (self.grabbed, self.frame) = self.stream.read()
+
+	# Variable to control when the camera is stopped
+        self.stopped = False
+
+    def start(self):
+	# Start the thread that reads frames from the video stream
+        Thread(target=self.update,args=()).start()
+        return self
+
+    def update(self):
+        # Keep looping indefinitely until the thread is stopped
+        while True:
+            # If the camera is stopped, stop the thread
+            if self.stopped:
+                # Close camera resources
+                self.stream.release()
+                return
+
+            # Otherwise, grab the next frame from the stream
+            (self.grabbed, self.frame) = self.stream.read()
+
+    def read(self):
+	# Return the most recent frame
+        return self.frame
+
+    def stop(self):
+	# Indicate that the camera and thread should be stopped
+        self.stopped = True
+
+input_mean = 127.5
+input_std = 127.5
+
+def pet_detector(frame, detection_time):
+    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    frame_resized = cv2.resize(frame_rgb, (width, height))
+    input_data = np.expand_dims(frame_resized, axis=0)
+    if floating_model:
+        input_data = (np.float32(input_data) - input_mean) / input_std
+
+    interpreter.set_tensor(input_details[0]['index'], input_data)
+    interpreter.invoke()
+
+    # Retrieve detection results
+    #boxes = interpreter.get_tensor(output_details[0]['index'])[0] # Bounding box coordinates of detected objects
+    classes = interpreter.get_tensor(output_details[1]['index'])[0] # Class index of detected objects
+    scores = interpreter.get_tensor(output_details[2]['index'])[0] # Confidence of detected objects
+
+    for i in range(len(scores)):
+        if ((scores[i] > min_conf_threshold) and (scores[i] <= 1.0)):
+            obj_name = labels[int(classes[i])]
+            #print(obj_name)
+            if obj_name == 'cat' or obj_name == 'teddy bear':
+                detection_time += 1
+            else:
+                detection_time = 0
+            print(detection_time)   
+
+    return frame, detection_time
+
+videostream = VideoStream(resolution=(resW, resH), framerate=30).start()
+time.sleep(1)
+
+detection_time = 0
+while(True):
+    frame = videostream.read()
+    frame, detection_time = pet_detector(frame, detection_time)
+    cv2.imshow('Cat-detector', frame)
+
+    if cv2.waitKey(1) == ord('q'):
+        break
+
+cv2.destroyAllWindows()
+videostream.stop()