I've been able to detect custom object which was trained by teachable machines. I used RPi Easy Object Detection - La Croix Flavor Detector tutorial to detect the object. I'm looking to get the center coordinates of the detected model. Searched in many sites and couldn't find a solution yet.
My code:
import multiprocessing
import numpy as np
import cv2
import tensorflow.keras as tf
import pyttsx3
import math
import os
# use matplotlib if cv2.imshow() doesn't work
# import matplotlib.pyplot as plt
DIR_PATH = os.path.dirname(os.path.realpath(__file__))
# this process is purely for text-to-speech so it doesn't hang processor
def speak(speakQ, ):
# initialize text-to-speech object
engine = pyttsx3.init()
# can adjust volume if you'd like
volume = engine.getProperty('volume')
engine.setProperty('volume', volume) # add number here
# initialize last_msg to be empty
last_msg = ""
# keeps program running forever until ctrl+c or window is closed
while True:
msg = speakQ.get()
# clear out msg queue to get most recent msg
while not speakQ.empty():
msg = speakQ.get()
# if most recent msg is different from previous msg
# and if it's not "Background"
if msg != last_msg and msg != "Background":
last_msg = msg
# text-to-speech say class name from labels.txt
engine.say(msg)
engine.runAndWait()
if msg == "Background":
last_msg = ""
def main():
# read .txt file to get labels
labels_path = f"{DIR_PATH}/la_croix_model/labels.txt"
# open input file label.txt
labelsfile = open(labels_path, 'r')
# initialize classes and read in lines until there are no more
classes = []
line = labelsfile.readline()
while line:
# retrieve just class name and append to classes
classes.append(line.split(' ', 1)[1].rstrip())
line = labelsfile.readline()
# close label file
labelsfile.close()
# load the teachable machine model
model_path = f"{DIR_PATH}/la_croix_model/keras_model.h5"
model = tf.models.load_model(model_path, compile=False)
# initialize webcam video object
cap = cv2.VideoCapture(0)
# width & height of webcam video in pixels -> adjust to your size
# adjust values if you see black bars on the sides of capture window
frameWidth = 1024
frameHeight = 768
# set width and height in pixels
cap.set(cv2.CAP_PROP_FRAME_WIDTH, frameWidth)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, frameHeight)
# enable auto gain
cap.set(cv2.CAP_PROP_GAIN, 0)
# creating a queue to share data to speech process
speakQ = multiprocessing.Queue()
# creating speech process to not hang processor
p1 = multiprocessing.Process(target=speak, args=(speakQ, ), daemon="True")
# starting process 1 - speech
p1.start()
# keeps program running forever until ctrl+c or window is closed
while True:
# disable scientific notation for clarity
np.set_printoptions(suppress=True)
# Create the array of the right shape to feed into the keras model.
# We are inputting 1x 224x224 pixel RGB image.
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
# capture image
check, frame = cap.read()
# mirror image - mirrored by default in Teachable Machine
# depending upon your computer/webcam, you may have to flip the video
# frame = cv2.flip(frame, 1)
# crop to square for use with TM model
margin = int(((frameWidth-frameHeight)/2))
square_frame = frame[0:frameHeight, margin:margin + frameHeight]
# resize to 224x224 for use with TM model
resized_img = cv2.resize(square_frame, (224, 224))
# convert image color to go to model
model_img = cv2.cvtColor(resized_img, cv2.COLOR_BGR2RGB)
# turn the image into a numpy array
image_array = np.asarray(model_img)
# normalize the image
normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
# load the image into the array
data[0] = normalized_image_array
# run the prediction
predictions = model.predict(data)
# confidence threshold is 90%.
conf_threshold = 90
confidence = []
conf_label = ""
threshold_class = ""
# create blach border at bottom for labels
per_line = 2 # number of classes per line of text
bordered_frame = cv2.copyMakeBorder(
square_frame,
top=0,
bottom=30 + 15*math.ceil(len(classes)/per_line),
left=0,
right=0,
borderType=cv2.BORDER_CONSTANT,
value=[0, 0, 0]
)
# for each one of the classes
for i in range(0, len(classes)):
# scale prediction confidence to % and apppend to 1-D list
confidence.append(int(predictions[0][i]*100))
# put text per line based on number of classes per line
if (i != 0 and not i % per_line):
cv2.putText(
img=bordered_frame,
text=conf_label,
org=(int(0), int(frameHeight+25+15*math.ceil(i/per_line))),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=0.5,
color=(255, 255, 255)
)
conf_label = ""
# append classes and confidences to text for label
conf_label += classes[i] + ": " + str(confidence[i]) + "%; "
# prints last line
if (i == (len(classes)-1)):
cv2.putText(
img=bordered_frame,
text=conf_label,
org=(int(0), int(frameHeight+25+15*math.ceil((i+1)/per_line))),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=0.5,
color=(255, 255, 255)
)
conf_label = ""
# if above confidence threshold, send to queue
if confidence[i] > conf_threshold:
speakQ.put(classes[i])
threshold_class = classes[i]
# add label class above confidence threshold
cv2.putText(
img=bordered_frame,
text=threshold_class,
org=(int(0), int(frameHeight+20)),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=0.75,
color=(255, 255, 255)
)
# original video feed implementation
cv2.imshow("Capturing", bordered_frame)
cv2.waitKey(10)
# # if the above implementation doesn't work properly
# # comment out two lines above and use the lines below
# # will also need to import matplotlib at the top
# plt_frame = cv2.cvtColor(bordered_frame, cv2.COLOR_BGR2RGB)
# plt.imshow(plt_frame)
# plt.draw()
# plt.pause(.001)
# terminate process 1
p1.terminate()
if __name__ == '__main__':
main()
Looking for your support. Thanks.
