i'm currently working saking personal project, simple object recognition in python na nakaka detect ng cup or bottle, gusto ko sanan forward yung output ng prediction model ko sa webpage, para dun ko na lang idisplay yung output , for example gaya ng sa heat sensor na nakaka display ng temperature sa webpage pag may heat na nadedect , pahelp naman po kung sino marunong, your help will be very much appreciated, thank you.
this is the source code for my python cup and bottle recognition:
import cv2
import tensorflow.keras as keras
import numpy as np
np.set_printoptions(suppress=True)
webcam = cv2.VideoCapture(0)
model = keras.models.load_model('keras_model.h5')
data_for_model = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
def load_labels(path):
f = open(path, 'r')
lines = f.readlines()
labels = []
for line in lines:
labels.append(line.split(' ')[1].strip('\n'))
return labels
label_path = 'labels.txt'
labels = load_labels(label_path)
print(labels)
def image_resize(image, height, inter=cv2.INTER_AREA):
dim = None
(h, w) = image.shape[:2]
r = height / float(h)
dim = (int(w * r), height)
resized = cv2.resize(image, dim, interpolation=inter)
return resized
def cropTo(img):
size = 224
height, width = img.shape[:2]
sideCrop = (width - 224) // 2
return img[:, sideCropwidth - sideCrop)]
while True:
ret, img = webcam.read()
if ret:
img = image_resize(img, height=224)
img = cropTo(img)
img = cv2.flip(img, 1)
normalized_img = (img.astype(np.float32) / 127.0) - 1
data_for_model[0] = normalized_img
prediction = model.predict(data_for_model)
for i in range(0, len(prediction[0])):
print('{}: {}'.format(labels, prediction[0]))
cv2.imshow('webcam', img)
if cv2.waitKey(1) == 27:
break
cv2.destroyAllWindows()
eto naman po young sample output na gusto kong display as webpage, gusto ko po sana kada may madetec na cup or bottle, mag didisplay din sa webpage:
bottle: 0.9784454107284546
cup: 0.04396909475326538
bottle: 0.9560309052467346
cup: 0.04825812205672264
bottle: 0.9517418742179871
cup: 0.01662440039217472
bottle: 0.983375608921051
this is the source code for my python cup and bottle recognition:
import cv2
import tensorflow.keras as keras
import numpy as np
np.set_printoptions(suppress=True)
webcam = cv2.VideoCapture(0)
model = keras.models.load_model('keras_model.h5')
data_for_model = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
def load_labels(path):
f = open(path, 'r')
lines = f.readlines()
labels = []
for line in lines:
labels.append(line.split(' ')[1].strip('\n'))
return labels
label_path = 'labels.txt'
labels = load_labels(label_path)
print(labels)
def image_resize(image, height, inter=cv2.INTER_AREA):
dim = None
(h, w) = image.shape[:2]
r = height / float(h)
dim = (int(w * r), height)
resized = cv2.resize(image, dim, interpolation=inter)
return resized
def cropTo(img):
size = 224
height, width = img.shape[:2]
sideCrop = (width - 224) // 2
return img[:, sideCropwidth - sideCrop)]
while True:
ret, img = webcam.read()
if ret:
img = image_resize(img, height=224)
img = cropTo(img)
img = cv2.flip(img, 1)
normalized_img = (img.astype(np.float32) / 127.0) - 1
data_for_model[0] = normalized_img
prediction = model.predict(data_for_model)
for i in range(0, len(prediction[0])):
print('{}: {}'.format(labels, prediction[0]))
cv2.imshow('webcam', img)
if cv2.waitKey(1) == 27:
break
cv2.destroyAllWindows()
eto naman po young sample output na gusto kong display as webpage, gusto ko po sana kada may madetec na cup or bottle, mag didisplay din sa webpage:
bottle: 0.9784454107284546
cup: 0.04396909475326538
bottle: 0.9560309052467346
cup: 0.04825812205672264
bottle: 0.9517418742179871
cup: 0.01662440039217472
bottle: 0.983375608921051