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import imutils
import pickle
from tensorflow.keras.models import load_model
def buka(title, image):
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.imshow(image)
plt.title(title)
plt.grid(False)
plt.axis("off")
plt.show()
print("loading model....")
model = load_model('/content/bananafreshness/pisang.model')
mlb = pickle.loads(open('/content/bananafreshness/pisang.pickle', "rb").read())
# load the image
image = cv2.imread("/content/pisang-busuk-sebagian_20171004_085005.jpg")
output = imutils.resize(image, width=400)
# pre-process the image for classification
image = cv2.resize(image, (94, 94))
image = image.astype("float") / 255.0
image = img_to_array(image)
image = np.expand_dims(image, axis=0)
proba = model.predict(image)[0]
idxs = np.argsort(proba)[::-1][:2]
for (i, j) in enumerate(idxs):
label = "{}: {:.2f}%".format(mlb.classes_[j], proba[j] * 100)
cv2.putText(output, label, (10, (i * 30) + 25),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
for (label, p) in zip(mlb.classes_, proba):
print("{}: {:.2f}%".format(label, p * 100))
buka("Output", output) |