forest.py 文件源码

def predict(url):
    global model, COOKED_PHRASES, RAW_PHRASES   
    # Read image
    image = io.imread(url)
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    image = cv2.resize(image, (500, 500), interpolation=cv2.INTER_CUBIC)    

    # Use otsu to mask
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    ret, mask = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
    mask = cv2.medianBlur(mask, 5)

    # Get features
    features = describe(image, mask)

    # Predict it
    result = model.predict([features])
    probability = model.predict_proba([features])[0][result][0]        
    state = le.inverse_transform(result)[0]

    phrase = ''

    if 'cook' in state:
        phrase = COOKED_PHRASES[int(random.random()*len(COOKED_PHRASES))]
    elif 'raw' in state:
        phrase = RAW_PHRASES[int(random.random()*len(RAW_PHRASES))]

    return {'type': state, 'confidence': probability, 'phrase': phrase}