import cv2
from darkflow.net.build import TFNet
import matplotlib.pyplot as plt
import numpy as np
import sys

sys.path.append('car_detect/')
options = {
    'model': 'cfg/yolo.cfg',
    'load': 'bin/yolo.weights',
    'threshold': 0.3,
    'gpu': 1.0
}

tfnet = TFNet(options)

def detect(img):

    img = np.array(img)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    # use YOLO to predict the image
    result = tfnet.return_predict(img)

    tl = (result[0]['topleft']['x'], result[0]['topleft']['y'])
    br = (result[0]['bottomright']['x'], result[0]['bottomright']['y'])
    label = result[0]['label']


    # add the box and label and display it
    img = cv2.rectangle(img, tl, br, (0, 255, 0), 7)
    img = cv2.putText(img, label, tl, cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 0), 2)
    return img

349a4940ed3bbe2edaa3bb98b9816f66c72a9ed8
<mask token>


def detect(img):
    img = np.array(img)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    result = tfnet.return_predict(img)
    tl = result[0]['topleft']['x'], result[0]['topleft']['y']
    br = result[0]['bottomright']['x'], result[0]['bottomright']['y']
    label = result[0]['label']
    img = cv2.rectangle(img, tl, br, (0, 255, 0), 7)
    img = cv2.putText(img, label, tl, cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 0), 2
        )
    return img

<mask token>
sys.path.append('car_detect/')
<mask token>


def detect(img):
    img = np.array(img)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    result = tfnet.return_predict(img)
    tl = result[0]['topleft']['x'], result[0]['topleft']['y']
    br = result[0]['bottomright']['x'], result[0]['bottomright']['y']
    label = result[0]['label']
    img = cv2.rectangle(img, tl, br, (0, 255, 0), 7)
    img = cv2.putText(img, label, tl, cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 0), 2
        )
    return img

<mask token>
sys.path.append('car_detect/')
options = {'model': 'cfg/yolo.cfg', 'load': 'bin/yolo.weights', 'threshold':
    0.3, 'gpu': 1.0}
tfnet = TFNet(options)


def detect(img):
    img = np.array(img)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    result = tfnet.return_predict(img)
    tl = result[0]['topleft']['x'], result[0]['topleft']['y']
    br = result[0]['bottomright']['x'], result[0]['bottomright']['y']
    label = result[0]['label']
    img = cv2.rectangle(img, tl, br, (0, 255, 0), 7)
    img = cv2.putText(img, label, tl, cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 0), 2
        )
    return img

import cv2
from darkflow.net.build import TFNet
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.append('car_detect/')
options = {'model': 'cfg/yolo.cfg', 'load': 'bin/yolo.weights', 'threshold':
    0.3, 'gpu': 1.0}
tfnet = TFNet(options)


def detect(img):
    img = np.array(img)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    result = tfnet.return_predict(img)
    tl = result[0]['topleft']['x'], result[0]['topleft']['y']
    br = result[0]['bottomright']['x'], result[0]['bottomright']['y']
    label = result[0]['label']
    img = cv2.rectangle(img, tl, br, (0, 255, 0), 7)
    img = cv2.putText(img, label, tl, cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 0), 2
        )
    return img

import cv2
from darkflow.net.build import TFNet
import matplotlib.pyplot as plt
import numpy as np
import sys

sys.path.append('car_detect/')
options = {
    'model': 'cfg/yolo.cfg',
    'load': 'bin/yolo.weights',
    'threshold': 0.3,
    'gpu': 1.0
}

tfnet = TFNet(options)

def detect(img):

    img = np.array(img)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    # use YOLO to predict the image
    result = tfnet.return_predict(img)

    tl = (result[0]['topleft']['x'], result[0]['topleft']['y'])
    br = (result[0]['bottomright']['x'], result[0]['bottomright']['y'])
    label = result[0]['label']


    # add the box and label and display it
    img = cv2.rectangle(img, tl, br, (0, 255, 0), 7)
    img = cv2.putText(img, label, tl, cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 0), 2)
    return img
