import cv2

def detect(input, output):
    classes = {0: 'background',
                1: 'person', 2: 'bicycle', 3: 'car', 4: 'motorcycle', 5: 'airplane', 6: 'bus',
                7: 'train', 8: 'truck', 9: 'boat', 10: 'traffic light', 11: 'fire hydrant',
                13: 'stop sign', 14: 'parking meter', 15: 'bench', 16: 'bird', 17: 'cat',
                18: 'dog', 19: 'horse', 20: 'sheep', 21: 'cow', 22: 'elephant', 23: 'bear',
                24: 'zebra', 25: 'giraffe', 27: 'backpack', 28: 'umbrella', 31: 'handbag',
                32: 'tie', 33: 'suitcase', 34: 'frisbee', 35: 'skis', 36: 'snowboard',
                37: 'sports ball', 38: 'kite', 39: 'baseball bat', 40: 'baseball glove',
                41: 'skateboard', 42: 'surfboard', 43: 'tennis racket', 44: 'bottle',
                46: 'wine glass', 47: 'cup', 48: 'fork', 49: 'knife', 50: 'spoon',
                51: 'bowl', 52: 'banana', 53: 'apple', 54: 'sandwich', 55: 'orange',
                56: 'broccoli', 57: 'carrot', 58: 'hot dog', 59: 'pizza', 60: 'donut',
                61: 'cake', 62: 'chair', 63: 'couch', 64: 'potted plant', 65: 'bed',
                67: 'dining table', 70: 'toilet', 72: 'tv', 73: 'laptop', 74: 'mouse',
                75: 'remote', 76: 'keyboard', 77: 'cell phone', 78: 'microwave', 79: 'oven',
                80: 'toaster', 81: 'sink', 82: 'refrigerator', 84: 'book', 85: 'clock',
                86: 'vase', 87: 'scissors', 88: 'teddy bear', 89: 'hair drier', 90: 'toothbrush'}

    # Load a model imported from Tensorflow
    tensorflowNet = cv2.dnn.readNetFromTensorflow('./model/frozen_inference_graph.pb', './model/graph.pbtxt')

    # Input image
    img = cv2.imread(input)
    rows, cols, channels = img.shape

    # Use the given image as input, which needs to be blob(s).
    tensorflowNet.setInput(cv2.dnn.blobFromImage(img, size=(300, 300), swapRB=True, crop=False))

    # Runs a forward pass to compute the net output
    networkOutput = tensorflowNet.forward()

    # Loop on the outputs
    for detection in networkOutput[0,0]:
        score = float(detection[2])
        if score > 0.2:
            left = detection[3] * cols
            top = detection[4] * rows
            right = detection[5] * cols
            bottom = detection[6] * rows
    
            #draw a red rectangle around detected objects
            cv2.rectangle(img, (int(left), int(top)), (int(right), int(bottom)), (0, 0, 255), thickness=2)
            #draw category name in top left of rectangle
            cv2.putText(img, classes[int(detection[1])], (int(left), int(top-4)), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 2, 8)

    cv2.imwrite(output, img)
    # Show the image with a rectagle surrounding the detected objects