import cv2
import numpy as np

def random_colors(N):
    np.random.seed(1)
    colors = [tuple(255 * np.random.rand(3)) for _ in range(N)]
    return colors

def apply_mask(image, mask, color, alpha=0.5):
    for n, c in enumerate(color):
        image[:, :, n] = np.where(mask == 1, image[:, :, n] * (1 - alpha) + alpha * c, image[:, :, n])
    return image

def display_instances(image, boxes, masks, ids, names, scores):
    n_instances = boxes.shape[0]
    if not n_instances:
        print("No Instances to Display!")
    else:
        assert boxes.shape[0] == masks.shape[-1] == ids.shape[0]

    colors = random_colors(n_instances)
    height, width = image.shape[:2]

    for i, color in enumerate(colors):
        if not np.any(boxes[i]):
            continue
        
        y1, x1, y2, x2 = boxes[i]
        mask = masks[:, :, i]
        image = apply_mask(image, mask, color)
        image = cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)

        label = names[ids[i]]
        score = scores[i] if scores is not None else None
        caption = '{} {:.2f}'.format(label, score) if score else label
        image = cv2.putText(image, caption, (x1, y1), cv2.FONT_HERSHEY_COMPLEX, 0.7, color, 2)

    return image

if __name__ == '__main__':
    import os
    import sys
    import random
    import math
    import time

    ROOT_DIR = os.getcwd()
    MODEL_DIR = os.path.join(ROOT_DIR, "logs")
    COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")

    sys.path.append(os.path.join(ROOT_DIR, "samples/coco/"))
    import coco
    import mrcnn.utils
    import mrcnn.model as modellib

    

    if not os.path.exists(COCO_MODEL_PATH):
        utils.download_trained_weights(COCO_MODEL_PATH)

    class InferenceConfig(coco.CocoConfig):
        GPU_COUNT = 1
        IMAGES_PER_GPU = 1

    config = InferenceConfig()
    config.display()


    model = modellib.MaskRCNN(model="inference", model_dir=MODEL_DIR, config=config)
    model.load_weights(COCO_MODEL_PATH, by_name=True)

    class_names = [
        'BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',
               'bus', 'train', 'truck', 'boat', 'traffic light',
               'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird',
               'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear',
               'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
               'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
               'kite', 'baseball bat', 'baseball glove', 'skateboard',
               'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
               'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
               'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
               'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
               'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
               'keyboard', 'cell phone', 'microwave', 'oven', 'toaster',
                'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors',
               'teddy bear', 'hair drier', 'toothbrush'
               ]

    capture = cv2.VideoCapture(0)
    capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)
    capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)

    while True:
        ret, frame = capture.read()
        results = model.detect([frame], verbose=0)
        r = results[0]

        frame = display_instances(
            frame, r['rois', r['masks'], r['class_ids'], class_names, r['scores']]
        )

        cv2.imshow('frame', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
        
    capturecv2.destroyAllWindows()