video_detection.py

"""
Ref: https://github.com/TannerGilbert/MaskRCNN-Object-Detection-and-Segmentation
"""

import os
import sys
import numpy as np

np.random.seed(0)
import matplotlib.pyplot as plt
import cv2
import argparse

# Root directory of the project
ROOT_DIR = os.path.abspath("./")
print(ROOT_DIR)
# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn.config import Config
from mrcnn import utils
import mrcnn.model as modellib
from mrcnn import visualize
from mrcnn.model import log

sys.path.append(os.path.join(ROOT_DIR, "samples/coco/"))
print(sys.path.append(os.path.join(ROOT_DIR, "samples/coco/")))
import coco


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


# based on https://github.com/matterport/Mask_RCNN/blob/master/mrcnn/visualize.py
# and https://github.com/markjay4k/Mask-RCNN-series/blob/887404d990695a7bf7f180e3ffaee939fbd9a1cf/visualize_cv.py
def display_instances(image, boxes, masks, class_ids, class_names, scores=None):
    assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0]

    N = boxes.shape[0]

    colors = colors = [tuple(255 * np.random.rand(3)) for _ in range(N)]

    for i, c in enumerate(colors):
        if not np.any(boxes[i]):
            continue

        y1, x1, y2, x2 = boxes[i]
        label = class_names[class_ids[i]]
        score = scores[i] if scores is not None else None
        caption = "{} {:.3f}".format(label, score) if score else label

        # Mask
        mask = masks[:, :, i]
        image = apply_mask(image, mask, c)
        image = cv2.rectangle(image, (x1, y1), (x2, y2), c, 2)
        image = cv2.putText(
            image, caption, (x1, y1), cv2.FONT_HERSHEY_COMPLEX, 0.7, c, 2
        )
    return image


class InferenceConfig(coco.CocoConfig):
    # Set batch size to 1 since we'll be running inference on
    # one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
    GPU_COUNT = 1
    IMAGES_PER_GPU = 1


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="MaskRCNN Video Object Detection/Instance Segmentation"
    )
    parser.add_argument(
        "-v",
        "--video_path",
        type=str,
        default="",
        help="Path to video. If None camera will be used",
    )
    parser.add_argument(
        "-sp",
        "--save_path",
        type=str,
        default="",
        help="Path to save the output. If None output won't be saved",
    )
    parser.add_argument(
        "-s", "--show", default=True, action="store_false", help="Show output"
    )
    args = parser.parse_args()

    # Directory to save logs and trained model
    MODEL_DIR = os.path.join(ROOT_DIR, "logs")

    # Local path to trained weights file
    COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
    # Download COCO trained weights from Releases if needed
    if not os.path.exists(COCO_MODEL_PATH):
        utils.download_trained_weights(COCO_MODEL_PATH)

    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",
    ]

    config = InferenceConfig()

    # Create model object in inference mode.
    model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)

    # Load weights trained on MS-COCO
    model.load_weights(COCO_MODEL_PATH, by_name=True)

    if args.video_path != "":
        cap = cv2.VideoCapture(args.video_path)
    else:
        cap = cv2.VideoCapture(0)

    if args.save_path:
        width = int(cap.get(3))
        height = int(cap.get(4))
        fps = cap.get(cv2.CAP_PROP_FPS)
        out = cv2.VideoWriter(
            args.save_path,
            cv2.VideoWriter_fourcc("M", "J", "P", "G"),
            fps,
            (width, height),
        )
    while cap.isOpened():
        ret, image = cap.read()
        results = model.detect([image], verbose=1)
        r = results[0]
        image = display_instances(
            image, r["rois"], r["masks"], r["class_ids"], class_names, r["scores"]
        )
        if args.show:
            cv2.imshow("MaskRCNN Object Detection/Instance Segmentation", image)
            if cv2.waitKey(1) & 0xFF == ord("q"):
                break
        if args.save_path:
            out.write(image)
    cap.release()
    if args.save_path:
        out.release()
    cv2.destroyAllWindows()