MaskRCNN:Example:VideoReadWrite
Mask R-CNN 데모를 위한 Video Read & Write Python example.
Source code
실행을 위해 samples/coco/coco.py
파일이 함께 필요하다.
- Import Mask RCNN
from mrcnn import utils import mrcnn.model as modellib from mrcnn import visualize
- Root directory of the project
ROOT_DIR = os.getcwd()
- Import COCO config
sys.path.append(ROOT_DIR) import coco
- Directory to save logs and trained model
MODEL_DIR = ROOT_DIR
- 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 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
DETECTION_MIN_CONFIDENCE = 0.6
#RPN_NMS_THRESHOLD = 0.5
config = InferenceConfig() config.display()
- 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)
- COCO Class names
- Index of the class in the list is its ID. For example, to get ID of
- the teddy bear class, use: class_names.index('teddy bear')
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']
- car: 3
- bus: 6
- truck: 8
- Load a random image from the images folder
- image = skimage.io.imread(os.path.join(ROOT_DIR, 'zz.jpg'))
- Run detection
- results = model.detect([image], verbose=1)
- Visualize results
- r = results[0]
- visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'], class_names, r['scores'])
import math
def drawImage(image, boxes, masks, class_ids, class_names, scores, show_bbox=False, show_label=False, show_seg=True, show_center=True):
N = boxes.shape[0] # Number of instances
if not N:
print("\n*** No instances to display *** \n")
else:
assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0]
masked_image = image.astype(np.uint32).copy()
car_count = 0
for i in range(N):
class_id = class_ids[i]
if class_id != 3 and class_id != 6 and class_id != 8:
continue
car_count = car_count + 1
# Bounding Box.
if not np.any(boxes[i]):
# Skip this instance. Has no bbox. Likely lost in image cropping.
continue
y1, x1, y2, x2 = boxes[i]
if show_bbox:
cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 1, cv2.LINE_AA)
#p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2, alpha=0.7, linestyle="dashed", edgecolor=color, facecolor='none')
if show_center:
cv2.circle(image, (x1 + int(math.fabs(float(x2 - x1))/2), y1 + int(math.fabs(float(y2 - y1))/2)), 4, (0, 0, 255), -1)
if show_label:
score = scores[i] if scores is not None else None
label = class_names[class_id]
caption = "{} {:.3f}".format(label, score) if score else label
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(image, caption, (x1, y1 + 8), font, 1, (255, 255, 255), 1, cv2.LINE_AA)
#ax.text(x1, y1 + 8, caption, color='w', size=11, backgroundcolor="none")
if show_seg:
# Mask
mask = masks[:, :, i]
masked_image = visualize.apply_mask(masked_image, mask, (0, 0, 0))
# Mask Polyline
# Pad to ensure proper polygons for masks that touch image edges.
padded_mask = np.zeros((mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8)
padded_mask[1:-1, 1:-1] = mask
contours = visualize.find_contours(padded_mask, 0.5)
for verts in contours:
# Subtract the padding and flip (y, x) to (x, y)
verts = np.fliplr(verts) - 1
cv2.polylines(image, np.int32([verts]), True, (0, 255, 255))
#p = visualize.Polygon(verts, facecolor="none", edgecolor=())
#ax.add_patch(p)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(image, 'CAR: {}'.format(car_count), (5, 32), font, 1, (0, 0, 255), 2, cv2.LINE_AA)
pass
import numpy as np import cv2
INPUT_VIDEO_PATH = os.path.join(ROOT_DIR, 'video1.mp4')
cap = cv2.VideoCapture(INPUT_VIDEO_PATH) VIDEO_WIDTH = cap.get(cv2.CAP_PROP_FRAME_WIDTH) VIDEO_HEIGHT = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) VIDEO_FRAMES = cap.get(cv2.CAP_PROP_FRAME_COUNT) VIDEO_FPS = cap.get(cv2.CAP_PROP_FPS)
print('Video Open: ', INPUT_VIDEO_PATH) print('Video size: ', VIDEO_WIDTH, 'x', VIDEO_HEIGHT) print('Video frames: ', VIDEO_FRAMES) print('Video FPS: ', VIDEO_FPS)
FOURCC = cv2.VideoWriter_fourcc(*'XVID') out = cv2.VideoWriter('output.avi', FOURCC, float(VIDEO_FPS), (int(VIDEO_WIDTH), int(VIDEO_HEIGHT)))
frame_index = 0 SKIP_PREFIX_FRAMES = 0
show_video = True
while (cap.isOpened()):
ret, frame = cap.read()
if (frame_index < SKIP_PREFIX_FRAMES):
frame_index = frame_index + 1
continue
results = model.detect([frame], verbose=1)
print('Detect: ', frame_index, '/', VIDEO_FRAMES)
frame_index = frame_index + 1
r = results[0]
drawImage(frame, r['rois'], r['masks'], r['class_ids'], class_names, r['scores'])
if show_video:
cv2.imshow('preview', frame)
out.write(frame)
key_code = cv2.waitKey(1)
if key_code & 0xFF == ord('q'):
break
elif key_code & 0xFF == ord('s'):
show_video = True
elif key_code & 0xFF == ord('h'):
show_video = False
cap.release() out.release() cv2.destroyAllWindows()
</syntaxhighlight>