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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import cv2
import torch
import numpy as np
from torchvision import transforms as T
from maskrcnn_benchmark.modeling.detector import build_detection_model
from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer
from maskrcnn_benchmark.structures.image_list import to_image_list
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.modeling.roi_heads.mask_head.inference import Masker
from maskrcnn_benchmark import layers as L
from maskrcnn_benchmark.utils import cv2_util
import timeit
class COCODemo(object):
# COCO categories for pretty print
CATEGORIES = [
"__background",
"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",
]
def __init__(
self,
cfg,
confidence_threshold=0.7,
show_mask_heatmaps=False,
masks_per_dim=2,
min_image_size=None,
exclude_region=None,
):
self.cfg = cfg.clone()
self.model = build_detection_model(cfg)
self.model.eval()
self.device = torch.device(cfg.MODEL.DEVICE)
self.model.to(self.device)
self.min_image_size = min_image_size
save_dir = cfg.OUTPUT_DIR
checkpointer = DetectronCheckpointer(cfg, self.model, save_dir=save_dir)
_ = checkpointer.load(cfg.MODEL.WEIGHT)
self.transforms = self.build_transform()
mask_threshold = -1 if show_mask_heatmaps else 0.5
self.masker = Masker(threshold=mask_threshold, padding=1)
# used to make colors for each class
self.palette = torch.tensor([2**25 - 1, 2**15 - 1, 2**21 - 1])
self.cpu_device = torch.device("cpu")
self.confidence_threshold = confidence_threshold
self.show_mask_heatmaps = show_mask_heatmaps
self.masks_per_dim = masks_per_dim
self.exclude_region = exclude_region
def build_transform(self):
"""
Creates a basic transformation that was used to train the models
"""
cfg = self.cfg
# we are loading images with OpenCV, so we don't need to convert them
# to BGR, they are already! So all we need to do is to normalize
# by 255 if we want to convert to BGR255 format, or flip the channels
# if we want it to be in RGB in [0-1] range.
if cfg.INPUT.TO_BGR255:
to_bgr_transform = T.Lambda(lambda x: x * 255)
else:
to_bgr_transform = T.Lambda(lambda x: x[[2, 1, 0]])
normalize_transform = T.Normalize(mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD)
transform = T.Compose(
[
T.ToPILImage(),
T.Resize(self.min_image_size) if self.min_image_size is not None else lambda x: x,
T.ToTensor(),
to_bgr_transform,
normalize_transform,
]
)
return transform
def inference(self, image, debug=False):
"""
Arguments:
image (np.ndarray): an image as returned by OpenCV
Returns:
prediction (BoxList): the detected objects. Additional information
of the detection properties can be found in the fields of
the BoxList via `prediction.fields()`
"""
predictions, debug_info = self.compute_prediction(image)
top_predictions = self.select_top_predictions(predictions)
if debug:
return top_predictions, debug_info
else:
return top_predictions
def run_on_opencv_image(self, image):
"""
Arguments:
image (np.ndarray): an image as returned by OpenCV
Returns:
prediction (BoxList): the detected objects. Additional information
of the detection properties can be found in the fields of
the BoxList via `prediction.fields()`
"""
predictions, debug_info = self.compute_prediction(image)
top_predictions = self.select_top_predictions(predictions)
result = image.copy()
if self.show_mask_heatmaps:
return self.create_mask_montage(result, top_predictions)
result = self.overlay_boxes(result, top_predictions)
if self.cfg.MODEL.MASK_ON:
result = self.overlay_mask(result, top_predictions)
if self.cfg.MODEL.KEYPOINT_ON:
result = self.overlay_keypoints(result, top_predictions)
result = self.overlay_class_names(result, top_predictions)
return result, debug_info, top_predictions
def compute_prediction(self, original_image):
"""
Arguments:
original_image (np.ndarray): an image as returned by OpenCV
Returns:
prediction (BoxList): the detected objects. Additional information
of the detection properties can be found in the fields of
the BoxList via `prediction.fields()`
"""
# apply pre-processing to image
# if self.exclude_region:
# for region in self.exclude_region:
# original_image[region[1]:region[3], region[0]:region[2], :] = 255
image = self.transforms(original_image)
# convert to an ImageList, padded so that it is divisible by
# cfg.DATALOADER.SIZE_DIVISIBILITY
image_list = to_image_list(image, self.cfg.DATALOADER.SIZE_DIVISIBILITY)
image_list = image_list.to(self.device)
tic = timeit.time.perf_counter()
# compute predictions
with torch.no_grad():
predictions, debug_info = self.model(image_list)
predictions = [o.to(self.cpu_device) for o in predictions]
debug_info["total_time"] = timeit.time.perf_counter() - tic
# always single image is passed at a time
prediction = predictions[0]
# reshape prediction (a BoxList) into the original image size
height, width = original_image.shape[:-1]
prediction = prediction.resize((width, height))
if prediction.has_field("mask"):
# if we have masks, paste the masks in the right position
# in the image, as defined by the bounding boxes
masks = prediction.get_field("mask")
# always single image is passed at a time
masks = self.masker([masks], [prediction])[0]
prediction.add_field("mask", masks)
return prediction, debug_info
def select_top_predictions(self, predictions):
"""
Select only predictions which have a `score` > self.confidence_threshold,
and returns the predictions in descending order of score
Arguments:
predictions (BoxList): the result of the computation by the model.
It should contain the field `scores`.
Returns:
prediction (BoxList): the detected objects. Additional information
of the detection properties can be found in the fields of
the BoxList via `prediction.fields()`
"""
scores = predictions.get_field("scores")
labels = predictions.get_field("labels").tolist()
thresh = scores.clone()
for i, lb in enumerate(labels):
if isinstance(self.confidence_threshold, float):
thresh[i] = self.confidence_threshold
elif len(self.confidence_threshold) == 1:
thresh[i] = self.confidence_threshold[0]
else:
thresh[i] = self.confidence_threshold[lb - 1]
keep = torch.nonzero(scores > thresh).squeeze(1)
predictions = predictions[keep]
if self.exclude_region:
exlude = BoxList(self.exclude_region, predictions.size)
iou = boxlist_iou(exlude, predictions)
keep = torch.nonzero(torch.sum(iou > 0.5, dim=0) == 0).squeeze(1)
if len(keep) > 0:
predictions = predictions[keep]
scores = predictions.get_field("scores")
_, idx = scores.sort(0, descending=True)
return predictions[idx]
def compute_colors_for_labels(self, labels):
"""
Simple function that adds fixed colors depending on the class
"""
colors = (30 * (labels[:, None] - 1) + 1) * self.palette
colors = (colors % 255).numpy().astype("uint8")
return colors
def overlay_boxes(self, image, predictions):
"""
Adds the predicted boxes on top of the image
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain the field `labels`.
"""
labels = predictions.get_field("labels")
boxes = predictions.bbox
colors = self.compute_colors_for_labels(labels).tolist()
for box, color in zip(boxes, colors):
box = box.to(torch.int64)
top_left, bottom_right = box[:2].tolist(), box[2:].tolist()
image = cv2.rectangle(image, tuple(top_left), tuple(bottom_right), tuple(color), 2)
return image
def overlay_scores(self, image, predictions):
"""
Adds the predicted boxes on top of the image
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain the field `labels`.
"""
scores = predictions.get_field("scores")
boxes = predictions.bbox
for box, score in zip(boxes, scores):
box = box.to(torch.int64)
image = cv2.putText(
image,
"%.3f" % score,
(box[0], (box[1] + box[3]) / 2),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(255, 255, 255),
1,
)
return image
def overlay_cboxes(self, image, predictions):
"""
Adds the predicted boxes on top of the image
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain the field `labels`.
"""
scores = predictions.get_field("scores")
boxes = predictions.bbox
for box, score in zip(boxes, scores):
box = box.to(torch.int64)
top_left, bottom_right = box[:2].tolist(), box[2:].tolist()
image = cv2.rectangle(image, tuple(top_left), tuple(bottom_right), (255, 0, 0), 2)
image = cv2.putText(
image, "%.3f" % score, (box[0], (box[1] + box[3]) / 2), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1
)
return image
def overlay_centers(self, image, predictions):
"""
Adds the predicted boxes on top of the image
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain the field `labels`.
"""
centers = predictions.get_field("centers")
for cord in centers:
cord = cord.to(torch.int64)
image = cv2.circle(image, (cord[0].item(), cord[1].item()), 2, (255, 0, 0), 20)
return image
def overlay_count(self, image, predictions):
"""
Adds the predicted boxes on top of the image
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain the field `labels`.
"""
if isinstance(predictions, int):
count = predictions
else:
count = len(predictions)
image = cv2.putText(image, "Count: %d" % count, (0, 100), cv2.FONT_HERSHEY_SIMPLEX, 3, (255, 0, 0), 3)
return image
def overlay_mask(self, image, predictions):
"""
Adds the instances contours for each predicted object.
Each label has a different color.
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain the field `mask` and `labels`.
"""
masks = predictions.get_field("mask").numpy()
labels = predictions.get_field("labels")
colors = self.compute_colors_for_labels(labels).tolist()
for mask, color in zip(masks, colors):
thresh = mask[0, :, :, None].astype(np.uint8)
contours, hierarchy = cv2_util.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
image = cv2.drawContours(image, contours, -1, color, 3)
composite = image
return composite
def overlay_keypoints(self, image, predictions):
keypoints = predictions.get_field("keypoints")
kps = keypoints.keypoints
scores = keypoints.get_field("logits")
kps = torch.cat((kps[:, :, 0:2], scores[:, :, None]), dim=2).numpy()
for region in kps:
image = vis_keypoints(
image, region.transpose((1, 0)), names=keypoints.NAMES, connections=keypoints.CONNECTIONS
)
return image
def create_mask_montage(self, image, predictions):
"""
Create a montage showing the probability heatmaps for each one one of the
detected objects
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain the field `mask`.
"""
masks = predictions.get_field("mask")
masks_per_dim = self.masks_per_dim
masks = L.interpolate(masks.float(), scale_factor=1 / masks_per_dim).byte()
height, width = masks.shape[-2:]
max_masks = masks_per_dim**2
masks = masks[:max_masks]
# handle case where we have less detections than max_masks
if len(masks) < max_masks:
masks_padded = torch.zeros(max_masks, 1, height, width, dtype=torch.uint8)
masks_padded[: len(masks)] = masks
masks = masks_padded
masks = masks.reshape(masks_per_dim, masks_per_dim, height, width)
result = torch.zeros((masks_per_dim * height, masks_per_dim * width), dtype=torch.uint8)
for y in range(masks_per_dim):
start_y = y * height
end_y = (y + 1) * height
for x in range(masks_per_dim):
start_x = x * width
end_x = (x + 1) * width
result[start_y:end_y, start_x:end_x] = masks[y, x]
return cv2.applyColorMap(result.numpy(), cv2.COLORMAP_JET)
def overlay_class_names(self, image, predictions, names=None):
"""
Adds detected class names and scores in the positions defined by the
top-left corner of the predicted bounding box
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain the field `scores` and `labels`.
"""
scores = predictions.get_field("scores").tolist()
labels = predictions.get_field("labels").tolist()
if names:
labels = [names[i - 1] for i in labels]
else:
labels = [self.CATEGORIES[i] for i in labels]
boxes = predictions.bbox
template = "{}: {:.2f}"
for box, score, label in zip(boxes, scores, labels):
x, y = box[:2]
s = template.format(label, score)
cv2.putText(image, s, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
return image
def vis_keypoints(img, kps, kp_thresh=0, alpha=0.7, names=None, connections=None):
"""Visualizes keypoints (adapted from vis_one_image).
kps has shape (4, #keypoints) where 4 rows are (x, y, logit, prob).
"""
dataset_keypoints = names
kp_lines = connections
# simple rainbow color map implementation
blue_red_ratio = 0.8
gx = lambda x: (6 - 2 * blue_red_ratio) * x + blue_red_ratio
colors = [
[
256 * max(0, (3 - abs(gx(i) - 4) - abs(gx(i) - 5)) / 2),
256 * max(0, (3 - abs(gx(i) - 2) - abs(gx(i) - 4)) / 2),
256 * max(0, (3 - abs(gx(i) - 1) - abs(gx(i) - 2)) / 2),
]
for i in np.linspace(0, 1, len(kp_lines) + 2)
]
# Perform the drawing on a copy of the image, to allow for blending.
kp_mask = np.copy(img)
# Draw mid shoulder / mid hip first for better visualization.
mid_shoulder = (
kps[:2, dataset_keypoints.index("right_shoulder")] + kps[:2, dataset_keypoints.index("left_shoulder")]
) / 2.0
sc_mid_shoulder = np.minimum(
kps[2, dataset_keypoints.index("right_shoulder")], kps[2, dataset_keypoints.index("left_shoulder")]
)
nose_idx = dataset_keypoints.index("nose")
if sc_mid_shoulder > kp_thresh and kps[2, nose_idx] > kp_thresh:
cv2.line(
kp_mask,
tuple(mid_shoulder),
tuple(kps[:2, nose_idx]),
color=colors[len(kp_lines)],
thickness=2,
lineType=cv2.LINE_AA,
)
if "right_hip" in names and "left_hip" in names:
mid_hip = (kps[:2, dataset_keypoints.index("right_hip")] + kps[:2, dataset_keypoints.index("left_hip")]) / 2.0
sc_mid_hip = np.minimum(
kps[2, dataset_keypoints.index("right_hip")], kps[2, dataset_keypoints.index("left_hip")]
)
if sc_mid_shoulder > kp_thresh and sc_mid_hip > kp_thresh:
cv2.line(
kp_mask,
tuple(mid_shoulder),
tuple(mid_hip),
color=colors[len(kp_lines) + 1],
thickness=2,
lineType=cv2.LINE_AA,
)
# Draw the keypoints.
for l in range(len(kp_lines)):
i1 = kp_lines[l][0]
i2 = kp_lines[l][1]
p1 = kps[0, i1], kps[1, i1]
p2 = kps[0, i2], kps[1, i2]
if kps[2, i1] > kp_thresh and kps[2, i2] > kp_thresh:
cv2.line(kp_mask, p1, p2, color=colors[l], thickness=2, lineType=cv2.LINE_AA)
if kps[2, i1] > kp_thresh:
cv2.circle(kp_mask, p1, radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA)
if kps[2, i2] > kp_thresh:
cv2.circle(kp_mask, p2, radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA)
# Blend the keypoints.
return cv2.addWeighted(img, 1.0 - alpha, kp_mask, alpha, 0)
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