zamba.models.densepose.densepose_manager¶
DENSEPOSE_AVAILABLE
¶
DensePoseOutputsTextureVisualizer
¶
DensePoseOutputsVertexVisualizer
¶
MODELS
¶
Classes¶
DensePoseManager
¶
Source code in zamba/models/densepose/densepose_manager.py
class DensePoseManager:
def __init__(
self,
model=MODELS["chimps"],
model_cache_dir: Path = Path(".zamba_cache"),
download_region=RegionEnum("us"),
):
"""Create a DensePoseManager object.
Parameters
----------
model : dict, optional (default MODELS['chimps'])
A dictionary with the densepose model defintion like those defined in MODELS.
"""
if not DENSEPOSE_AVAILABLE:
raise ImportError(
"Densepose not installed. See: https://zamba.drivendata.org/docs/stable/models/densepose/#installation"
)
# setup configuration for densepose
self.cfg = get_cfg()
add_densepose_config(self.cfg)
self.cfg.merge_from_file(model["config"])
if not (model_cache_dir / model["weights"]).exists():
model_cache_dir.mkdir(parents=True, exist_ok=True)
self.cfg.MODEL.WEIGHTS = download_weights(
model["weights"], model_cache_dir, download_region
)
# automatically use CPU if no cuda available
if not torch.cuda.is_available():
self.cfg.MODEL.DEVICE = "cpu"
self.cfg.freeze()
logging.getLogger("fvcore").setLevel("CRITICAL") # silence noisy detectron2 logging
# set up predictor with the configuration
self.predictor = DefaultPredictor(self.cfg)
# we have a specific texture atlas for chimps with relevant regions
# labeled that we can use instead of the default segmentation
self.visualizer = model["viz_class"](
self.cfg,
device=self.cfg.MODEL.DEVICE,
**model.get("viz_class_kwargs", {}),
)
# set up utilities for use with visualizer
self.vis_extractor = create_extractor(self.visualizer)
self.vis_embedder = build_densepose_embedder(self.cfg)
self.vis_class_to_mesh_name = get_class_to_mesh_name_mapping(self.cfg)
self.vis_mesh_vertex_embeddings = {
mesh_name: self.vis_embedder(mesh_name).to(self.cfg.MODEL.DEVICE)
for mesh_name in self.vis_class_to_mesh_name.values()
if self.vis_embedder.has_embeddings(mesh_name)
}
if "anatomy_color_mapping" in model:
self.anatomy_color_mapping = pd.read_csv(model["anatomy_color_mapping"], index_col=0)
else:
self.anatomy_color_mapping = None
def predict_image(self, image):
"""Run inference to get the densepose results for an image.
Parameters
----------
image :
numpy array (unit8) of an image in BGR format or path to an image
Returns
-------
tuple
Returns the image array as passed or loaded and the the densepose Instances as results.
"""
if isinstance(image, (str, Path)):
image = read_image(image, format="BGR")
return image, self.predict(image)
def predict_video(self, video, video_loader_config=None, pbar=True):
"""Run inference to get the densepose results for a video.
Parameters
----------
video :
numpy array (uint8) of a a video in BGR layout with time dimension first or path to a video
video_loader_config : VideoLoaderConfig, optional
A video loader config for loading videos (uses all defaults except pix_fmt="bgr24")
pbar : bool, optional
Whether to display a progress bar, by default True
Returns
-------
tuple
Tuple of (video_array, list of densepose results per frame)
"""
if isinstance(video, (str, Path)):
video = load_video_frames(video, config=video_loader_config)
pbar = tqdm if pbar else lambda x, **kwargs: x
return video, [
self.predict_image(img)[1] for img in pbar(video, desc="Frames")
] # just the predictions
def predict(self, image_arr):
"""Main call to DensePose for inference. Runs inference on an image array.
Parameters
----------
image_arr : numpy array
BGR image array
Returns
-------
Instances
Detection instances with boxes, scores, and densepose estimates.
"""
with torch.no_grad():
instances = self.predictor(image_arr)["instances"]
return instances
def serialize_video_output(self, instances, filename=None, write_embeddings=False):
serialized = {
"frames": [
self.serialize_image_output(
frame_instances, filename=None, write_embeddings=write_embeddings
)
for frame_instances in instances
]
}
if filename is not None:
with Path(filename).open("w") as f:
json.dump(serialized, f, indent=2)
return serialized
def serialize_image_output(self, instances, filename=None, write_embeddings=False):
"""Convert the densepose output into Python-native objects that can
be written and read with json.
Parameters
----------
instances : Instance
The output from the densepose model
filename : (str, Path), optional
If not None, the filename to write the output to, by default None
"""
if isinstance(instances, list):
img_height, img_width = instances[0].image_size
else:
img_height, img_width = instances.image_size
boxes = instances.get("pred_boxes").tensor
scores = instances.get("scores").tolist()
labels = instances.get("pred_classes").tolist()
try:
pose_result = instances.get("pred_densepose")
except KeyError:
pose_result = None
# include embeddings + segmentation if they exist and they are requested
write_embeddings = write_embeddings and (pose_result is not None)
serialized = {
"instances": [
{
"img_height": img_height,
"img_width": img_width,
"box": boxes[i].cpu().tolist(),
"score": scores[i],
"label": {
"value": labels[i],
"mesh_name": self.vis_class_to_mesh_name[labels[i]],
},
"embedding": pose_result.embedding[[i], ...].cpu().tolist()
if write_embeddings
else None,
"segmentation": pose_result.coarse_segm[[i], ...].cpu().tolist()
if write_embeddings
else None,
}
for i in range(len(instances))
]
}
if filename is not None:
with Path(filename).open("w") as f:
json.dump(serialized, f, indent=2)
return serialized
def deserialize_output(self, instances_dict=None, filename=None):
if filename is not None:
with Path(filename).open("r") as f:
instances_dict = json.load(f)
# handle image case
is_image = False
if "frames" not in instances_dict:
instances_dict = {"frames": [instances_dict]}
is_image = True
frames = []
for frame in instances_dict["frames"]:
heights, widths, boxes, scores, labels, embeddings, segmentations = zip(
*[
(
i["img_height"],
i["img_width"],
i["box"],
i["score"],
i["label"]["value"],
i["embedding"] if i["embedding"] is not None else [np.nan],
i["segmentation"] if i["segmentation"] is not None else [np.nan],
)
for i in frame["instances"]
]
)
frames.append(
Instances(
(heights[0], widths[0]),
pred_boxes=boxes,
scores=scores,
pred_classes=labels,
pred_densepose=DensePoseEmbeddingPredictorOutput(
embedding=torch.tensor(embeddings),
coarse_segm=torch.tensor(segmentations),
),
)
)
# if image or single frame, just return the instance
if is_image:
return frames[0]
else:
return frames
def visualize_image(self, image_arr, outputs, output_path=None):
"""Visualize the pose information.
Parameters
----------
image_arr : numpy array (unit8) BGR
The numpy array representing the image.
outputs :
The outputs from running DensePoseManager.predict*
output_path : str or Path, optional
If not None, write visualization to this path; by default None
Returns
-------
numpy array (unit8) BGR
DensePose outputs visualized on top of the image.
"""
bw_image = cv2.cvtColor(image_arr, cv2.COLOR_BGR2GRAY)
bw_image = np.tile(bw_image[:, :, np.newaxis], [1, 1, 3])
data = self.vis_extractor(outputs)
image_vis = self.visualizer.visualize(bw_image, data)
if output_path is not None:
cv2.imwrite(str(output_path), image_vis)
return image_vis
def anatomize_image(self, visualized_img_arr, outputs, output_path=None):
"""Convert the pose information into the percent of pixels in the detection
bounding box that correspond to each part of the anatomy in an image.
Parameters
----------
visualized_img_arr : numpy array (unit8) BGR
The numpy array the image after the texture has been visualized (by calling DensePoseManager.visualize_image).
outputs :
The outputs from running DensePoseManager.predict*
Returns
-------
pandas.DataFrame
DataFrame with percent of pixels of the bounding box that correspond to each anatomical part
"""
if self.anatomy_color_mapping is None:
raise ValueError(
"No anatomy_color_mapping provided to track anatomy; did you mean to use a different MODEL?"
)
# no detections, return empty df for joining later (e.g., in anatomize_video)
if not outputs:
return pd.DataFrame([])
_, _, N, bboxes_xywh, pred_classes = self.visualizer.extract_and_check_outputs_and_boxes(
self.vis_extractor(outputs)
)
all_detections = []
for n in range(N):
x, y, w, h = bboxes_xywh[n].int().cpu().numpy()
detection_area = visualized_img_arr[y : y + h, x : x + w]
detection_stats = {
name: (detection_area == np.array([[[color.B, color.G, color.R]]]))
.all(axis=-1)
.sum()
/ (h * w) # calc percent of bounding box with this color
for name, color in self.anatomy_color_mapping.iterrows()
}
detection_stats["x"] = x
detection_stats["y"] = y
detection_stats["h"] = h
detection_stats["w"] = w
all_detections.append(detection_stats)
results = pd.DataFrame(all_detections)
if output_path is not None:
results.to_csv(output_path, index=False)
return results
def visualize_video(
self, video_arr, outputs, output_path=None, frame_size=None, fps=30, pbar=True
):
"""Visualize the pose information on a video
Parameters
----------
video_arr : numpy array (unit8) BGR, time first
The numpy array representing the video.
outputs :
The outputs from running DensePoseManager.predict*
output_path : str or Path, optional
If not None, write visualization to this path (should be .mp4); by default None
frame_size : (innt, float), optional
If frame_size is float, scale up or down by that float value; if frame_size is an integer,
set width to that size and scale height appropriately.
fps : int
frames per second for output video if writing; defaults to 30
pbar : bool
display a progress bar
Returns
-------
numpy array (unit8) BGR
DensePose outputs visualized on top of the image.
"""
pbar = tqdm if pbar else lambda x, **kwargs: x
out_frames = np.array(
[
self.visualize_image(
image_arr,
output,
)
for image_arr, output in pbar(
zip(video_arr, outputs), total=video_arr.shape[0], desc="Visualize frames"
)
]
)
if output_path is not None:
# get new size for output video if scaling
if frame_size is None:
frame_size = video_arr.shape[2] # default to same size
# if float, scale as a multiple
if isinstance(frame_size, float):
frame_width = round(video_arr.shape[2] * frame_size)
frame_height = round(video_arr.shape[1] * frame_size)
# if int, use as width of the video and scale height proportionally
elif isinstance(frame_size, int):
frame_width = frame_size
scale = frame_width / video_arr.shape[2]
frame_height = round(video_arr.shape[1] * scale)
# setup output for writing
output_path = output_path.with_suffix(".mp4")
out = cv2.VideoWriter(
str(output_path),
cv2.VideoWriter_fourcc(*"mp4v"),
max(1, int(fps)),
(frame_width, frame_height),
)
for f in pbar(out_frames, desc="Write frames"):
if (f.shape[0] != frame_height) or (f.shape[1] != frame_width):
f = cv2.resize(
f,
(frame_width, frame_height),
# https://stackoverflow.com/a/51042104/1692709
interpolation=(
cv2.INTER_LINEAR if f.shape[1] < frame_width else cv2.INTER_AREA
),
)
out.write(f)
out.release()
return out_frames
def anatomize_video(self, visualized_video_arr, outputs, output_path=None, fps=30):
"""Convert the pose information into the percent of pixels in the detection
bounding box that correspond to each part of the anatomy in a video.
Parameters
----------
visualized_video_arr : numpy array (unit8) BGR
The numpy array the video after the texture has been visualized (by calling DensePoseManager.visualize_video).
outputs :
The outputs from running DensePoseManager.predict*
Returns
-------
numpy array (unit8) BGR
DensePose outputs visualized on top of the image.
"""
all_detections = []
for ix in range(visualized_video_arr.shape[0]):
detection_df = self.anatomize_image(visualized_video_arr[ix, ...], outputs[ix])
detection_df["frame"] = ix
detection_df["seconds"] = ix / fps
all_detections.append(detection_df)
results = pd.concat(all_detections)
if output_path is not None:
results.to_csv(output_path, index=False)
return results
Methods¶
__init__(self, model = {'config': '/home/runner/work/zamba/zamba/zamba/models/densepose/assets/densepose_rcnn_R_50_FPN_soft_chimps_finetune_4k.yaml', 'densepose_weights_url': 'https://dl.fbaipublicfiles.com/densepose/cse/densepose_rcnn_R_50_FPN_soft_chimps_finetune_4k/253146869/model_final_52f649.pkl', 'weights': 'zamba_densepose_model_final_52f649.pkl', 'viz_class': None, 'viz_class_kwargs': {'texture_atlases_dict': {'chimp_5029': None}}, 'anatomy_color_mapping': '/home/runner/work/zamba/zamba/zamba/models/densepose/assets/chimp_5029_parts.csv'}, model_cache_dir: Path = PosixPath('.zamba_cache'), download_region = <RegionEnum.us: 'us'>)
special
¶
Create a DensePoseManager object.
Parameters¶
model : dict, optional (default MODELS['chimps']) A dictionary with the densepose model defintion like those defined in MODELS.
Source code in zamba/models/densepose/densepose_manager.py
def __init__(
self,
model=MODELS["chimps"],
model_cache_dir: Path = Path(".zamba_cache"),
download_region=RegionEnum("us"),
):
"""Create a DensePoseManager object.
Parameters
----------
model : dict, optional (default MODELS['chimps'])
A dictionary with the densepose model defintion like those defined in MODELS.
"""
if not DENSEPOSE_AVAILABLE:
raise ImportError(
"Densepose not installed. See: https://zamba.drivendata.org/docs/stable/models/densepose/#installation"
)
# setup configuration for densepose
self.cfg = get_cfg()
add_densepose_config(self.cfg)
self.cfg.merge_from_file(model["config"])
if not (model_cache_dir / model["weights"]).exists():
model_cache_dir.mkdir(parents=True, exist_ok=True)
self.cfg.MODEL.WEIGHTS = download_weights(
model["weights"], model_cache_dir, download_region
)
# automatically use CPU if no cuda available
if not torch.cuda.is_available():
self.cfg.MODEL.DEVICE = "cpu"
self.cfg.freeze()
logging.getLogger("fvcore").setLevel("CRITICAL") # silence noisy detectron2 logging
# set up predictor with the configuration
self.predictor = DefaultPredictor(self.cfg)
# we have a specific texture atlas for chimps with relevant regions
# labeled that we can use instead of the default segmentation
self.visualizer = model["viz_class"](
self.cfg,
device=self.cfg.MODEL.DEVICE,
**model.get("viz_class_kwargs", {}),
)
# set up utilities for use with visualizer
self.vis_extractor = create_extractor(self.visualizer)
self.vis_embedder = build_densepose_embedder(self.cfg)
self.vis_class_to_mesh_name = get_class_to_mesh_name_mapping(self.cfg)
self.vis_mesh_vertex_embeddings = {
mesh_name: self.vis_embedder(mesh_name).to(self.cfg.MODEL.DEVICE)
for mesh_name in self.vis_class_to_mesh_name.values()
if self.vis_embedder.has_embeddings(mesh_name)
}
if "anatomy_color_mapping" in model:
self.anatomy_color_mapping = pd.read_csv(model["anatomy_color_mapping"], index_col=0)
else:
self.anatomy_color_mapping = None
anatomize_image(self, visualized_img_arr, outputs, output_path = None)
¶
Convert the pose information into the percent of pixels in the detection bounding box that correspond to each part of the anatomy in an image.
Parameters¶
visualized_img_arr : numpy array (unit8) BGR The numpy array the image after the texture has been visualized (by calling DensePoseManager.visualize_image). outputs : The outputs from running DensePoseManager.predict*
Returns¶
pandas.DataFrame DataFrame with percent of pixels of the bounding box that correspond to each anatomical part
Source code in zamba/models/densepose/densepose_manager.py
def anatomize_image(self, visualized_img_arr, outputs, output_path=None):
"""Convert the pose information into the percent of pixels in the detection
bounding box that correspond to each part of the anatomy in an image.
Parameters
----------
visualized_img_arr : numpy array (unit8) BGR
The numpy array the image after the texture has been visualized (by calling DensePoseManager.visualize_image).
outputs :
The outputs from running DensePoseManager.predict*
Returns
-------
pandas.DataFrame
DataFrame with percent of pixels of the bounding box that correspond to each anatomical part
"""
if self.anatomy_color_mapping is None:
raise ValueError(
"No anatomy_color_mapping provided to track anatomy; did you mean to use a different MODEL?"
)
# no detections, return empty df for joining later (e.g., in anatomize_video)
if not outputs:
return pd.DataFrame([])
_, _, N, bboxes_xywh, pred_classes = self.visualizer.extract_and_check_outputs_and_boxes(
self.vis_extractor(outputs)
)
all_detections = []
for n in range(N):
x, y, w, h = bboxes_xywh[n].int().cpu().numpy()
detection_area = visualized_img_arr[y : y + h, x : x + w]
detection_stats = {
name: (detection_area == np.array([[[color.B, color.G, color.R]]]))
.all(axis=-1)
.sum()
/ (h * w) # calc percent of bounding box with this color
for name, color in self.anatomy_color_mapping.iterrows()
}
detection_stats["x"] = x
detection_stats["y"] = y
detection_stats["h"] = h
detection_stats["w"] = w
all_detections.append(detection_stats)
results = pd.DataFrame(all_detections)
if output_path is not None:
results.to_csv(output_path, index=False)
return results
anatomize_video(self, visualized_video_arr, outputs, output_path = None, fps = 30)
¶
Convert the pose information into the percent of pixels in the detection bounding box that correspond to each part of the anatomy in a video.
Parameters¶
visualized_video_arr : numpy array (unit8) BGR The numpy array the video after the texture has been visualized (by calling DensePoseManager.visualize_video). outputs : The outputs from running DensePoseManager.predict*
Returns¶
numpy array (unit8) BGR DensePose outputs visualized on top of the image.
Source code in zamba/models/densepose/densepose_manager.py
def anatomize_video(self, visualized_video_arr, outputs, output_path=None, fps=30):
"""Convert the pose information into the percent of pixels in the detection
bounding box that correspond to each part of the anatomy in a video.
Parameters
----------
visualized_video_arr : numpy array (unit8) BGR
The numpy array the video after the texture has been visualized (by calling DensePoseManager.visualize_video).
outputs :
The outputs from running DensePoseManager.predict*
Returns
-------
numpy array (unit8) BGR
DensePose outputs visualized on top of the image.
"""
all_detections = []
for ix in range(visualized_video_arr.shape[0]):
detection_df = self.anatomize_image(visualized_video_arr[ix, ...], outputs[ix])
detection_df["frame"] = ix
detection_df["seconds"] = ix / fps
all_detections.append(detection_df)
results = pd.concat(all_detections)
if output_path is not None:
results.to_csv(output_path, index=False)
return results
deserialize_output(self, instances_dict = None, filename = None)
¶
Source code in zamba/models/densepose/densepose_manager.py
def deserialize_output(self, instances_dict=None, filename=None):
if filename is not None:
with Path(filename).open("r") as f:
instances_dict = json.load(f)
# handle image case
is_image = False
if "frames" not in instances_dict:
instances_dict = {"frames": [instances_dict]}
is_image = True
frames = []
for frame in instances_dict["frames"]:
heights, widths, boxes, scores, labels, embeddings, segmentations = zip(
*[
(
i["img_height"],
i["img_width"],
i["box"],
i["score"],
i["label"]["value"],
i["embedding"] if i["embedding"] is not None else [np.nan],
i["segmentation"] if i["segmentation"] is not None else [np.nan],
)
for i in frame["instances"]
]
)
frames.append(
Instances(
(heights[0], widths[0]),
pred_boxes=boxes,
scores=scores,
pred_classes=labels,
pred_densepose=DensePoseEmbeddingPredictorOutput(
embedding=torch.tensor(embeddings),
coarse_segm=torch.tensor(segmentations),
),
)
)
# if image or single frame, just return the instance
if is_image:
return frames[0]
else:
return frames
predict(self, image_arr)
¶
Main call to DensePose for inference. Runs inference on an image array.
Parameters¶
image_arr : numpy array BGR image array
Returns¶
Instances Detection instances with boxes, scores, and densepose estimates.
Source code in zamba/models/densepose/densepose_manager.py
def predict(self, image_arr):
"""Main call to DensePose for inference. Runs inference on an image array.
Parameters
----------
image_arr : numpy array
BGR image array
Returns
-------
Instances
Detection instances with boxes, scores, and densepose estimates.
"""
with torch.no_grad():
instances = self.predictor(image_arr)["instances"]
return instances
predict_image(self, image)
¶
Run inference to get the densepose results for an image.
Parameters¶
image : numpy array (unit8) of an image in BGR format or path to an image
Returns¶
tuple Returns the image array as passed or loaded and the the densepose Instances as results.
Source code in zamba/models/densepose/densepose_manager.py
def predict_image(self, image):
"""Run inference to get the densepose results for an image.
Parameters
----------
image :
numpy array (unit8) of an image in BGR format or path to an image
Returns
-------
tuple
Returns the image array as passed or loaded and the the densepose Instances as results.
"""
if isinstance(image, (str, Path)):
image = read_image(image, format="BGR")
return image, self.predict(image)
predict_video(self, video, video_loader_config = None, pbar = True)
¶
Run inference to get the densepose results for a video.
Parameters¶
video : numpy array (uint8) of a a video in BGR layout with time dimension first or path to a video video_loader_config : VideoLoaderConfig, optional A video loader config for loading videos (uses all defaults except pix_fmt="bgr24") pbar : bool, optional Whether to display a progress bar, by default True
Returns¶
tuple Tuple of (video_array, list of densepose results per frame)
Source code in zamba/models/densepose/densepose_manager.py
def predict_video(self, video, video_loader_config=None, pbar=True):
"""Run inference to get the densepose results for a video.
Parameters
----------
video :
numpy array (uint8) of a a video in BGR layout with time dimension first or path to a video
video_loader_config : VideoLoaderConfig, optional
A video loader config for loading videos (uses all defaults except pix_fmt="bgr24")
pbar : bool, optional
Whether to display a progress bar, by default True
Returns
-------
tuple
Tuple of (video_array, list of densepose results per frame)
"""
if isinstance(video, (str, Path)):
video = load_video_frames(video, config=video_loader_config)
pbar = tqdm if pbar else lambda x, **kwargs: x
return video, [
self.predict_image(img)[1] for img in pbar(video, desc="Frames")
] # just the predictions
serialize_image_output(self, instances, filename = None, write_embeddings = False)
¶
Convert the densepose output into Python-native objects that can be written and read with json.
Parameters¶
instances : Instance The output from the densepose model filename : (str, Path), optional If not None, the filename to write the output to, by default None
Source code in zamba/models/densepose/densepose_manager.py
def serialize_image_output(self, instances, filename=None, write_embeddings=False):
"""Convert the densepose output into Python-native objects that can
be written and read with json.
Parameters
----------
instances : Instance
The output from the densepose model
filename : (str, Path), optional
If not None, the filename to write the output to, by default None
"""
if isinstance(instances, list):
img_height, img_width = instances[0].image_size
else:
img_height, img_width = instances.image_size
boxes = instances.get("pred_boxes").tensor
scores = instances.get("scores").tolist()
labels = instances.get("pred_classes").tolist()
try:
pose_result = instances.get("pred_densepose")
except KeyError:
pose_result = None
# include embeddings + segmentation if they exist and they are requested
write_embeddings = write_embeddings and (pose_result is not None)
serialized = {
"instances": [
{
"img_height": img_height,
"img_width": img_width,
"box": boxes[i].cpu().tolist(),
"score": scores[i],
"label": {
"value": labels[i],
"mesh_name": self.vis_class_to_mesh_name[labels[i]],
},
"embedding": pose_result.embedding[[i], ...].cpu().tolist()
if write_embeddings
else None,
"segmentation": pose_result.coarse_segm[[i], ...].cpu().tolist()
if write_embeddings
else None,
}
for i in range(len(instances))
]
}
if filename is not None:
with Path(filename).open("w") as f:
json.dump(serialized, f, indent=2)
return serialized
serialize_video_output(self, instances, filename = None, write_embeddings = False)
¶
Source code in zamba/models/densepose/densepose_manager.py
def serialize_video_output(self, instances, filename=None, write_embeddings=False):
serialized = {
"frames": [
self.serialize_image_output(
frame_instances, filename=None, write_embeddings=write_embeddings
)
for frame_instances in instances
]
}
if filename is not None:
with Path(filename).open("w") as f:
json.dump(serialized, f, indent=2)
return serialized
visualize_image(self, image_arr, outputs, output_path = None)
¶
Visualize the pose information.
Parameters¶
image_arr : numpy array (unit8) BGR The numpy array representing the image. outputs : The outputs from running DensePoseManager.predict* output_path : str or Path, optional If not None, write visualization to this path; by default None
Returns¶
numpy array (unit8) BGR DensePose outputs visualized on top of the image.
Source code in zamba/models/densepose/densepose_manager.py
def visualize_image(self, image_arr, outputs, output_path=None):
"""Visualize the pose information.
Parameters
----------
image_arr : numpy array (unit8) BGR
The numpy array representing the image.
outputs :
The outputs from running DensePoseManager.predict*
output_path : str or Path, optional
If not None, write visualization to this path; by default None
Returns
-------
numpy array (unit8) BGR
DensePose outputs visualized on top of the image.
"""
bw_image = cv2.cvtColor(image_arr, cv2.COLOR_BGR2GRAY)
bw_image = np.tile(bw_image[:, :, np.newaxis], [1, 1, 3])
data = self.vis_extractor(outputs)
image_vis = self.visualizer.visualize(bw_image, data)
if output_path is not None:
cv2.imwrite(str(output_path), image_vis)
return image_vis
visualize_video(self, video_arr, outputs, output_path = None, frame_size = None, fps = 30, pbar = True)
¶
Visualize the pose information on a video
Parameters¶
video_arr : numpy array (unit8) BGR, time first The numpy array representing the video. outputs : The outputs from running DensePoseManager.predict* output_path : str or Path, optional If not None, write visualization to this path (should be .mp4); by default None frame_size : (innt, float), optional If frame_size is float, scale up or down by that float value; if frame_size is an integer, set width to that size and scale height appropriately. fps : int frames per second for output video if writing; defaults to 30 pbar : bool display a progress bar
Returns¶
numpy array (unit8) BGR DensePose outputs visualized on top of the image.
Source code in zamba/models/densepose/densepose_manager.py
def visualize_video(
self, video_arr, outputs, output_path=None, frame_size=None, fps=30, pbar=True
):
"""Visualize the pose information on a video
Parameters
----------
video_arr : numpy array (unit8) BGR, time first
The numpy array representing the video.
outputs :
The outputs from running DensePoseManager.predict*
output_path : str or Path, optional
If not None, write visualization to this path (should be .mp4); by default None
frame_size : (innt, float), optional
If frame_size is float, scale up or down by that float value; if frame_size is an integer,
set width to that size and scale height appropriately.
fps : int
frames per second for output video if writing; defaults to 30
pbar : bool
display a progress bar
Returns
-------
numpy array (unit8) BGR
DensePose outputs visualized on top of the image.
"""
pbar = tqdm if pbar else lambda x, **kwargs: x
out_frames = np.array(
[
self.visualize_image(
image_arr,
output,
)
for image_arr, output in pbar(
zip(video_arr, outputs), total=video_arr.shape[0], desc="Visualize frames"
)
]
)
if output_path is not None:
# get new size for output video if scaling
if frame_size is None:
frame_size = video_arr.shape[2] # default to same size
# if float, scale as a multiple
if isinstance(frame_size, float):
frame_width = round(video_arr.shape[2] * frame_size)
frame_height = round(video_arr.shape[1] * frame_size)
# if int, use as width of the video and scale height proportionally
elif isinstance(frame_size, int):
frame_width = frame_size
scale = frame_width / video_arr.shape[2]
frame_height = round(video_arr.shape[1] * scale)
# setup output for writing
output_path = output_path.with_suffix(".mp4")
out = cv2.VideoWriter(
str(output_path),
cv2.VideoWriter_fourcc(*"mp4v"),
max(1, int(fps)),
(frame_width, frame_height),
)
for f in pbar(out_frames, desc="Write frames"):
if (f.shape[0] != frame_height) or (f.shape[1] != frame_width):
f = cv2.resize(
f,
(frame_width, frame_height),
# https://stackoverflow.com/a/51042104/1692709
interpolation=(
cv2.INTER_LINEAR if f.shape[1] < frame_width else cv2.INTER_AREA
),
)
out.write(f)
out.release()
return out_frames
get_texture_atlas(x)
¶
Source code in zamba/models/densepose/densepose_manager.py
get_texture_atlas = lambda x: None # noqa: E731