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__main__.py
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__main__.py
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import argparse
import os
import pathlib
import time
import warnings
import cv2
import torch
import yolov3
def write_mp4(frames, fps, filepath):
"""
Write provided frames to an .mp4 video.
Args:
frames (list): List of frames (np.ndarray).
fps (int): Framerate (frames per second) of the output video.
filepath (str): Path to output video file.
"""
if not filepath.endswith(".mp4"):
filepath += ".mp4"
h, w = frames[0].shape[:2]
writer = cv2.VideoWriter(
filepath, cv2.VideoWriter_fourcc(*"mp4v"), int(fps), (w, h)
)
for frame in frames:
writer.write(frame)
writer.release()
def main():
parser = argparse.ArgumentParser()
source_ = parser.add_argument_group(title="input source [required]")
source_args = source_.add_mutually_exclusive_group(required=True)
source_args.add_argument(
"-C", "--cam", metavar="cam_id", nargs="?", const=0,
help="Camera or video capture device ID or path. [Default 0]"
)
source_args.add_argument(
"-I", "--image", type=pathlib.Path, metavar="<path>",
help="Path to image file or directory of images."
)
source_args.add_argument(
"-V", "--video", type=pathlib.Path, metavar="<path>",
help="Path to video file."
)
model_args = parser.add_argument_group(title="model parameters")
model_args.add_argument(
"-c", "--config", type=pathlib.Path, required=True, metavar="<path>",
help="[Required] Path to Darknet model config file."
)
model_args.add_argument(
"-d", "--device", type=str, default="cuda", metavar="<device>",
help="Device for inference ('cpu', 'cuda'). [Default 'cuda']"
)
model_args.add_argument(
"-i", "--iou-thresh", type=float, default=0.3, metavar="<iou>",
help="Non-maximum suppression IOU threshold. [Default 0.3]"
)
model_args.add_argument(
"-n", "--class-names", type=pathlib.Path, metavar="<path>",
help="Path to text file of class names. If omitted, class index is \
displayed instead of name."
)
model_args.add_argument(
"-p", "--prob-thresh", type=float, default=0.05, metavar="<prob>",
help="Detection probability threshold. [Default 0.05]"
)
model_args.add_argument(
"-w", "--weights", type=pathlib.Path, required=True, metavar="<path>",
help="[Required] Path to Darknet model weights file."
)
other_args = parser.add_argument_group(title="Output/display options")
other_args.add_argument(
"-o", "--output", type=pathlib.Path, metavar="<path>",
help="Path for writing output video file. Only .mp4 filetype \
currently supported. If --video input source selected, output \
FPS matches input FPS."
)
other_args.add_argument(
"--show-fps", action="store_true",
help="Display frames processed per second (for --cam input)."
)
other_args.add_argument(
"-v", "--verbose", action="store_true", help="Verbose output"
)
args = vars(parser.parse_args())
# Expand pathlib Paths and convert to string.
path_args = (
"class_names", "config", "weights", "image", "video", "output"
)
for path_arg in path_args:
if args[path_arg] is not None:
args[path_arg] = str(args[path_arg].expanduser().absolute())
device = args["device"]
if device.startswith("cuda") and not torch.cuda.is_available():
warnings.warn(
"CUDA not available; falling back to CPU. Pass `-d cpu` or ensure "
"compatible versions of CUDA and pytorch are installed.",
RuntimeWarning, stacklevel=2
)
device = "cpu"
net = yolov3.Darknet(args["config"], device=device)
net.load_weights(args["weights"])
net.eval()
if device.startswith("cuda"):
net.cuda(device=device)
if args["verbose"]:
if device == "cpu":
device_name = "CPU"
else:
device_name = torch.cuda.get_device_name(net.device)
print(f"Running model on {device_name}")
class_names = None
if args["class_names"] is not None and os.path.isfile(args["class_names"]):
with open(args["class_names"], "r") as f:
class_names = [line.strip() for line in f.readlines()]
if args["image"]:
source = "image"
elif args["video"]:
source = "video"
else:
source = "cam"
# If --cam argument is str representation of an int, interpret it as
# an int device ID. Else interpret as a path to a video capture stream.
if isinstance(args["cam"], str) and args["cam"].isdigit():
args["cam"] = int(args["cam"])
if source == "image":
if os.path.isdir(args["image"]):
image_dir = args["image"]
fnames = os.listdir(image_dir)
else:
image_dir, fname = os.path.split(args["image"])
fnames = [fname]
images = []
for fname in fnames:
images.append(cv2.imread(os.path.join(image_dir, fname)))
# TODO: batch images
results = []
for image in images:
results.extend(
yolov3.inference(
net, image, device=device, prob_thresh=args["prob_thresh"],
nms_iou_thresh=args["iou_thresh"]
)
)
for image, (bbox_xywh, _, class_idx) in zip(images, results):
yolov3.draw_boxes(
image, bbox_xywh, class_idx=class_idx, class_names=class_names
)
cv2.imshow("YOLOv3", image)
cv2.waitKey(0)
else:
frames = None
if args["output"]:
frames = []
if source == "cam":
start_time = time.time()
# Wrap in try/except block so that output video is written
# even if an error occurs while streaming webcam input.
try:
yolov3.detect_in_cam(
net, cam_id=args["cam"], device=device,
prob_thresh=args["prob_thresh"],
nms_iou_thresh=args["iou_thresh"],
class_names=class_names, show_fps=args["show_fps"],
frames=frames
)
except Exception as e:
raise e
finally:
if args["output"] and frames:
# Get average FPS and write output at that framerate.
fps = 1 / ((time.time() - start_time) / len(frames))
write_mp4(frames, fps, args["output"])
elif source == "video":
yolov3.detect_in_video(
net, filepath=args["video"], device=device,
prob_thresh=args["prob_thresh"],
nms_iou_thresh=args["iou_thresh"], class_names=class_names,
frames=frames
)
if args["output"] and frames:
# Get input video FPS and write output video at same FPS.
cap = cv2.VideoCapture(args["video"])
fps = cap.get(cv2.CAP_PROP_FPS)
cap.release()
write_mp4(frames, fps, args["output"])
cv2.destroyAllWindows()