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wyzely-detect
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Turn off verbose output 

Also print object type
This commit is contained in:
slashtechno 2023-10-03 21:03:39 -05:00
parent c56d7c86fc
commit dbf1b0ed63
Signed by: slashtechno
GPG Key ID: 8EC1D9D9286C2B17
1 changed files with 19 additions and 4 deletions
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23
src/__main__.py
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@ -75,8 +75,10 @@ def main():
# Check if a CUDA GPU is available. If it is, set it via torch. Ff not, set it to cpu # Check if a CUDA GPU is available. If it is, set it via torch. Ff not, set it to cpu
# https://github.com/ultralytics/ultralytics/issues/3084#issuecomment-1732433168 # https://github.com/ultralytics/ultralytics/issues/3084#issuecomment-1732433168
device = "0" if torch.cuda.is_available() else "cpu" # Currently, I have been unable to set up Poetry to use GPU for Torch
if device == "0": for i in range(torch.cuda.device_count()):
print(torch.cuda.get_device_properties(i).name)
if torch.cuda.is_available():
torch.cuda.set_device(0) torch.cuda.set_device(0)
print("Set CUDA device") print("Set CUDA device")
else: else:
@ -84,6 +86,7 @@ def main():
model = YOLO("yolov8n.pt") model = YOLO("yolov8n.pt")
# video_capture = cv2.VideoCapture(args.capture_device)
video_capture = cv2.VideoCapture(args.capture_device) video_capture = cv2.VideoCapture(args.capture_device)
# Eliminate lag by setting the buffer size to 1 # Eliminate lag by setting the buffer size to 1
# This makes it so that the video capture will only grab the most recent frame # This makes it so that the video capture will only grab the most recent frame
@ -105,9 +108,21 @@ def main():
run_frame = cv2.resize(frame, (0, 0), fx=args.run_scale, fy=args.run_scale) run_frame = cv2.resize(frame, (0, 0), fx=args.run_scale, fy=args.run_scale)
# view_frame = cv2.resize(frame, (0, 0), fx=args.view_scale, fy=args.view_scale) # view_frame = cv2.resize(frame, (0, 0), fx=args.view_scale, fy=args.view_scale)
results = model(run_frame) results = model(run_frame, verbose=False)
for r in results: for r in results:
for box in r.boxes:
# Get the name of the object
class_id = r.names[box.cls[0].item()]
# Get the coordinates of the object
cords = box.xyxy[0].tolist()
cords = [round(x) for x in cords]
# Get the confidence
conf = round(box.conf[0].item(), 2)
# Print it out, adding a spacer between each object
print("Object type:", class_id)
print("Coordinates:", cords)
print("Probability:", conf)
print("---")
im_array = r.plot() im_array = r.plot()
# Scale back up the coordinates of the locations of detected objects. # Scale back up the coordinates of the locations of detected objects.
# im_array = np.multiply(im_array, 1/args.run_scale) # im_array = np.multiply(im_array, 1/args.run_scale)