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Bug fix: confidence works and displays correctly

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This commit is contained in:
Elijah Harmon 2023-03-28 17:56:05 -04:00
parent bc9af95607
commit 4a7853645d
14 changed files with 90 additions and 70 deletions
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14
main.py
View File

@ -28,7 +28,7 @@ def main():
aaMovementAmp = .8
# Person Class Confidence
confidence = 0.5
confidence = 0.4
# What key to press to quit and shutdown the autoaim
aaQuitKey = "Q"
@ -113,7 +113,7 @@ def main():
npImg = np.array(camera.get_latest_frame())
# Normalizing Data
im = torch.from_numpy(npImg).to('cuda')
im = torch.from_numpy(npImg)
im = torch.movedim(im, 2, 0)
im = im.half()
im /= 255
@ -125,7 +125,7 @@ def main():
# Suppressing results that dont meet thresholds
pred = non_max_suppression(
results, 0.25, 0.25, 0, False, max_det=1000)
results, confidence, confidence, 0, False, max_det=1000)
# Converting output to usable cords
targets = []
@ -139,10 +139,10 @@ def main():
for *xyxy, conf, cls in reversed(det):
targets.append((xyxy2xywh(torch.tensor(xyxy).view(
1, 4)) / gn).view(-1).tolist()) # normalized xywh
1, 4)) / gn).view(-1).tolist() + [float(conf)]) # normalized xywh
targets = pd.DataFrame(
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height"])
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height", "confidence"])
# If there are people in the center bounding box
if len(targets) > 0:
@ -187,12 +187,10 @@ def main():
(startX, startY, endX, endY) = int(
midX + halfW), int(midY + halfH), int(midX - halfW), int(midY - halfH)
confidence = .5
idx = 0
# draw the bounding box and label on the frame
label = "{}: {:.2f}%".format("Human", confidence * 100)
label = "{}: {:.2f}%".format("Human", targets["confidence"][i] * 100)
cv2.rectangle(npImg, (startX, startY), (endX, endY),
COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15

23
main_onnx_amd.py
View File

@ -27,10 +27,10 @@ def main():
aaRightShift = 0
# Autoaim mouse movement amplifier
aaMovementAmp = 0.6
aaMovementAmp = .8
# Person Class Confidence
confidence = 0.35
confidence = 0.4
# What key to press to quit and shutdown the autoaim
aaQuitKey = "Q"
@ -105,9 +105,16 @@ def main():
last_mid_coord = None
while win32api.GetAsyncKeyState(ord(aaQuitKey)) == 0:
npImg = cp.array([camera.get_latest_frame()]) / 255
npImg = npImg.astype(cp.half)
npImg = cp.moveaxis(npImg, 3, 1)
# Getting Frame
npImg = np.array(camera.get_latest_frame())
# Normalizing Data
im = torch.from_numpy(npImg)
im = torch.movedim(im, 2, 0)
im = im.half()
im /= 255
if len(im.shape) == 3:
im = im[None]
outputs = ort_sess.run(None, {'images': npImg})
@ -127,10 +134,10 @@ def main():
for *xyxy, conf, cls in reversed(det):
targets.append((xyxy2xywh(torch.tensor(xyxy).view(
1, 4)) / gn).view(-1).tolist()) # normalized xywh
1, 4)) / gn).view(-1).tolist() + [float(conf)]) # normalized xywh
targets = pd.DataFrame(
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height"])
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height", "confidence"])
# If there are people in the center bounding box
if len(targets) > 0:
@ -177,7 +184,7 @@ def main():
idx = 0
# draw the bounding box and label on the frame
label = "{}: {:.2f}%".format("Human", confidence * 100)
label = "{}: {:.2f}%".format("Human", targets["confidence"][i] * 100)
cv2.rectangle(npImg, (startX, startY), (endX, endY),
COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15

23
main_onnx_cpu.py
View File

@ -26,10 +26,10 @@ def main():
aaRightShift = 0
# Autoaim mouse movement amplifier
aaMovementAmp = 0.6
aaMovementAmp = .8
# Person Class Confidence
confidence = 0.35
confidence = 0.4
# What key to press to quit and shutdown the autoaim
aaQuitKey = "Q"
@ -106,9 +106,16 @@ def main():
last_mid_coord = None
while win32api.GetAsyncKeyState(ord(aaQuitKey)) == 0:
npImg = np.array([camera.get_latest_frame()]) / 255
npImg = npImg.astype(np.half)
npImg = np.moveaxis(npImg, 3, 1)
# Getting Frame
npImg = np.array(camera.get_latest_frame())
# Normalizing Data
im = torch.from_numpy(npImg)
im = torch.movedim(im, 2, 0)
im = im.half()
im /= 255
if len(im.shape) == 3:
im = im[None]
outputs = ort_sess.run(None, {'images': npImg})
@ -128,10 +135,10 @@ def main():
for *xyxy, conf, cls in reversed(det):
targets.append((xyxy2xywh(torch.tensor(xyxy).view(
1, 4)) / gn).view(-1).tolist()) # normalized xywh
1, 4)) / gn).view(-1).tolist() + [float(conf)]) # normalized xywh
targets = pd.DataFrame(
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height"])
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height", "confidence"])
# If there are people in the center bounding box
if len(targets) > 0:
@ -178,7 +185,7 @@ def main():
idx = 0
# draw the bounding box and label on the frame
label = "{}: {:.2f}%".format("Human", confidence * 100)
label = "{}: {:.2f}%".format("Human", targets["confidence"][i] * 100)
cv2.rectangle(npImg, (startX, startY), (endX, endY),
COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15

29
main_onnx_nvidia copy.py → main_onnx_nvidia.py
View File

@ -27,10 +27,10 @@ def main():
aaRightShift = 0
# Autoaim mouse movement amplifier
aaMovementAmp = 0.6
aaMovementAmp = .8
# Person Class Confidence
confidence = 0.35
confidence = 0.4
# What key to press to quit and shutdown the autoaim
aaQuitKey = "Q"
@ -42,7 +42,7 @@ def main():
cpsDisplay = True
# Set to True if you want to get the visuals
visuals = False
visuals = True
# Selecting the correct game window
try:
@ -105,11 +105,18 @@ def main():
last_mid_coord = None
while win32api.GetAsyncKeyState(ord(aaQuitKey)) == 0:
npImg = cp.array([camera.get_latest_frame()]) / 255
npImg = npImg.astype(cp.half)
npImg = cp.moveaxis(npImg, 3, 1)
# Getting Frame
npImg = np.array(camera.get_latest_frame())
outputs = ort_sess.run(None, {'images': cp.asnumpy(npImg)})
# Normalizing Data
im = torch.from_numpy(npImg).to('cuda')
im = torch.movedim(im, 2, 0)
im = im.half()
im /= 255
if len(im.shape) == 3:
im = im[None]
outputs = ort_sess.run(None, {'images': cp.asnumpy(im)})
im = torch.from_numpy(outputs[0]).to('cpu')
@ -119,7 +126,7 @@ def main():
targets = []
for i, det in enumerate(pred):
s = ""
gn = torch.tensor(npImg.shape)[[0, 0, 0, 0]]
gn = torch.tensor(im.shape)[[0, 0, 0, 0]]
if len(det):
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
@ -127,10 +134,10 @@ def main():
for *xyxy, conf, cls in reversed(det):
targets.append((xyxy2xywh(torch.tensor(xyxy).view(
1, 4)) / gn).view(-1).tolist()) # normalized xywh
1, 4)) / gn).view(-1).tolist() + [float(conf)]) # normalized xywh
targets = pd.DataFrame(
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height"])
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height", "confidence"])
# If there are people in the center bounding box
if len(targets) > 0:
@ -177,7 +184,7 @@ def main():
idx = 0
# draw the bounding box and label on the frame
label = "{}: {:.2f}%".format("Human", confidence * 100)
label = "{}: {:.2f}%".format("Human", targets["confidence"][i] * 100)
cv2.rectangle(npImg, (startX, startY), (endX, endY),
COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15

32
main_tensorrt_gpu.py
View File

@ -94,7 +94,7 @@ def main():
sTime = time.time()
# Loading Yolo5 Small AI Model
model = DetectMultiBackend('yolov5s.engine', device=torch.device(
model = DetectMultiBackend('yolov5s320Half.engine', device=torch.device(
'cuda'), dnn=False, data='', fp16=True)
stride, names, pt = model.stride, model.names, model.pt
@ -106,17 +106,18 @@ def main():
with torch.no_grad():
while win32api.GetAsyncKeyState(ord(aaQuitKey)) == 0:
npImg = cp.array([camera.get_latest_frame()]) / 255
npImg = npImg.astype(cp.half)
npImg = cp.array([camera.get_latest_frame()])
im = npImg / 255
im = im.astype(cp.half)
im = cp.moveaxis(npImg, 3, 1)
im = cp.moveaxis(im, 3, 1)
im = torch.from_numpy(cp.asnumpy(im)).to('cuda')
# Converting to numpy for visuals
im0 = im[0].permute(1, 2, 0) * 255
im0 = im0.cpu().numpy().astype(np.uint8)
# Image has to be in BGR for visualization
im0 = cv2.cvtColor(im0, cv2.COLOR_RGB2BGR)
# # Converting to numpy for visuals
# im0 = im[0].permute(1, 2, 0) * 255
# im0 = im0.cpu().numpy().astype(np.uint8)
# # Image has to be in BGR for visualization
# im0 = cv2.cvtColor(im0, cv2.COLOR_RGB2BGR)
# Detecting all the objects
results = model(im)
@ -135,10 +136,10 @@ def main():
for *xyxy, conf, cls in reversed(det):
targets.append((xyxy2xywh(torch.tensor(xyxy).view(
1, 4)) / gn).view(-1).tolist()) # normalized xywh
1, 4)) / gn).view(-1).tolist() + [float(conf)]) # normalized xywh
targets = pd.DataFrame(
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height"])
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height", "confidence"])
# If there are people in the center bounding box
if len(targets) > 0:
@ -174,6 +175,7 @@ def main():
# See what the bot sees
if visuals:
npImg = cp.asnumpy(npImg[0])
# Loops over every item identified and draws a bounding box
for i in range(0, len(targets)):
halfW = round(targets["width"][i] / 2)
@ -185,11 +187,11 @@ def main():
idx = 0
# draw the bounding box and label on the frame
label = "{}: {:.2f}%".format("Human", confidence * 100)
cv2.rectangle(im0, (startX, startY), (endX, endY),
label = "{}: {:.2f}%".format("Human", targets["confidence"][i] * 100)
cv2.rectangle(npImg, (startX, startY), (endX, endY),
COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(im0, label, (startX, y),
cv2.putText(npImg, label, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
# Forced garbage cleanup every second
@ -205,7 +207,7 @@ def main():
# See visually what the Aimbot sees
if visuals:
cv2.imshow('Live Feed', im0)
cv2.imshow('Live Feed', npImg)
if (cv2.waitKey(1) & 0xFF) == ord('q'):
exit()
camera.stop()

10
main_torch_gpu.py
View File

@ -25,10 +25,10 @@ def main():
aaRightShift = 0
# Autoaim mouse movement amplifier
aaMovementAmp = 1.0
aaMovementAmp = .8
# Person Class Confidence
confidence = 0.25
confidence = 0.4
# What key to press to quit and shutdown the autoaim
aaQuitKey = "Q"
@ -136,10 +136,10 @@ def main():
for *xyxy, conf, cls in reversed(det):
targets.append((xyxy2xywh(torch.tensor(xyxy).view(
1, 4)) / gn).view(-1).tolist()) # normalized xywh
1, 4)) / gn).view(-1).tolist() + [float(conf)]) # normalized xywh
targets = pd.DataFrame(
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height"])
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height", "confidence"])
# If there are people in the center bounding box
if len(targets) > 0:
@ -186,7 +186,7 @@ def main():
idx = 0
# draw the bounding box and label on the frame
label = "{}: {:.2f}%".format("Human", confidence * 100)
label = "{}: {:.2f}%".format("Human", targets["confidence"][i] * 100)
cv2.rectangle(npImg, (startX, startY), (endX, endY),
COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15

3
utils/dataloaders.py
View File

@ -531,13 +531,14 @@ class LoadImagesAndLabels(Dataset):
# Update labels
include_class = [] # filter labels to include only these classes (optional)
self.segments = list(self.segments)
include_class_array = np.array(include_class).reshape(1, -1)
for i, (label, segment) in enumerate(zip(self.labels, self.segments)):
if include_class:
j = (label[:, 0:1] == include_class_array).any(1)
self.labels[i] = label[j]
if segment:
self.segments[i] = segment[j]
self.segments[i] = [segment[idx] for idx, elem in enumerate(j) if elem]
if single_cls: # single-class training, merge all classes into 0
self.labels[i][:, 0] = 0

5
utils/docker/Dockerfile
View File

@ -2,9 +2,8 @@
# Builds ultralytics/yolov5:latest image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
# Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference
# Start FROM NVIDIA PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
# FROM docker.io/pytorch/pytorch:latest
FROM pytorch/pytorch:latest
# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch
FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-runtime
# Downloads to user config dir
ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/

2
utils/downloads.py
View File

@ -118,8 +118,8 @@ def attempt_download(file, repo='ultralytics/yolov5', release='v7.0'):
except Exception:
tag = release
file.parent.mkdir(parents=True, exist_ok=True) # make parent dir (if required)
if name in assets:
file.parent.mkdir(parents=True, exist_ok=True) # make parent dir (if required)
safe_download(file,
url=f'https://github.com/{repo}/releases/download/{tag}/{name}',
min_bytes=1E5,

8
utils/general.py
View File

@ -1119,13 +1119,13 @@ def increment_path(path, exist_ok=False, sep='', mkdir=False):
imshow_ = cv2.imshow # copy to avoid recursion errors
def imread(path, flags=cv2.IMREAD_COLOR):
return cv2.imdecode(np.fromfile(path, np.uint8), flags)
def imread(filename, flags=cv2.IMREAD_COLOR):
return cv2.imdecode(np.fromfile(filename, np.uint8), flags)
def imwrite(path, im):
def imwrite(filename, img):
try:
cv2.imencode(Path(path).suffix, im)[1].tofile(path)
cv2.imencode(Path(filename).suffix, img)[1].tofile(filename)
return True
except Exception:
return False

2
utils/segment/general.py
View File

@ -10,7 +10,7 @@ def crop_mask(masks, boxes):
Vectorized by Chong (thanks Chong).
Args:
- masks should be a size [h, w, n] tensor of masks
- masks should be a size [n, h, w] tensor of masks
- boxes should be a size [n, 4] tensor of bbox coords in relative point form
"""

1
utils/segment/loss.py
View File

@ -16,7 +16,6 @@ class ComputeLoss:
self.overlap = overlap
device = next(model.parameters()).device # get model device
h = model.hyp # hyperparameters
self.device = device
# Define criteria
BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))

2
utils/segment/plots.py
View File

@ -54,7 +54,7 @@ def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg'
x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders
if paths:
annotator.text((x + 5, y + 5 + h), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
if len(targets) > 0:
idx = targets[:, 0] == i
ti = targets[idx] # image targets