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fixed onnx_amd and added skipRegions (#76)

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* fixed main_onnx_amd.py

* cleaned up some old requirements
* added separate requirements for the onnx versions
* ran autopep8 on all the script files

* added skipRegions
This commit is contained in:
kotae4 2023-03-30 01:21:42 -04:00 committed by GitHub
parent 02a9aee048
commit 4852365592
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GPG Key ID: 4AEE18F83AFDEB23
9 changed files with 86 additions and 30 deletions
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4
.gitignore vendored
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@ -2,6 +2,7 @@ test*
*.onnx
*.pt
*.torchscript
*.engine
onnxVer.py
!yolov5s320.onnx
yolov5s.engine
@ -10,4 +11,5 @@ tensorrt-8.4.1.5-cp39-none-win_amd64.whl
tensorrt-8.4.1.5-cp310-none-win_amd64.whl
__pycache__
utils/__pycache__
models/__pycache__
models/__pycache__
venv

7
main.py
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@ -140,10 +140,10 @@ def main():
for *xyxy, conf, cls in reversed(det):
targets.append((xyxy2xywh(torch.tensor(xyxy).view(
1, 4)) / gn).view(-1).tolist() + [float(conf)]) # normalized xywh
targets = pd.DataFrame(
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height", "confidence"])
# If there are people in the center bounding box
if len(targets) > 0:
# Get the last persons mid coordinate if it exists
@ -190,7 +190,8 @@ def main():
idx = 0
# draw the bounding box and label on the frame
label = "{}: {:.2f}%".format("Human", targets["confidence"][i] * 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

73
main_onnx_amd.py
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@ -26,6 +26,15 @@ def main():
# EXAMPLE: Fortnite and New World
aaRightShift = 0
# An alternative to aaRightShift
# Mark regions of the screen where your own player character is
# This will often prevent the mouse from drifting to an edge of the screen
# Format is (minX, minY, maxX, maxY) to form a rectangle
# Remember, Y coordinates start at the top and move downward (higher Y values = lower on screen)
skipRegions: list[tuple] = [
(200, 230, screenShotWidth, screenShotHeight)
]
# Autoaim mouse movement amplifier
aaMovementAmp = .8
@ -85,6 +94,14 @@ def main():
return
camera.start(target_fps=160, video_mode=True)
if visuals == True:
# Create and Position the Live Feed window to the left of the game window
cv2WindowName = 'Live Feed'
cv2.namedWindow(cv2WindowName)
visualsXPos = videoGameWindow.left - screenShotWidth - 5
cv2.moveWindow(cv2WindowName, (visualsXPos if visualsXPos >
0 else 0), videoGameWindow.top)
# Calculating the center Autoaim box
cWidth = sctArea["width"] / 2
cHeight = sctArea["height"] / 2
@ -95,6 +112,7 @@ def main():
so = ort.SessionOptions()
so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
so.enable_mem_pattern = False
ort_sess = ort.InferenceSession('yolov5s320.onnx', sess_options=so, providers=[
'DmlExecutionProvider'])
@ -106,23 +124,22 @@ def main():
while win32api.GetAsyncKeyState(ord(aaQuitKey)) == 0:
# Getting Frame
npImg = np.array(camera.get_latest_frame())
cap = camera.get_latest_frame()
if cap is None:
continue
# 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': np.array(im)})
npImg = np.array([cap]) / 255
npImg = npImg.astype(np.half)
npImg = np.moveaxis(npImg, 3, 1)
# Run ML Inference
outputs = ort_sess.run(None, {'images': np.array(npImg)})
im = torch.from_numpy(outputs[0]).to('cpu')
pred = non_max_suppression(
im, confidence, confidence, 0, False, max_det=10)
# Get targets from ML predictions
targets = []
for i, det in enumerate(pred):
s = ""
@ -133,8 +150,25 @@ def main():
s += f"{n} {int(c)}, " # add to string
for *xyxy, conf, cls in reversed(det):
targets.append((xyxy2xywh(torch.tensor(xyxy).view(
1, 4)) / gn).view(-1).tolist() + [float(conf)]) # normalized xywh
# normalized xywh
detTensorScreenCoords = (xyxy2xywh(torch.tensor(xyxy).view(
1, 4)) / gn).view(-1)
detScreenCoords = (
detTensorScreenCoords.tolist() + [float(conf)])
isSkipped = False
for skipRegion in skipRegions:
# TODO check logic. there are some rare edge cases.
# if min and max are both within the min and max of the other, then we are fully within it
detectionWithinSkipRegion = ((xyxy[0] >= skipRegion[0] and xyxy[2] <= skipRegion[2])
and (xyxy[1] >= skipRegion[1] and xyxy[3] <= skipRegion[3]))
# if above top edge, to the right of right edge, below bottom edge, or left of left edge, then there can be no intersection
detectionIntersectsSkipRegion = not (
xyxy[0] > skipRegion[2] or xyxy[2] < skipRegion[0] or xyxy[1] > skipRegion[3] or xyxy[1] < skipRegion[3])
if detectionWithinSkipRegion or detectionIntersectsSkipRegion:
isSkipped = True
break
if isSkipped == False:
targets.append(detScreenCoords)
targets = pd.DataFrame(
targets, columns=['current_mid_x', 'current_mid_y', 'width', "height", "confidence"])
@ -148,6 +182,7 @@ def main():
# Take distance between current person mid coordinate and last person mid coordinate
targets['dist'] = np.linalg.norm(
targets.iloc[:, [0, 1]].values - targets.iloc[:, [4, 5]], axis=1)
# This ensures the person closest to the crosshairs is the one that's targeted
targets.sort_values(by="dist", ascending=False)
# Take the first person that shows up in the dataframe (Recall that we sort based on Euclidean distance)
@ -184,12 +219,16 @@ def main():
idx = 0
# draw the bounding box and label on the frame
label = "{}: {:.2f}%".format("Human", targets["confidence"][i] * 100)
cv2.rectangle(npImg, (startX, startY), (endX, endY),
label = "{}: {:.2f}%".format(
"Human", targets["confidence"][i] * 100)
cv2.rectangle(cap, (startX, startY), (endX, endY),
COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(npImg, label, (startX, y),
cv2.putText(cap, label, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
for skipRegion in skipRegions:
cv2.rectangle(cap, (skipRegion[0], skipRegion[1]), (skipRegion[2],
skipRegion[3]), (0, 0, 0), 2)
# Forced garbage cleanup every second
count += 1
@ -204,8 +243,9 @@ def main():
# See visually what the Aimbot sees
if visuals:
cv2.imshow('Live Feed', npImg)
cv2.imshow(cv2WindowName, cap)
if (cv2.waitKey(1) & 0xFF) == ord('q'):
cv2.destroyAllWindows()
exit()
camera.stop()
@ -219,3 +259,4 @@ if __name__ == "__main__":
traceback.print_exception(e)
print(str(e))
print("Please read the above message and think about how it could be solved before posting it on discord.")
cv2.destroyAllWindows()

7
main_onnx_cpu.py
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@ -97,7 +97,7 @@ def main():
so = ort.SessionOptions()
so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
ort_sess = ort.InferenceSession('yolov5s320.onnx', sess_options=so, providers=[
'CUDAExecutionProvider'])
'CPUExecutionProvider'])
# Used for colors drawn on bounding boxes
COLORS = np.random.uniform(0, 255, size=(1500, 3))
@ -135,7 +135,7 @@ def main():
for *xyxy, conf, cls in reversed(det):
targets.append((xyxy2xywh(torch.tensor(xyxy).view(
1, 4)) / gn).view(-1).tolist() + [float(conf)]) # 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", "confidence"])
@ -185,7 +185,8 @@ def main():
idx = 0
# draw the bounding box and label on the frame
label = "{}: {:.2f}%".format("Human", targets["confidence"][i] * 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

5
main_onnx_nvidia.py
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@ -134,7 +134,7 @@ def main():
for *xyxy, conf, cls in reversed(det):
targets.append((xyxy2xywh(torch.tensor(xyxy).view(
1, 4)) / gn).view(-1).tolist() + [float(conf)]) # 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", "confidence"])
@ -184,7 +184,8 @@ def main():
idx = 0
# draw the bounding box and label on the frame
label = "{}: {:.2f}%".format("Human", targets["confidence"][i] * 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
main_tensorrt_gpu.py
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@ -187,7 +187,8 @@ def main():
idx = 0
# draw the bounding box and label on the frame
label = "{}: {:.2f}%".format("Human", targets["confidence"][i] * 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
main_torch_gpu.py
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@ -186,7 +186,8 @@ def main():
idx = 0
# draw the bounding box and label on the frame
label = "{}: {:.2f}%".format("Human", targets["confidence"][i] * 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
requirements.txt
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@ -1,8 +1,5 @@
PyAutoGUI
PyDirectInput
Pillow
opencv-python
mss
numpy==1.23
pandas
pywin32

11
requirements_onnx_amd.txt Normal file
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@ -0,0 +1,11 @@
dxcam
numpy==1.23.0
onnxruntime_directml
opencv_python
pandas
PyAutoGUI
pywin32
torch_directml
# below is due to the scripts in the utils folder
pyyaml
matplotlib