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https://github.com/EyeTrackVR/EyeTrackVR.git
synced 2025-11-04 14:39:42 +08:00
remove torch for smaller binary. kill bug for mommy
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@ -121,4 +121,4 @@ class EyeTrackConfig(BaseModel):
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pass
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with open(CONFIG_FILE_NAME, "w") as settings_file:
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json.dump(obj=self.dict(), fp=settings_file)
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print("[INFO] Config Saved Successfully")
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print(f"\033[92m[INFO] Config Saved Successfully\033[0m")
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@ -30,8 +30,6 @@ import os
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os.environ["OMP_NUM_THREADS"] = "1"
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import onnxruntime
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import numpy as np
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from PIL import Image
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import torchvision.transforms as transforms
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import cv2
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import time
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import math
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@ -52,10 +50,20 @@ def run_model(input_queue, output_queue, session):
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if frame is None:
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break
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to_tensor = transforms.ToTensor()
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img_tensor = to_tensor(frame)
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img_tensor.unsqueeze_(0)
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img_np = img_tensor.numpy()
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# to_tensor = transforms.ToTensor()
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# img_tensor = to_tensor(frame)
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# img_tensor.unsqueeze_(0)
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# img_np = img_tensor.numpy()
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img_np = np.array(frame)
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# Normalize the pixel values to [0, 1] and convert the data type to float32
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img_np = img_np.astype(np.float32) / 255.0
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# Transpose the dimensions from (height, width, channels) to (channels, height, width)
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img_np = np.transpose(img_np, (2, 0, 1))
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# Add a batch dimension
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img_np = np.expand_dims(img_np, axis=0)
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ort_inputs = {session.get_inputs()[0].name: img_np}
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pre_landmark = session.run(None, ort_inputs)
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@ -180,7 +188,7 @@ class MOMMY_C(object):
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x = pre_landmark[17][0]
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y = pre_landmark[17][1]
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frame = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
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return frame, x, y, per
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return frame, float(x), float(y), per
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frame = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
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