mirror of
https://github.com/EyeTrackVR/EyeTrackVR.git
synced 2025-09-26 23:09:28 +08:00
348 lines
14 KiB
Python
348 lines
14 KiB
Python
'''
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@@@ @ @@@@@@@@#
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DADDY By: PallasNeko Optimization
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Algorithm App Implementations By: PallasNeko, Prohurtz
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Copyright (c) 2023 EyeTrackVR <3
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------------------------------------------------------------------------------------------------------
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'''
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import sys
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from typing import Tuple
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import math
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import numpy as np
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import cv2
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import onnxruntime
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from one_euro_filter import OneEuroFilter
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from utils.misc_utils import FastMedian
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# DADDY
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# Please change the name of this script and the name of the method if you have something better.
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video_path = "ezgif.com-gif-maker.avi"
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input_size = 192 # Do not change this number.
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heatmap_size = 48 # Do not change this number.
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kernel_size = 7
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model_file = "Models/daddy230210.onnx" # The model file name will be changed when performance stabilises.
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# SHA256 for model version verification
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# daddy230210.onnx = 59e59aa2a21024884200dd3acbd5e6a2e8d7209c46555fbdc727d4fe3adb68d3
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imshow_enable = False
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save_video = False
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save_filepath = 'output.mp4'
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def get_max_preds(batch_heatmaps):
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# base:https://github.com/ilovepose/DarkPose
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batch_size = batch_heatmaps.shape[0]
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num_joints = batch_heatmaps.shape[1]
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width = batch_heatmaps.shape[3]
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heatmaps_reshaped = batch_heatmaps.reshape((batch_size, num_joints, -1))
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idx = np.argmax(heatmaps_reshaped, 2)
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maxvals = np.amax(heatmaps_reshaped, 2)
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maxvals = maxvals.reshape((batch_size, num_joints, 1))
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idx = idx.reshape((batch_size, num_joints, 1))
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preds = np.tile(idx, (1, 1, 2)).astype(np.float32)
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preds[:, :, 0] = (preds[:, :, 0]) % width
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preds[:, :, 1] = np.floor((preds[:, :, 1]) / width)
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pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2))
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pred_mask = pred_mask.astype(np.float32)
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preds *= pred_mask
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return preds, maxvals
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def taylor(hm, coord):
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# base:https://github.com/ilovepose/DarkPose
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heatmap_height = hm.shape[0]
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heatmap_width = hm.shape[1]
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px = int(coord[0])
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py = int(coord[1])
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if 1 < px < heatmap_width - 2 and 1 < py < heatmap_height - 2:
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dx = 0.5 * (hm[py][px + 1] - hm[py][px - 1])
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dy = 0.5 * (hm[py + 1][px] - hm[py - 1][px])
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dxx = 0.25 * (hm[py][px + 2] - 2 * hm[py][px] + hm[py][px - 2])
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dxy = 0.25 * (hm[py + 1][px + 1] - hm[py - 1][px + 1] - hm[py + 1][px - 1] \
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+ hm[py - 1][px - 1])
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dyy = 0.25 * (hm[py + 2 * 1][px] - 2 * hm[py][px] + hm[py - 2 * 1][px])
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derivative = np.matrix([[dx], [dy]])
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hessian = np.matrix([[dxx, dxy], [dxy, dyy]])
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if dxx * dyy - dxy ** 2 != 0:
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hessianinv = hessian.I
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offset = -hessianinv * derivative
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offset = np.squeeze(np.array(offset.T), axis=0)
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coord += offset
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return coord
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def gaussian_blur(hm, kernel):
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# base:https://github.com/ilovepose/DarkPose
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border = (kernel - 1) // 2
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batch_size = hm.shape[0]
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num_joints = hm.shape[1]
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height = hm.shape[2]
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width = hm.shape[3]
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for i in range(batch_size):
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for j in range(num_joints):
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origin_max = np.max(hm[i, j])
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dr = np.zeros((height + 2 * border, width + 2 * border))
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dr[border: -border, border: -border] = hm[i, j].copy()
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dr = cv2.GaussianBlur(dr, (kernel, kernel), 0)
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hm[i, j] = dr[border: -border, border: -border].copy()
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hm[i, j] *= origin_max / np.max(hm[i, j])
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return hm
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def get_final_preds(hm, realsize):
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# base:https://github.com/ilovepose/DarkPose
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coords, maxvals = get_max_preds(hm)
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# post-processing
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hm = gaussian_blur(hm, kernel_size)
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hm = np.maximum(hm, 1e-10)
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hm = np.log(hm)
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for n in range(coords.shape[0]):
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for p in range(coords.shape[1]):
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coords[n, p] = taylor(hm[n][p], coords[n][p])
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preds = coords.copy()
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preds = (preds / heatmap_size) * realsize # input_size
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# Transform back
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# for i in range(coords.shape[0]):
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# preds[i] = transform_preds(
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# coords[i], center[i], scale[i], [heatmap_width, heatmap_height]
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# )
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return preds, maxvals
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def resize_with_pad(image: np.array,
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new_shape: Tuple[int, int],
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padding_color: Tuple[int] = (255, 255, 255)) -> np.array:
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"""
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https://gist.github.com/IdeaKing/11cf5e146d23c5bb219ba3508cca89ec
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Maintains aspect ratio and resizes with padding.
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Params:
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image: Image to be resized.
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new_shape: Expected (width, height) of new image.
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padding_color: Tuple in BGR of padding color
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Returns:
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image: Resized image with padding
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"""
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original_shape = (image.shape[1], image.shape[0])
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ratio = float(max(new_shape)) / max(original_shape)
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new_size = tuple([int(x * ratio) for x in original_shape])
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image = cv2.resize(image, new_size)
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delta_w = new_shape[0] - new_size[0]
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delta_h = new_shape[1] - new_size[1]
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top, bottom = delta_h // 2, delta_h - (delta_h // 2)
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left, right = delta_w // 2, delta_w - (delta_w // 2)
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image = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=padding_color)
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return image
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# Better Eye aspEct Ratio
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class BEER(object):
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def __init__(self):
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self.ear_th = 0.2
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self.ear_min = 0.05
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self.ear_max = 0.2
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self.p03_med = FastMedian(k=256)
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self.prev_ear = 0.5
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# todo https://peerj.com/articles/cs-943/
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def ear(self, pred):
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p15 = np.linalg.norm(pred[1]-pred[5])
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p24 = np.linalg.norm(pred[2]-pred[4])
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p03 = np.linalg.norm(pred[0]-pred[3])
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self.p03_med+p03
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if p03 > self.p03_med.median()*1.5:
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return self.prev_ear
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ear = (p15+p24)/(2*self.p03_med.median())
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self.ear_minmax(ear)
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norm_ear = self.ear_norm(ear)
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self.prev_ear = norm_ear.copy()
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return norm_ear
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def ear_minmax(self,ear):
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if ear < self.ear_min:
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self.ear_min = ear.copy()
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if ear > self.ear_max:
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self.ear_max = ear.copy()
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def ear_norm(self,ear):
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return (ear-self.ear_min)/(self.ear_max-self.ear_min) # todo:It is better to add very small values to avoid zero division.
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#
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# loopnum = 0
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#
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# Deep leArning lanDmark Detection for eYes
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class DADDY_cls(object):
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def __init__(self):
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onnxruntime.disable_telemetry_events()
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options = onnxruntime.SessionOptions()
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options.inter_op_num_threads = 1 # This number should be changed accordingly
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options.intra_op_num_threads = 4 # This number should be changed accordingly
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options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
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options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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ort_session = onnxruntime.InferenceSession(model_file, sess_options=options, providers=["CPUExecutionProvider"])
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ort_session.set_providers(['CPUExecutionProvider']) # only cpu mode
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self.ort_session = ort_session
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self.input_name = ort_session.get_inputs()[0].name
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self.output_name = ort_session.get_outputs()[0].name
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min_cutoff = 0.0004
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beta = 0.9
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input_point = np.zeros((11, 2)) # np.array([1, 1])
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self.one_euro_filter = OneEuroFilter(
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input_point,
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min_cutoff=min_cutoff,
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beta=beta
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)
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# self.ear_oef = OneEuroFilter(
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# np.zeros(1),
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# min_cutoff=min_cutoff,
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# beta=beta
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# ) # memo: Parameters need tuning
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self.beer = BEER()
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# filepath = 'test.mp4'
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# codec = cv2.VideoWriter_fourcc(*"mp4v")
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# video = cv2.VideoWriter(filepath, codec, 60.0, (200, 150), 0) # (60, 60)) # (150, 200))
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# self.video = video
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def open_video(self, video_path):
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# Temporary implementation to run
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cap = cv2.VideoCapture(video_path)
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if not cap.isOpened():
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raise IOError("Error opening video stream or file")
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self.cap = cap
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return True
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def read_frame(self):
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# Temporary implementation to run
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if not self.cap.isOpened():
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return False
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ret, frame = self.cap.read()
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if ret:
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# I have set it to grayscale (1ch) just in case, but if the frame is 1ch, this line can be commented out.
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# self.current_image=frame # debug code
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self.current_image_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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return True
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return False
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def single_run(self):
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# Temporary implementation to run
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# todo: If it's the left hand eye, flip the image left to right.
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gray_frame = self.current_image_gray.copy()
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# frame_resize=resize_with_pad(gray_frame,(input_size,input_size))
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# or
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frame_resize = cv2.resize(gray_frame, (input_size, input_size))
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imgs = np.divide(frame_resize[np.newaxis, np.newaxis], 255, dtype=np.float32) # input/255.0
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pred_heatmap = self.ort_session.run(None, {self.input_name: imgs})[0] # .reshape((-1, 2))
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# if imshow_enable:
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# heatmap = pred_heatmap.reshape((-1, heatmap_size, heatmap_size))
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# for i in range(heatmap.shape[0]):
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# cv2.imshow("heatmap_{}".format(i + 1), heatmap[i])
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pred, max_val = get_final_preds(pred_heatmap, (self.current_image_gray.shape[1], self.current_image_gray.shape[0]))
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pred = pred.reshape((-1, 2))
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# or
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# pred, max_val = get_final_preds(pred_heatmap, input_size)
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# pred = pred.reshape((-1, 2))
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# height, width = self.current_image_gray.shape[:2]
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# scale_x = input_size/ width
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# scale_y = input_size / height
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# pred[:, 0] *= scale_x
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# pred[:, 1] *= scale_y
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pred = self.one_euro_filter(pred)
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kps = pred.astype(np.int32)
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# eyecenter = kps[:6].mean(axis=0).astype(int)
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ear = self.beer.ear(pred)
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# ear=self.ear_oef(ear[np.newaxis])#memo: Parameters need tuning
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pupil_center = pred[7:].mean(axis=0)
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pupil_center_x = int(pupil_center[0])
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pupil_center_y = int(pupil_center[1])
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for i in range(kps.shape[0]):
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if i < 6:
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color = (0, 0, 255)
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elif i == 6:
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color = 128
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else:
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color = (255, 0, 0)
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# todo: We should have a proper variable for drawing.
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cv2.circle(self.current_image_gray, (kps[i, 0], kps[i, 1]), 1, color, 2)
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# cv2.putText(self.current_image_gray, str(i), (kps[i, 0] - 10, kps[i, 1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)
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# cv2.putText(self.current_image_gray, "EAR: "+str(ear), (self.current_image_gray.shape[1]//10, self.current_image_gray.shape[0]//10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255,0,0), 1)
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# global loopnum
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# if loopnum < 1350*2:
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# # self.video.write(cv2.resize(gray_frame.copy(), (200, 150), None))
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# loopnum += 1
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# else:
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# # self.video.release()
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# cv2.destroyAllWindows()
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# sys.exit()
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# if w_video:
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# video.release()
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# kps[i, :] = (x, y)
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# i == [0:6] = Inner and outer corners of eyes and eyelids
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# i == [6] = pupil
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# i == [7:] = iris
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return pupil_center_x, pupil_center_y, ear
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class External_Run_DADDY(object):
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def __init__(self):
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self.algo = DADDY_cls()
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def run(self, current_image_gray):
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self.algo.current_image_gray = current_image_gray
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pupil_x, pupil_y, ear = self.algo.single_run()
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return pupil_x, pupil_y, ear
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if __name__ == "__main__":
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daddy = DADDY_cls()
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daddy.open_video(video_path)
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while daddy.read_frame():
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_ = daddy.single_run() |