""" ------------------------------------------------------------------------------------------------------ ,@@@@@@ @@@@@@@@@@@ @@@ @@@@@@@@@@@@ @@@@@@@@@@@ @@@@@@@@@@@@@ @@@@@@@@@@@@@@ @@@@@@@/ ,@@@@@@@@@@@@@ /@@@@@@@@@@@@@@@ @@@@@@@@ @@@@@@@@@@@@@@@@@@@@@@@@ @@@@@ @@@@@@@@ @@@@@ ,@@@ @@@@& @@@@@@. @@@@ @@@ @@@@@@@@@/ @@@@@ ,@@@. @@@@@@((@ @@@@( //@@@ ,, @@@@ @@@@@ @@@( @@@@@@@ @@@ @ @@@@@@@@# @@@@@@@@@@@@@@@@@ @@@@@@@@@@@@@( DADDY By: PallasNeko Optimization Algorithm App Implementations By: PallasNeko, Prohurtz Copyright (c) 2025 EyeTrackVR <3 LICENSE: Babble Software Distribution License 1.0 ------------------------------------------------------------------------------------------------------ """ import sys from typing import Tuple import math import platform import numpy as np import cv2 import onnxruntime from one_euro_filter import OneEuroFilter from utils.misc_utils import FastMedian, resource_path import os os.environ["OMP_NUM_THREADS"] = "1" # DADDY # Please change the name of this script and the name of the method if you have something better. video_path = "ezgif.com-gif-maker.avi" input_size = 192 # Do not change this number. heatmap_size = 48 # Do not change this number. kernel_size = 7 if platform.system() == "Darwin": model_file = "Models/daddy230210.onnx" # The model file name will be changed when performance stabilises. # funny MacOS files issues :P else: model_file = "Models/daddy230210.onnx" # The model file name will be changed when performance stabilises. # SHA256 for model version verification # daddy230210.onnx = 59e59aa2a21024884200dd3acbd5e6a2e8d7209c46555fbdc727d4fe3adb68d3 imshow_enable = False save_video = False save_filepath = "output.mp4" def get_max_preds(batch_heatmaps): # base:https://github.com/ilovepose/DarkPose batch_size = batch_heatmaps.shape[0] num_joints = batch_heatmaps.shape[1] width = batch_heatmaps.shape[3] heatmaps_reshaped = batch_heatmaps.reshape((batch_size, num_joints, -1)) idx = np.argmax(heatmaps_reshaped, 2) maxvals = np.amax(heatmaps_reshaped, 2) maxvals = maxvals.reshape((batch_size, num_joints, 1)) idx = idx.reshape((batch_size, num_joints, 1)) preds = np.tile(idx, (1, 1, 2)).astype(np.float32) preds[:, :, 0] = (preds[:, :, 0]) % width preds[:, :, 1] = np.floor((preds[:, :, 1]) / width) pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2)) pred_mask = pred_mask.astype(np.float32) preds *= pred_mask return preds, maxvals def taylor(hm, coord): # base:https://github.com/ilovepose/DarkPose heatmap_height = hm.shape[0] heatmap_width = hm.shape[1] px = int(coord[0]) py = int(coord[1]) if 1 < px < heatmap_width - 2 and 1 < py < heatmap_height - 2: dx = 0.5 * (hm[py][px + 1] - hm[py][px - 1]) dy = 0.5 * (hm[py + 1][px] - hm[py - 1][px]) dxx = 0.25 * (hm[py][px + 2] - 2 * hm[py][px] + hm[py][px - 2]) dxy = 0.25 * (hm[py + 1][px + 1] - hm[py - 1][px + 1] - hm[py + 1][px - 1] + hm[py - 1][px - 1]) dyy = 0.25 * (hm[py + 2 * 1][px] - 2 * hm[py][px] + hm[py - 2 * 1][px]) derivative = np.matrix([[dx], [dy]]) hessian = np.matrix([[dxx, dxy], [dxy, dyy]]) if dxx * dyy - dxy**2 != 0: hessianinv = hessian.I offset = -hessianinv * derivative offset = np.squeeze(np.array(offset.T), axis=0) coord += offset return coord def gaussian_blur(hm, kernel): # base:https://github.com/ilovepose/DarkPose border = (kernel - 1) // 2 batch_size = hm.shape[0] num_joints = hm.shape[1] height = hm.shape[2] width = hm.shape[3] for i in range(batch_size): for j in range(num_joints): origin_max = np.max(hm[i, j]) dr = np.zeros((height + 2 * border, width + 2 * border)) dr[border:-border, border:-border] = hm[i, j].copy() dr = cv2.GaussianBlur(dr, (kernel, kernel), 0) hm[i, j] = dr[border:-border, border:-border].copy() hm[i, j] *= origin_max / np.max(hm[i, j]) return hm def get_final_preds(hm, realsize): # base:https://github.com/ilovepose/DarkPose coords, maxvals = get_max_preds(hm) # post-processing hm = gaussian_blur(hm, kernel_size) hm = np.maximum(hm, 1e-10) hm = np.log(hm) for n in range(coords.shape[0]): for p in range(coords.shape[1]): coords[n, p] = taylor(hm[n][p], coords[n][p]) preds = coords.copy() preds = (preds / heatmap_size) * realsize # input_size # Transform back # for i in range(coords.shape[0]): # preds[i] = transform_preds( # coords[i], center[i], scale[i], [heatmap_width, heatmap_height] # ) return preds, maxvals def resize_with_pad( image: np.array, new_shape: Tuple[int, int], padding_color: Tuple[int] = (255, 255, 255) ) -> np.array: """ https://gist.github.com/IdeaKing/11cf5e146d23c5bb219ba3508cca89ec Maintains aspect ratio and resizes with padding. Params: image: Image to be resized. new_shape: Expected (width, height) of new image. padding_color: Tuple in BGR of padding color Returns: image: Resized image with padding """ original_shape = (image.shape[1], image.shape[0]) ratio = float(max(new_shape)) / max(original_shape) new_size = tuple([int(x * ratio) for x in original_shape]) image = cv2.resize(image, new_size) delta_w = new_shape[0] - new_size[0] delta_h = new_shape[1] - new_size[1] top, bottom = delta_h // 2, delta_h - (delta_h // 2) left, right = delta_w // 2, delta_w - (delta_w // 2) image = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=padding_color) return image # Better Eye aspEct Ratio class BEER(object): def __init__(self): self.ear_th = 0.2 self.ear_min = 0.05 self.ear_max = 0.2 self.p03_med = FastMedian(k=256) self.prev_ear = 0.5 # todo https://peerj.com/articles/cs-943/ def ear(self, pred): p15 = np.linalg.norm(pred[1] - pred[5]) p24 = np.linalg.norm(pred[2] - pred[4]) p03 = np.linalg.norm(pred[0] - pred[3]) self.p03_med + p03 if p03 > self.p03_med.median() * 1.5: return self.prev_ear ear = (p15 + p24) / (2 * self.p03_med.median()) self.ear_minmax(ear) norm_ear = self.ear_norm(ear) self.prev_ear = norm_ear.copy() return norm_ear def ear_minmax(self, ear): if ear < self.ear_min: self.ear_min = ear.copy() if ear > self.ear_max: self.ear_max = ear.copy() def ear_norm(self, ear): return (ear - self.ear_min) / ( self.ear_max - self.ear_min ) # todo:It is better to add very small values to avoid zero division. # # loopnum = 0 # # Deep leArning lanDmark Detection for eYes class DADDY_cls(object): def __init__(self): onnxruntime.disable_telemetry_events() options = onnxruntime.SessionOptions() options.inter_op_num_threads = 1 # This number should be changed accordingly options.intra_op_num_threads = 1 # This number should be changed accordingly options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL ort_session = onnxruntime.InferenceSession( resource_path(model_file), sess_options=options, providers=["CPUExecutionProvider"] ) ort_session.set_providers(["CPUExecutionProvider"]) # only cpu mode self.ort_session = ort_session self.input_name = ort_session.get_inputs()[0].name self.output_name = ort_session.get_outputs()[0].name min_cutoff = 0.0004 beta = 0.9 input_point = np.zeros((11, 2)) # np.array([1, 1]) self.one_euro_filter = OneEuroFilter(input_point, min_cutoff=min_cutoff, beta=beta) # self.ear_oef = OneEuroFilter( # np.zeros(1), # min_cutoff=min_cutoff, # beta=beta # ) # memo: Parameters need tuning self.beer = BEER() # filepath = 'test.mp4' # codec = cv2.VideoWriter_fourcc(*"mp4v") # video = cv2.VideoWriter(filepath, codec, 60.0, (200, 150), 0) # (60, 60)) # (150, 200)) # self.video = video def open_video(self, video_path): # Temporary implementation to run cap = cv2.VideoCapture(video_path) if not cap.isOpened(): raise IOError("Error opening video stream or file") self.cap = cap return True def read_frame(self): # Temporary implementation to run if not self.cap.isOpened(): return False ret, frame = self.cap.read() if ret: # I have set it to grayscale (1ch) just in case, but if the frame is 1ch, this line can be commented out. # self.current_image=frame # debug code self.current_image_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) return True return False def single_run(self): # Temporary implementation to run # todo: If it's the left hand eye, flip the image left to right. gray_frame = self.current_image_gray.copy() # frame_resize=resize_with_pad(gray_frame,(input_size,input_size)) # or frame_resize = cv2.resize(gray_frame, (input_size, input_size)) imgs = np.divide(frame_resize[np.newaxis, np.newaxis], 255, dtype=np.float32) # input/255.0 pred_heatmap = self.ort_session.run(None, {self.input_name: imgs})[0] # .reshape((-1, 2)) # if imshow_enable: # heatmap = pred_heatmap.reshape((-1, heatmap_size, heatmap_size)) # for i in range(heatmap.shape[0]): # cv2.imshow("heatmap_{}".format(i + 1), heatmap[i]) pred, max_val = get_final_preds( pred_heatmap, (self.current_image_gray.shape[1], self.current_image_gray.shape[0]) ) pred = pred.reshape((-1, 2)) # or # pred, max_val = get_final_preds(pred_heatmap, input_size) # pred = pred.reshape((-1, 2)) # height, width = self.current_image_gray.shape[:2] # scale_x = input_size/ width # scale_y = input_size / height # pred[:, 0] *= scale_x # pred[:, 1] *= scale_y pred = self.one_euro_filter(pred) kps = pred.astype(np.int32) # eyecenter = kps[:6].mean(axis=0).astype(int) ear = self.beer.ear(pred) # ear=self.ear_oef(ear[np.newaxis])#memo: Parameters need tuning pupil_center = pred[7:].mean(axis=0) pupil_center_x = int(pupil_center[0]) pupil_center_y = int(pupil_center[1]) for i in range(kps.shape[0]): if i < 6: color = (0, 0, 255) elif i == 6: color = 128 else: color = (255, 0, 0) # todo: We should have a proper variable for drawing. cv2.circle(self.current_image_gray, (kps[i, 0], kps[i, 1]), 1, color, 2) # 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) # 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) # global loopnum # if loopnum < 1350*2: # # self.video.write(cv2.resize(gray_frame.copy(), (200, 150), None)) # loopnum += 1 # else: # # self.video.release() # cv2.destroyAllWindows() # sys.exit() # if w_video: # video.release() # kps[i, :] = (x, y) # i == [0:6] = Inner and outer corners of eyes and eyelids # i == [6] = pupil # i == [7:] = iris return pupil_center_x, pupil_center_y, ear class External_Run_DADDY(object): def __init__(self): self.algo = DADDY_cls() def run(self, current_image_gray): self.algo.current_image_gray = current_image_gray pupil_x, pupil_y, ear = self.algo.single_run() return pupil_x, pupil_y, ear if __name__ == "__main__": daddy = DADDY_cls() daddy.open_video(video_path) while daddy.read_frame(): _ = daddy.single_run()