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| import gradio as gr | |
| import mediapipe as mp | |
| import cv2 | |
| import pandas as pd | |
| from statistics import mean | |
| # Run simple face mesh | |
| mp_face_mesh = mp.solutions.face_mesh | |
| mp_drawing = mp.solutions.drawing_utils | |
| drawing_spec = mp_drawing.DrawingSpec(thickness=2, circle_radius=3) | |
| global pupilLocation, movementLeft, movementRight | |
| pupilLocation = pd.DataFrame() | |
| movementLeft = pd.DataFrame(index=['Up', 'Center', 'Down'], columns=['Left', 'Center', 'Right']) | |
| movementRight = pd.DataFrame(index=['Up', 'Center', 'Down'], columns=['Left', 'Center', 'Right']) | |
| # TO DO: | |
| # 1. Calibration screen | |
| def findIris(input_img1, input_img2, input_img3, input_img4, input_img5): | |
| global pupilLocation | |
| pupilLocation = pd.DataFrame() # Make sure it is empty | |
| images = [input_img1, input_img2, input_img3, input_img4, input_img5] | |
| output_images = [] | |
| pupil_sizes = [] | |
| with mp_face_mesh.FaceMesh(max_num_faces=1, refine_landmarks=True, | |
| static_image_mode=True, | |
| min_detection_confidence=0.45) as face_mesh: | |
| for image in images: | |
| if image is None: | |
| continue | |
| results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) | |
| if not results.multi_face_landmarks: | |
| continue | |
| annotated_image = image.copy() | |
| for face_landmarks in results.multi_face_landmarks: | |
| height, width, _ = annotated_image.shape | |
| nose = [int(face_landmarks.landmark[168].x * width), int(face_landmarks.landmark[168].y * height)] | |
| cv2.circle(annotated_image, (nose[0], nose[1]), 3, (0, 0, 255), -1) | |
| leftIrisPoints = [474, 475, 476, 477] | |
| rightIrisPoints = [469, 470, 471, 472] | |
| # right, top, left, bottom | |
| left_iris = [] | |
| for p in leftIrisPoints: | |
| point = [int(face_landmarks.landmark[p].x * width), int(face_landmarks.landmark[p].y * height)] | |
| left_iris.append(point) | |
| cv2.circle(annotated_image, point, 1, (255, 0, 255), 2) | |
| right_iris = [] | |
| for p in rightIrisPoints: | |
| point = [int(face_landmarks.landmark[p].x * width), int(face_landmarks.landmark[p].y * height)] | |
| right_iris.append(point) | |
| cv2.circle(annotated_image, point, 1, (255, 0, 255), 2) | |
| leftIris_leftside = (int(left_iris[2][0]), int(left_iris[2][1])) | |
| leftIris_rightside = (int(left_iris[0][0]), int(left_iris[0][1])) | |
| leftIris_top = (int(left_iris[1][0]), int(left_iris[1][1])) | |
| leftIris_bottom = (int(left_iris[3][0]), int(left_iris[3][1])) | |
| rightIris_leftside = (int(right_iris[2][0]), int(right_iris[2][1])) | |
| rightIris_rightside = (int(right_iris[0][0]), int(right_iris[0][1])) | |
| rightIris_top = (int(right_iris[1][0]), int(right_iris[1][1])) | |
| rightIris_bottom = (int(right_iris[3][0]), int(right_iris[3][1])) | |
| ''' | |
| cv2.circle(annotated_image, | |
| (int((leftIris_leftside[0] + leftIris_rightside[0]) / 2), | |
| int((leftIris_top[1] + leftIris_bottom[1]) / 2)), | |
| # int(abs(leftIris_leftside[0] - leftIris_rightside[0])/2 | |
| 1, | |
| (0, 255, 255), 2) | |
| cv2.circle(annotated_image, | |
| (int((rightIris_leftside[0] + rightIris_rightside[0]) / 2), | |
| int((rightIris_top[1] + rightIris_bottom[1]) / 2)), | |
| # int(abs(rightIris_leftside[0] - rightIris_rightside[0]) / 2 | |
| 1, | |
| (0, 255, 255), 2) | |
| ''' | |
| left = leftIris_leftside[0] - 150 | |
| right = rightIris_rightside[0] + 150 | |
| up = leftIris_top[1] - 50 | |
| down = leftIris_bottom[1] + 50 | |
| annotated_image = annotated_image[up:down, left:right] | |
| name = 'TBD' | |
| newRow = pd.Series([name, | |
| leftIris_leftside[0] - nose[0], | |
| leftIris_top[1] - nose[1], | |
| leftIris_rightside[0] - nose[0], | |
| leftIris_bottom[1] - nose[1], | |
| rightIris_leftside[0] - nose[0], | |
| rightIris_top[1] - nose[1], | |
| rightIris_rightside[0] - nose[0], | |
| rightIris_bottom[1] - nose[1] | |
| ]) | |
| newRow = newRow.to_frame().T | |
| pupilLocation = pd.concat([pupilLocation, newRow], axis=0, ignore_index=True) | |
| #print("Inside pupil Location = ", pupilLocation) | |
| #filename = directoy_name + 'Analysis/' + name[0:-4] + '-analysis.jpg' | |
| #cv2.imwrite(filename, annotated_image) | |
| x1 = (leftIris_leftside[0] - nose[0] + leftIris_rightside[0] - nose[0]) / 2 | |
| y1 = (leftIris_top[1] - nose[1] + leftIris_bottom[1] - nose[1]) / 2 | |
| x2 = (rightIris_leftside[0] - nose[0] + rightIris_rightside[0] - nose[0]) / 2 | |
| y2 = (rightIris_top[1] - nose[1] + rightIris_bottom[1] - nose[1]) / 2 | |
| print("Slope=", (y2 - y1) / (x2 - x1)) | |
| text = "Slope=" + str(round((y2 - y1) / (x2 - x1), 2)) | |
| cv2.putText(annotated_image, text, | |
| (5, 110), cv2.FONT_HERSHEY_SIMPLEX, | |
| 1, (255, 255, 0), 1, cv2.LINE_AA) | |
| print("left iris size in pixels = ", abs(leftIris_leftside[0] - leftIris_rightside[0])) | |
| print("Right iris size in pixels = ", abs(rightIris_leftside[0] - rightIris_rightside[0])) | |
| pupil_sizes.append(abs(leftIris_leftside[0] - leftIris_rightside[0])) | |
| pupil_sizes.append(abs(rightIris_leftside[0] - rightIris_rightside[0])) | |
| output_images.append(annotated_image) | |
| # calculate final results from pupilLocations | |
| pupilDiff = pupilLocation.copy() | |
| pupilDiff = pupilDiff.drop(pupilDiff.columns[0], axis=1) # Remove file name | |
| for i in range(pupilDiff.shape[0] - 1): # Calculate deltas | |
| pupilDiff.loc[i + 1] = abs(pupilDiff.loc[i + 1] - pupilDiff.loc[0]) | |
| print("pupilDiff=", pupilDiff) | |
| pupilDiff = pupilDiff.drop(0, axis=0) # Remove first row was was used as reference row | |
| #print("pupilDiff (in pixels)=", pupilDiff) | |
| # Find average pupil size | |
| pupil_sizes.remove(max(pupil_sizes)) | |
| pupil_sizes.remove(min(pupil_sizes)) | |
| pupil_average = mean(pupil_sizes) # this should be 11.7 mm | |
| pixels = 11.7 / pupil_average | |
| print("pixels (In MM) = ", pixels) | |
| # Left Eye movement | |
| movementLeft.iloc[0, 0] = ' ' | |
| movementLeft.iloc[0, 2] = ' ' | |
| movementLeft.iloc[1, 1] = 0 # reference point | |
| movementLeft.iloc[2, 0] = ' ' | |
| movementLeft.iloc[2, 2] = ' ' | |
| # Y movement only | |
| movementLeft.iloc[0, 1] = round(abs(pupilLocation.iloc[0, 4] - pupilLocation.iloc[1, 4]) * pixels, 0) # Up | |
| movementLeft.iloc[2, 1] = round(abs(pupilLocation.iloc[0, 2] - pupilLocation.iloc[3, 2]) * pixels, 0) # Down | |
| # X movement only | |
| movementLeft.iloc[1, 0] = round(abs(pupilLocation.iloc[0, 3] - pupilLocation.iloc[1, 3]) * pixels, 1) # Left | |
| movementLeft.iloc[1, 2] = round(abs(pupilLocation.iloc[0, 1] - pupilLocation.iloc[2, 1]) * pixels, 1) # Right | |
| # Right Eye Movement | |
| movementRight.iloc[0, 0] = ' ' | |
| movementRight.iloc[0, 2] = ' ' | |
| movementRight.iloc[1, 1] = 0 # reference point | |
| movementRight.iloc[2, 0] = ' ' | |
| movementRight.iloc[2, 2] = ' ' | |
| # Y movement only | |
| movementRight.iloc[0, 1] = round(abs(pupilLocation.iloc[0, 8] - pupilLocation.iloc[1, 8]) * pixels, 0) # Up | |
| movementRight.iloc[2, 1] = round(abs(pupilLocation.iloc[0, 6] - pupilLocation.iloc[3, 6]) * pixels, 0) # Down | |
| # X movement only | |
| movementRight.iloc[1, 0] = round(abs(pupilLocation.iloc[0, 7] - pupilLocation.iloc[1, 7]) * pixels, 0) # Left | |
| movementRight.iloc[1, 2] = round(abs(pupilLocation.iloc[0, 5] - pupilLocation.iloc[2, 5]) * pixels, 0) # Right | |
| return output_images[0], output_images[1], output_images[2], output_images[3], output_images[4], pupilLocation, movementLeft, movementRight | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| """ | |
| # Range of Motion Image Analysis | |
| Take 5 pictures below looking stright, left, right, up & down | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| img1 = gr.Image(shape=(1000, 1000), source='webcam', label='Front') | |
| with gr.Column(scale=1): | |
| out1 = gr.Image(label='Out-Front') | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| img2 = gr.Image(shape=(1000, 1000), source='webcam', label='Left') | |
| with gr.Column(scale=1): | |
| out2 = gr.Image(label='Out-Left') | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| img3 = gr.Image(shape=(1000, 1000), source='webcam', label='Right') | |
| with gr.Column(scale=1): | |
| out3 = gr.Image(label='Out-Right') | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| img4 = gr.Image(shape=(1000, 1000), source='webcam', label='Up') | |
| with gr.Column(scale=1): | |
| out4 = gr.Image(label='Out-Up') | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| img5 = gr.Image(shape=(1000, 1000), source='webcam', label='Down') | |
| with gr.Column(scale=1): | |
| out5 = gr.Image(label='Down-Right') | |
| b = gr.Button("Go!") | |
| gr.Markdown( | |
| """ | |
| Pupil Locations: | |
| """) | |
| pupilData = gr.Dataframe(pupilLocation) | |
| gr.Markdown( | |
| """ | |
| # Left eye results (in mm): | |
| """) | |
| movementDataLeft = gr.Dataframe(movementLeft) | |
| gr.Markdown( | |
| """ | |
| # Right eye results (in mm): | |
| """) | |
| movementDataRight = gr.Dataframe(movementRight) | |
| inp = [img1, img2, img3, img4, img5] | |
| out = [out1, out2, out3, out4, out5, pupilData, movementDataLeft, movementDataRight] | |
| b.click(fn=findIris, inputs=inp, outputs=out) | |
| demo.launch(auth=("Andrew", "Andrew")) #, share=True |