| import os | |
| import gradio as gr | |
| import requests | |
| import json | |
| from PIL import Image | |
| def get_attributes(json): | |
| liveness = "GENUINE" if json.get('liveness') >= 0.5 else "FAKE" | |
| attr = json.get('attribute') | |
| age = attr.get('age') | |
| gender = attr.get('gender') | |
| emotion = attr.get('emotion') | |
| ethnicity = attr.get('ethnicity') | |
| mask = [attr.get('face_mask')] | |
| if attr.get('glasses') == 'USUAL': | |
| mask.append('GLASSES') | |
| if attr.get('glasses') == 'DARK': | |
| mask.append('SUNGLASSES') | |
| eye = [] | |
| if attr.get('eye_left') >= 0.3: | |
| eye.append('LEFT') | |
| if attr.get('eye_right') >= 0.3: | |
| eye.append('RIGHT') | |
| facehair = attr.get('facial_hair') | |
| haircolor = attr.get('hair_color') | |
| hairtype = attr.get('hair_type') | |
| headwear = attr.get('headwear') | |
| activity = [] | |
| if attr.get('food_consumption') >= 0.5: | |
| activity.append('EATING') | |
| if attr.get('phone_recording') >= 0.5: | |
| activity.append('PHONE_RECORDING') | |
| if attr.get('phone_use') >= 0.5: | |
| activity.append('PHONE_USE') | |
| if attr.get('seatbelt') >= 0.5: | |
| activity.append('SEATBELT') | |
| if attr.get('smoking') >= 0.5: | |
| activity.append('SMOKING') | |
| pitch = attr.get('pitch') | |
| roll = attr.get('roll') | |
| yaw = attr.get('yaw') | |
| quality = attr.get('quality') | |
| return liveness, age, gender, emotion, ethnicity, mask, eye, facehair, haircolor, hairtype, headwear, activity, pitch, roll, yaw, quality | |
| def compare_face(frame1, frame2): | |
| url = "https://recognito.p.rapidapi.com/api/face" | |
| try: | |
| files = {'image1': open(frame1, 'rb'), 'image2': open(frame2, 'rb')} | |
| headers = {"X-RapidAPI-Key": os.environ.get("API_KEY")} | |
| r = requests.post(url=url, files=files, headers=headers) | |
| except: | |
| raise gr.Error("Please select images files!") | |
| faces = None | |
| try: | |
| image1 = Image.open(frame1) | |
| image2 = Image.open(frame2) | |
| face1 = Image.new('RGBA',(150, 150), (80,80,80,0)) | |
| face2 = Image.new('RGBA',(150, 150), (80,80,80,0)) | |
| liveness1, age1, gender1, emotion1, ethnicity1, mask1, eye1, facehair1, haircolor1, hairtype1, headwear1, activity1, pitch1, roll1, yaw1, quality1 = [None] * 16 | |
| liveness2, age2, gender2, emotion2, ethnicity2, mask2, eye2, facehair2, haircolor2, hairtype2, headwear2, activity2, pitch2, roll2, yaw2, quality2 = [None] * 16 | |
| res1 = r.json().get('image1') | |
| if res1 is not None and res1: | |
| face = res1.get('detection') | |
| x1 = face.get('x') | |
| y1 = face.get('y') | |
| x2 = x1 + face.get('w') | |
| y2 = y1 + face.get('h') | |
| if x1 < 0: | |
| x1 = 0 | |
| if y1 < 0: | |
| y1 = 0 | |
| if x2 >= image1.width: | |
| x2 = image1.width - 1 | |
| if y2 >= image1.height: | |
| y2 = image1.height - 1 | |
| face1 = image1.crop((x1, y1, x2, y2)) | |
| face_image_ratio = face1.width / float(face1.height) | |
| resized_w = int(face_image_ratio * 150) | |
| resized_h = 150 | |
| face1 = face1.resize((int(resized_w), int(resized_h))) | |
| liveness1, age1, gender1, emotion1, ethnicity1, mask1, eye1, facehair1, haircolor1, hairtype1, headwear1, activity1, pitch1, roll1, yaw1, quality1 = get_attributes(res1) | |
| res2 = r.json().get('image2') | |
| if res2 is not None and res2: | |
| face = res2.get('detection') | |
| x1 = face.get('x') | |
| y1 = face.get('y') | |
| x2 = x1 + face.get('w') | |
| y2 = y1 + face.get('h') | |
| if x1 < 0: | |
| x1 = 0 | |
| if y1 < 0: | |
| y1 = 0 | |
| if x2 >= image2.width: | |
| x2 = image2.width - 1 | |
| if y2 >= image2.height: | |
| y2 = image2.height - 1 | |
| face2 = image2.crop((x1, y1, x2, y2)) | |
| face_image_ratio = face2.width / float(face2.height) | |
| resized_w = int(face_image_ratio * 150) | |
| resized_h = 150 | |
| face2 = face2.resize((int(resized_w), int(resized_h))) | |
| liveness2, age2, gender2, emotion2, ethnicity2, mask2, eye2, facehair2, haircolor2, hairtype2, headwear2, activity2, pitch2, roll2, yaw2, quality2 = get_attributes(res2) | |
| except: | |
| pass | |
| matching_result = "" | |
| if face1 is not None and face2 is not None: | |
| matching_score = r.json().get('matching_score') | |
| if matching_score is not None: | |
| matching_result = """<br/><br/><br/><h1 style="text-align: center;color: #05ee3c;">SAME<br/>PERSON</h1>""" if matching_score >= 0.7 else """<br/><br/><br/><h1 style="text-align: center;color: red;">DIFFERENT<br/>PERSON</h1>""" | |
| return [r.json(), [face1, face2], matching_result, | |
| liveness1, age1, gender1, emotion1, ethnicity1, mask1, eye1, facehair1, haircolor1, hairtype1, headwear1, activity1, pitch1, roll1, yaw1, quality1, | |
| liveness2, age2, gender2, emotion2, ethnicity2, mask2, eye2, facehair2, haircolor2, hairtype2, headwear2, activity2, pitch2, roll2, yaw2, quality2] | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| """ | |
| # **Recognito Face Analysis** | |
| ## NIST FRVT Top #1 Face Recognition Algorithm Developer<br/> | |
| ## Contact us at https://recognito.vision | |
| <img src="https://recognito.vision/wp-content/uploads/2023/12/black-1.png" alt="NIST FRVT 1:1 Leaderboard" width="50%"> | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| compare_face_input1 = gr.Image(label="Image1", type='filepath', height=270) | |
| gr.Examples(['examples/1.jpg', 'examples/2.jpg', 'examples/3.jpg', 'examples/4.jpg'], | |
| inputs=compare_face_input1) | |
| compare_face_input2 = gr.Image(label="Image2", type='filepath', height=270) | |
| gr.Examples(['examples/5.jpg', 'examples/6.jpg', 'examples/7.jpg', 'examples/8.jpg'], | |
| inputs=compare_face_input2) | |
| compare_face_button = gr.Button("Face Analysis & Verification", variant="primary", size="lg") | |
| with gr.Column(scale=2): | |
| with gr.Row(): | |
| compare_face_output = gr.Gallery(label="Faces", height=230, columns=[2], rows=[1]) | |
| with gr.Column(variant="panel"): | |
| compare_result = gr.Markdown("") | |
| with gr.Row(): | |
| with gr.Column(variant="panel"): | |
| gr.Markdown("<b>Image 1<b/>") | |
| liveness1 = gr.CheckboxGroup(["GENUINE", "FAKE"], label="Liveness") | |
| age1 = gr.Number(0, label="Age") | |
| gender1 = gr.CheckboxGroup(["MALE", "FEMALE"], label="Gender") | |
| emotion1 = gr.CheckboxGroup(["HAPPINESS", "ANGER", "FEAR", "NEUTRAL", "SADNESS", "SURPRISE"], label="Emotion") | |
| ethnicity1 = gr.CheckboxGroup(["ASIAN", "BLACK", "CAUCASIAN", "EAST_INDIAN"], label="Ethnicity") | |
| mask1 = gr.CheckboxGroup(["LOWER_FACE_MASK", "FULL_FACE_MASK", "OTHER_MASK", "GLASSES", "SUNGLASSES"], label="Mask & Glasses") | |
| eye1 = gr.CheckboxGroup(["LEFT", "RIGHT"], label="Eye Open") | |
| facehair1 = gr.CheckboxGroup(["BEARD", "BRISTLE", "MUSTACHE", "SHAVED"], label="Facial Hair") | |
| haircolor1 = gr.CheckboxGroup(["BLACK", "BLOND", "BROWN"], label="Hair Color") | |
| hairtype1 = gr.CheckboxGroup(["BALD", "SHORT", "MEDIUM", "LONG"], label="Hair Type") | |
| headwear1 = gr.CheckboxGroup(["B_CAP", "CAP", "HAT", "HELMET", "HOOD"], label="Head Wear") | |
| activity1 = gr.CheckboxGroup(["EATING", "PHONE_RECORDING", "PHONE_USE", "SMOKING", "SEATBELT"], label="Activity") | |
| with gr.Row(): | |
| pitch1 = gr.Number(0, label="Pitch") | |
| roll1 = gr.Number(0, label="Roll") | |
| yaw1 = gr.Number(0, label="Yaw") | |
| quality1 = gr.Number(0, label="Quality") | |
| with gr.Column(variant="panel"): | |
| gr.Markdown("<b>Image 2<b/>") | |
| liveness2 = gr.CheckboxGroup(["GENUINE", "FAKE"], label="Liveness") | |
| age2 = gr.Number(0, label="Age") | |
| gender2 = gr.CheckboxGroup(["MALE", "FEMALE"], label="Gender") | |
| emotion2 = gr.CheckboxGroup(["HAPPINESS", "ANGER", "FEAR", "NEUTRAL", "SADNESS", "SURPRISE"], label="Emotion") | |
| ethnicity2 = gr.CheckboxGroup(["ASIAN", "BLACK", "CAUCASIAN", "EAST_INDIAN"], label="Ethnicity") | |
| mask2 = gr.CheckboxGroup(["LOWER_FACE_MASK", "FULL_FACE_MASK", "OTHER_MASK", "GLASSES", "SUNGLASSES"], label="Mask & Glasses") | |
| eye2 = gr.CheckboxGroup(["LEFT", "RIGHT"], label="Eye Open") | |
| facehair2 = gr.CheckboxGroup(["BEARD", "BRISTLE", "MUSTACHE", "SHAVED"], label="Facial Hair") | |
| haircolor2 = gr.CheckboxGroup(["BLACK", "BLOND", "BROWN"], label="Hair Color") | |
| hairtype2 = gr.CheckboxGroup(["BALD", "SHORT", "MEDIUM", "LONG"], label="Hair Type") | |
| headwear2 = gr.CheckboxGroup(["B_CAP", "CAP", "HAT", "HELMET", "HOOD"], label="Head Wear") | |
| activity2 = gr.CheckboxGroup(["EATING", "PHONE_RECORDING", "PHONE_USE", "SMOKING", "SEATBELT"], label="Activity") | |
| with gr.Row(): | |
| pitch2 = gr.Number(0, label="Pitch") | |
| roll2 = gr.Number(0, label="Roll") | |
| yaw2 = gr.Number(0, label="Yaw") | |
| quality2 = gr.Number(0, label="Quality") | |
| compare_result_output = gr.JSON(label='Result', visible=False) | |
| compare_face_button.click(compare_face, inputs=[compare_face_input1, compare_face_input2], outputs=[compare_result_output, compare_face_output, compare_result, | |
| liveness1, age1, gender1, emotion1, ethnicity1, mask1, eye1, facehair1, haircolor1, hairtype1, headwear1, activity1, pitch1, roll1, yaw1, quality1, | |
| liveness2, age2, gender2, emotion2, ethnicity2, mask2, eye2, facehair2, haircolor2, hairtype2, headwear2, activity2, pitch2, roll2, yaw2, quality2]) | |
| demo.launch(server_name="0.0.0.0", server_port=7860, show_api=False) |