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 = """


SAME
PERSON

""" if matching_score >= 0.7 else """


DIFFERENT
PERSON

""" 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
## Contact us at https://recognito.vision NIST FRVT 1:1 Leaderboard """ ) 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("Image 1") 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("Image 2") 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)