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import os |
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import gradio as gr |
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import requests |
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import json |
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import io |
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from gradio.components import Image |
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from PIL import Image as PILImage, ImageDraw, ImageFont |
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from PIL import Image |
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from PIL import Image |
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import io |
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import base64 |
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def face_crop(image, face_rect): |
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x = face_rect.get('x') |
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y = face_rect.get('y') |
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width = face_rect.get('width') |
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height = face_rect.get('height') |
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if x < 0: |
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x = 0 |
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if y < 0: |
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y = 0 |
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if x + width >= image.width: |
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width = image.width - x |
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if y + height >= image.height: |
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height = image.height - y |
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face_image = image.crop((x, y, x + width - 1, y + height - 1)) |
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face_image_ratio = face_image.width / float(face_image.height) |
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resized_w = int(face_image_ratio * 150) |
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resized_h = 150 |
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face_image = face_image.resize((int(resized_w), int(resized_h))) |
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return face_image |
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def pil_image_to_base64(image, format="PNG"): |
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""" |
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Converts a PIL.Image object to a Base64-encoded string. |
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:param image: PIL.Image object |
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:param format: Format to save the image, e.g., "PNG", "JPEG" |
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:return: Base64-encoded string |
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""" |
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buffer = io.BytesIO() |
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image.save(buffer, format=format) |
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buffer.seek(0) |
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base64_string = base64.b64encode(buffer.getvalue()).decode('utf-8') |
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return base64_string |
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def compare_face(image1, image2, verifyThreshold): |
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try: |
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img_bytes1 = io.BytesIO() |
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image1.save(img_bytes1, format="JPEG") |
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img_bytes1.seek(0) |
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except: |
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return ["Failed to open image1", {"resultCode": "Failed to open image1"}] |
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try: |
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img_bytes2 = io.BytesIO() |
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image2.save(img_bytes2, format="JPEG") |
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img_bytes2.seek(0) |
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except: |
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return ["Failed to open image2", {"resultCode": "Failed to open image2"}] |
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url = "http://127.0.0.1:9000/compare_face" |
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files = {'image1': img_bytes1, 'image2': img_bytes2} |
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result = requests.post(url=url, files=files) |
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if result.ok: |
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json_result = result.json() |
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if json_result.get("resultCode") != "Ok": |
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return [json_result.get("resultCode"), json_result] |
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html = "" |
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faces1 = json_result.get("faces1", {}) |
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faces2 = json_result.get("faces2", {}) |
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results = json_result.get("results", {}) |
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for result in results: |
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score = result.get('score') |
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face1_idx = result.get('face1') |
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face2_idx = result.get('face2') |
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face_image1 = face_crop(image1, faces1[face1_idx]) |
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face_value1 = ('<img src="data:image/png;base64,{base64_image}" style="width: 100px; height: auto; object-fit: contain;"/>').format(base64_image=pil_image_to_base64(face_image1, format="PNG")) |
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face_image2 = face_crop(image2, faces2[face2_idx]) |
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face_value2 = ('<img src="data:image/png;base64,{base64_image}" style="width: 100px; height: auto; object-fit: contain;"/>').format(base64_image=pil_image_to_base64(face_image2, format="PNG")) |
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match_icon = '<svg fill="red" width="19" height="32" viewBox="0 0 19 32"><path d="M0 13.92V10.2H19V13.92H0ZM0 21.64V17.92H19V21.64H0Z"></path><path d="M14.08 0H18.08L5.08 32H1.08L14.08 0Z"></path></svg>' |
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if score > verifyThreshold: |
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match_icon = '<svg fill="green" width="19" height="32" viewBox="0 0 19 32"><path d="M0 13.9202V10.2002H19V13.9202H0ZM0 21.6402V17.9202H19V21.6402H0Z"></path></svg>' |
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item_value = ('<div style="align-items: center; gap: 10px; display: flex; flex-direction: column;">' |
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'<div style="display: flex; align-items: center; gap: 20px;">' |
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'{face_value1}' |
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'{match_icon}' |
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'{face_value2}' |
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'</div>' |
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'<div style="text-align: center; margin-top: 10px;">' |
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'Score: {score}' |
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'</div>' |
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'</div>' |
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).format(face_value1=face_value1, face_value2=face_value2, match_icon=match_icon, score=f"{score:.2f}") |
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html += item_value |
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html += '<hr style="border: 1px solid #C0C0C0; margin: 10px 0;"/>' |
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return [html, json_result] |
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else: |
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return [result.text, {"resultCode": result.text}] |
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def detect_face(image): |
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try: |
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img_bytes = io.BytesIO() |
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image.save(img_bytes, format="JPEG") |
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img_bytes.seek(0) |
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except: |
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return ["Failed to open image", {"resultCode": "Failed to open image"}] |
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url = "http://127.0.0.1:9000/detect_face" |
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files = {'image': img_bytes} |
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result = requests.post(url=url, files=files) |
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if result.ok: |
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json_result = result.json() |
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html = "" |
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resultCode = json_result.get("resultCode") |
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if resultCode == "Ok": |
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faces = json_result.get("result", {}) |
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for face in faces: |
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face_rect = face.get("rect", {}) |
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angles = face.get("angles", {}) |
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age_gender = face.get("age_gender", {}) |
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emotion = face.get("emotion", {}) |
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attribute = face.get("attribute", {}) |
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face_image = face_crop(image, face_rect) |
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face_value = ('<img src="data:image/png;base64,{base64_image}" style="width: 100px; height: auto; object-fit: contain;"/>').format(base64_image=pil_image_to_base64(face_image, format="PNG")) |
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item_value = ('<div style="display: flex; justify-content: center; align-items: flex-start; margin: 10px;">' |
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'<div style="display: flex; align-items: flex-start; gap: 40px; ">' |
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'{face_value}' |
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'<div style="display: flex; gap: 20px; border-left: 1px solid #C0C0C0; padding-left: 20px;">' |
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'<div>' |
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'<p><b>Age</b></p>' |
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'<p><b>Gender</b></p>' |
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'<p><b>Mask</b></p>' |
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'<p><b>Left Eye</b></p>' |
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'<p><b>Right Eye</b></p>' |
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'<p><b>Yaw</b></p>' |
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'<p><b>Roll</b></p>' |
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'<p><b>Pitch</b></p>' |
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'</div>' |
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'<div>' |
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'<p>{age}</p>' |
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'<p>{gender}</p>' |
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'<p>{masked}</p>' |
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'<p>{left_eye}</p>' |
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'<p>{right_eye}</p>' |
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'<p>{yaw}</p>' |
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'<p>{roll}</p>' |
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'<p>{pitch}</p>' |
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'</div>' |
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'</div>' |
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'<div style="display: flex; gap: 20px; border-left: 1px solid #C0C0C0; padding-left: 20px;">' |
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'<div>' |
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'<p><b>Neutral</b></p>' |
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'<p><b>Happy</b></p>' |
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'<p><b>Angry</b></p>' |
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'<p><b>Surprised</b></p>' |
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'<p><b>Disgusted</b></p>' |
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'<p><b>Sad</b></p>' |
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'<p><b>Scared</b></p>' |
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'</div>' |
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'<div>' |
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'<p>{neutral}</p>' |
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'<p>{happy}</p>' |
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'<p>{angry}</p>' |
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'<p>{surprised}</p>' |
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'<p>{disgusted}</p>' |
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'<p>{sad}</p>' |
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'<p>{scared}</p>' |
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'</div>' |
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'</div>' |
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'</div></div>').format(face_value=face_value, |
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age=age_gender.get('age'), |
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gender="Female" if age_gender.get('gender') == 0 else "Male", |
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neutral=f"{emotion.get('neutral'):.2f}", |
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happy=f"{emotion.get('happy'):.2f}", |
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angry=f"{emotion.get('angry'):.2f}", |
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surprised=f"{emotion.get('surprised'):.2f}", |
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disgusted=f"{emotion.get('disgusted'):.2f}", |
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sad=f"{emotion.get('sad'):.2f}", |
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scared=f"{emotion.get('scared'):.2f}", |
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masked="Yes" if attribute.get('masked') == 1 else "No", |
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left_eye="Open" if attribute.get('left_eye_opened') == 1 else "Close", |
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right_eye="Open" if attribute.get('right_eye_opened') == 1 else "Close", |
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yaw=f"{angles.get('yaw'):.2f}", |
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roll=f"{angles.get('roll'):.2f}", |
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pitch=f"{angles.get('pitch'):.2f}", |
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) |
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html += item_value |
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html += '<hr style="border: 1px solid #C0C0C0; margin: 10px 0;"/>' |
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else: |
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html = "No face!" |
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return [html, json_result] |
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else: |
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return [result.text, {"resultCode": result.text}] |
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with gr.Blocks() as demo: |
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gr.Markdown( |
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""" |
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<div style="display: flex;align-items: center;"> |
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<img alt="Opulentyn Logo" src="https://github.com/user-attachments/assets/5fc78032-bff2-4f7e-a174-7d64b22f506d" width="350"/> |
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<div> |
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<h1>Face Recognition/Face Attribute</h1> |
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<p>We offer <b>on-premises</b> OCR and liveness check solutions available with a <b>perpetual license</b>.</p> |
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</div> |
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</div> |
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## π€ Talk to us |
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<div style="display: flex; align-items: center;"> |
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<a href="https://opulentyn.com" target="_blank"> |
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<img src="https://img.shields.io/badge/Website-https%3A%2F%2Fopulentyn.com-blue?style=flat&logo=google-chrome&logoColor=white" alt="Website"> |
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</a> |
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<a href="mailto:support@opulentyn.com"> |
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<img src="https://img.shields.io/badge/Email-support%40opulentyn.com-blue?style=flat&logo=gmail&logoColor=white" alt="Email"> |
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</a> |
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<a href="https://join.slack.com/t/opulentyn/shared_invite/zt-2s230jtbq-dWBs8XUZcrYim~nUqiimSA" target="_blank"> |
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<img src="https://img.shields.io/badge/Slack-support--sdk-blueviolet?style=flat&logo=slack&logoColor=white" alt="Slack"> |
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</a> |
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</div> |
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""" |
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) |
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with gr.TabItem("Face Recognition"): |
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with gr.Row(): |
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with gr.Column(scale=7): |
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with gr.Row(): |
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with gr.Column(): |
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image_input1 = gr.Image(type='pil') |
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gr.Examples(['examples/1.webp', 'examples/2.webp', 'examples/3.webp', 'examples/4.webp'], |
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inputs=image_input1) |
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with gr.Column(): |
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image_input2 = gr.Image(type='pil') |
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gr.Examples(['examples/5.webp', 'examples/6.webp', 'examples/7.webp', 'examples/8.webp'], |
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inputs=image_input2) |
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verifyThreshold = gr.Slider(minimum=0, maximum=1, value=0.67, label="Verify Threshold") |
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face_recog_button = gr.Button("Face Recognition") |
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with gr.Column(scale=3): |
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with gr.TabItem("Output"): |
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recog_html_output = gr.HTML() |
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with gr.TabItem("JSON"): |
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recog_json_output = gr.JSON() |
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with gr.TabItem("Face Attribute"): |
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with gr.Row(): |
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with gr.Column(): |
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image_input = gr.Image(type='pil') |
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gr.Examples(['examples/11.webp', 'examples/12.webp', 'examples/13.webp', 'examples/14.webp'], |
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inputs=image_input) |
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face_attr_button = gr.Button("Face Attribute") |
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with gr.Column(): |
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with gr.TabItem("Output"): |
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detect_html_output = gr.HTML() |
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with gr.TabItem("JSON"): |
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detect_json_output = gr.JSON() |
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face_recog_button.click(compare_face, inputs=[image_input1, image_input2, verifyThreshold], outputs=[recog_html_output, recog_json_output]) |
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face_attr_button.click(detect_face, inputs=[image_input], outputs=[detect_html_output, detect_json_output]) |
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gr.HTML('<a href="https://visitorbadge.io/status?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2Fopulentyn%2FFaceRecog"><img src="https://api.visitorbadge.io/api/combined?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2Fopulentyn%2FFaceRecog&countColor=%23263759" /></a>') |
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demo.launch(server_name="0.0.0.0", server_port=7860) |