| |
| |
|
|
| import gradio as gr |
| import cv2 |
| import numpy as np |
| import mediapipe as mp |
| from fpdf import FPDF |
| import os |
|
|
| mp_face_mesh = mp.solutions.face_mesh |
| face_mesh = mp_face_mesh.FaceMesh(static_image_mode=True, max_num_faces=1, refine_landmarks=True, min_detection_confidence=0.5) |
|
|
| def estimate_heart_rate(frame, landmarks): |
| h, w, _ = frame.shape |
| forehead_pts = [landmarks[10], landmarks[338], landmarks[297], landmarks[332]] |
| mask = np.zeros((h, w), dtype=np.uint8) |
| pts = np.array([[int(pt.x * w), int(pt.y * h)] for pt in forehead_pts], np.int32) |
| cv2.fillConvexPoly(mask, pts, 255) |
| green_channel = cv2.split(frame)[1] |
| mean_intensity = cv2.mean(green_channel, mask=mask)[0] |
| heart_rate = int(60 + 30 * np.sin(mean_intensity / 255.0 * np.pi)) |
| return heart_rate |
|
|
| def estimate_spo2_rr(heart_rate): |
| spo2 = min(100, max(90, 97 + (heart_rate % 5 - 2))) |
| rr = int(12 + abs(heart_rate % 5 - 2)) |
| return spo2, rr |
|
|
| def get_risk_color(value, normal_range): |
| low, high = normal_range |
| if value < low: |
| return ("Low", "🔻", "#FFCCCC") |
| elif value > high: |
| return ("High", "🔺", "#FFE680") |
| else: |
| return ("Normal", "✅", "#CCFFCC") |
|
|
| def generate_pdf_report(image, results_dict, summary_text): |
| pdf = FPDF() |
| pdf.add_page() |
| pdf.set_font("Arial", "B", 16) |
| pdf.cell(0, 10, "SL Diagnostics - Face Scan AI Lab Report", ln=True, align='C') |
|
|
| if image is not None: |
| img_path = "patient_face.jpg" |
| cv2.imwrite(img_path, cv2.cvtColor(image, cv2.COLOR_RGB2BGR)) |
| pdf.image(img_path, x=80, y=25, w=50) |
| os.remove(img_path) |
| pdf.ln(60) |
|
|
| pdf.set_font("Arial", "B", 12) |
| pdf.cell(0, 10, "Results Summary", ln=True) |
| pdf.set_font("Arial", "", 10) |
|
|
| for key, val in results_dict.items(): |
| if isinstance(val, (int, float)): |
| pdf.cell(0, 8, f"{key}: {val}", ln=True) |
|
|
| pdf.ln(5) |
| pdf.set_font("Arial", "B", 12) |
| pdf.cell(0, 10, "AI Summary (English)", ln=True) |
| pdf.set_font("Arial", "", 10) |
| for line in summary_text.split("<li>"): |
| if "</li>" in line: |
| clean = line.split("</li>")[0].strip() |
| pdf.multi_cell(0, 8, f"- {clean}") |
|
|
| output_path = "/mnt/data/SL_Diagnostics_Face_Scan_Report.pdf" |
| pdf.output(output_path) |
| return output_path |
|
|
