first_upload app

app.py

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+import gradio as gr
+from flask import Flask, Response
+import cv2
+import numpy as np
+import mediapipe as mp
+import matplotlib.pyplot as plt
+import math
+from IPython import display
+# --------------------
+MAX_ANGLE = 10
+# --------------------
+# --------------------
+# --------------------
+# --------------------
+def distanceCalculate(p):
+    p1 = p[0]
+    p2 = p[1]
+    dis = ((p2[0] - p1[0]) ** 2 + (p2[1] - p1[1]) ** 2) ** 0.5
+    return dis
+# --------------------
+def angleLinePoints(p):
+    p1 = p[0]
+    p2 = p[1]
+    # 
+    p1_x = p1[0]
+    p1_y = p1[1]
+    p2_x = p2[0]
+    p2_y = p2[1]
+    # 
+    d_x = p2_x - p1_x
+    d_y = p2_y - p1_y
+    # 
+    angle_radians = np.arctan(d_y/d_x)
+    angle_degrees = math.degrees(angle_radians)
+    return angle_degrees
+# --------------------
+def process_image(image):
+    print(type(image))
+    if isinstance(image, str):
+        image = cv2.imread(image)
+    # 
+    print(image.size)
+    print(image.ndim)
+    (B, G, R) = cv2.split(image)
+    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)
+    result = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+    height, width, _ = image.shape
+    mesh_points= np.array([np.multiply([p.x, p.y], [width, height]).astype(int) for p in result.multi_face_landmarks[0].landmark])
+    # Landmark mapping
+    landmarks = {
+        'topToBottom': [10, 152],
+        'leftToRight': [234, 454],
+        'rightEyeIris': [473, 474, 475, 476, 477],
+        'leftEyeIris': [468, 469, 470, 471, 472],
+        'outerRightEyebrowUpper': 70,
+        'outerLeftEyebrowUpper': 300,
+        'silhouette': [10,  338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,
+        397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,
+        172, 58,  132, 93,  234, 127, 162, 21,  54,  103, 67,  109]}
+    # Iris location
+    (l_cx, l_cy), l_radius = cv2.minEnclosingCircle(mesh_points[landmarks['rightEyeIris']])
+    (r_cx, r_cy), r_radius = cv2.minEnclosingCircle(mesh_points[landmarks['leftEyeIris']])
+    center_left_iris = np.array([l_cx, l_cy], dtype=np.int32)
+    center_right_iris = np.array([r_cx, r_cy], dtype=np.int32)
+    # Face dimension
+    width_face_px = distanceCalculate(mesh_points[landmarks['leftToRight']])
+    height_face_px = distanceCalculate(mesh_points[landmarks['topToBottom']])
+    # IPD calculation
+    ipd = distanceCalculate([center_left_iris, center_right_iris])
+    # Photo Quality Check
+    vertical_angle = angleLinePoints(mesh_points[landmarks['topToBottom']])
+    horizonal_angle = angleLinePoints(mesh_points[landmarks['leftToRight']])
+    # Rectangle for Card Area
+    x_start = mesh_points[landmarks['outerRightEyebrowUpper']][0]
+    y_start = mesh_points[landmarks['outerRightEyebrowUpper']][1] - 0.6*height_face_px
+    x_end, y_end =  mesh_points[landmarks['outerLeftEyebrowUpper']]
+    start_point = (int(x_start), int(y_start))
+    end_point = (int(x_end), int(y_end))
+    # 
+    overlay = image.copy()
+    alpha = 0.1
+    beta = (1.0 - alpha)
+    # Plot all the information
+    # plt.imshow(image)
+    # plt.imshow(image_landmarks)
+    for facial_landmarks in result.multi_face_landmarks:
+        for i in range(0, 468):
+            pt1 = facial_landmarks.landmark[i]
+            x = int(pt1.x * width)
+            y = int(pt1.y * height)
+            cv2.circle(image, (x, y), 1, (0,255,0), -1)
+    plt.imshow(cv2.polylines(image, [mesh_points[landmarks['topToBottom']]], 1, (0,255,0), 1))
+    plt.imshow(cv2.polylines(image, [mesh_points[landmarks['leftToRight']]], 1, (0,255,0), 1))
+    plt.imshow(cv2.polylines(image, [mesh_points[landmarks['silhouette']]], 1, (0,255,0), 1))
+    plt.imshow(cv2.line(image, center_left_iris, center_right_iris, (0, 255, 0), 2))
+    plt.imshow(cv2.line(image, mesh_points[landmarks['outerLeftEyebrowUpper']], mesh_points[landmarks['outerRightEyebrowUpper']], (0, 255, 0), 1))
+    plt.imshow(cv2.rectangle(image, start_point, end_point, (255,0,0), 1))
+    plt.imshow(cv2.ellipse(image, center=(int(width/2), int(height/2)), axes=(int(min(width,height)/4),int(min(width,height)/3)), angle=0, startAngle=0, endAngle=360, color=(255,255,255), thickness=4))    
+    plt.imshow(cv2.putText(image, f'IPD: {str(ipd)} [px]', (50,50), cv2.FONT_HERSHEY_PLAIN,1, (0,0,0), 1))
+    plt.imshow(cv2.putText(image, f'Face Height: {str(height_face_px)} [px]', (50,100), cv2.FONT_HERSHEY_PLAIN,1, (0,0,0),1))
+    plt.imshow(cv2.putText(image, f'Face Width: {str(width_face_px)} [px]', (50,150), cv2.FONT_HERSHEY_PLAIN,1, (0,0,0), 1))
+    if abs(horizonal_angle) < MAX_ANGLE:
+        plt.imshow(cv2.putText(image, f'Horizonal Angle: {str(horizonal_angle)} [degrees]', (50,200), cv2.FONT_HERSHEY_PLAIN,1, (0,255,0), 1))
+        cv2.rectangle(overlay, (0,0), (width, height), (0, 255, 0), -1)  
+        image = cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0)
+    elif horizonal_angle < -MAX_ANGLE:
+        plt.imshow(cv2.arrowedLine(image,[int(x_start) - (int(min(width,height)/4)),int(y_start+ height_face_px/2)], [int(x_start), int(y_start+ height_face_px/2)], (0, 255, 0), 4))
+        plt.imshow(cv2.putText(image, f'Horizonal Angle: {str(horizonal_angle)} [degrees]', (50,200), cv2.FONT_HERSHEY_PLAIN,1, (0,0,255), 1))
+        cv2.rectangle(overlay, (0,0), (width, height), (0, 0, 255), -1)  
+        image = cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0)
+        plt.imshow(image)
+    elif horizonal_angle > MAX_ANGLE:
+        plt.imshow(cv2.arrowedLine(image, [int(x_start), int(y_start+ height_face_px/2)],[int(x_start) - (int(min(width,height)/4)),int(y_start+ height_face_px/2)], (0, 255, 0), 4))
+        plt.imshow(cv2.putText(image, f'Horizonal Angle: {str(horizonal_angle)} [degrees]', (50,200), cv2.FONT_HERSHEY_PLAIN,1, (0,0,255), 1))
+        cv2.rectangle(overlay, (0,0), (width, height), (0, 0, 255), -1)  
+        image = cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0)
+        plt.imshow(image)
+    if abs(vertical_angle) > (90 - MAX_ANGLE): #[-80, 80] -> okay
+        plt.imshow(cv2.putText(image, f'Vertical Angle: {str(vertical_angle)} [degrees]', (50,250), cv2.FONT_HERSHEY_PLAIN,1, (0,255,0), 1))
+    else:         
+        plt.imshow(cv2.putText(image, f'Vertical Angle: {str(vertical_angle)} [degrees]', (50,250), cv2.FONT_HERSHEY_PLAIN,1, (0,0,255), 1))
+    # 
+    return image
+# --------------------
+# --------------------
+# --------------------
+# --------------------
+demo = gr.Interface(
+    flip, 
+    gr.Image(source="webcam", streaming=True), 
+    "image",
+    live=True
+)
+demo.launch()

requirements.txt

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+Flask==2.0.1
+gradio==3.16.2
+ipython==8.8.0
+matplotlib==3.6.2
+mediapipe==0.9.0.1
+numpy==1.21.5
+opencv_contrib_python==4.7.0.68