[Python] Problems with landmarks using face angle age estimation

I'm having problems with plotting C1, C2, C3, R1, R2.

It's not in the right place using python opencv and I'm not sure if it's Step 6 & 7?



Based on: Age Group Estimation Using Face Angle. IOSR Journal of Computer Engineering (IOSRJCE) ISSN: 2278-0661, ISBN: 2278-8727 Volume 7, Issue 5 (Nov-Dec. 2012), PP 35-39 www.iosrjournals.org

1. Detect the rectangular face area from input face image using Matlab in-built object function. If number of detected face is more than one, an error message will be displayed.
2. Crop the detected rectangular face area and detect the significance eye-region using Matlab in-built object function.
3. The cropped rectangular face image is histogram equalized and then converted into binary image.
4. The binary image of face is divided horizontally into two parts. Upper part that contains two eyes is denoted by UPART and lower part that contains mouth is denoted by LPART.
5. Divide UPART vertically into two parts. One part that contains right eye is denoted by REYE and other part that contains left eye is denoted by LEYE.
6. Find the row number R1 with minimum row sum of gray level in UPART. Find the column numbers C1 and C2 with minimum column sum of gray level in REYE and LEYE. So, (R1, C1) coordinate represents middle point of right eyeball and (R1, C2) coordinate represents middle point of left eyeball.
7. Find the row number R2 with minimum row sum of gray level in LPART. So, R2 row represents the mouth row.
8. Calculate the midpoint C3 of two eye ball. So, C3= (C1+ C2) /2 and the coordinate (R2, C3) is middle point of mouth.
9. Draw a triangle by three coordinate points left eyeball (R1, C1), right eyeball (R1, C2) & mouth point (R2, C3).
10. Calculate slope (m1) of triangle sides from mouth point (R2, C3) to right eyeball (R1, C1) and slope (m2) of triangle sides from mouth point (R2, C3) to left eyeball (R1, C2).
11. Find the face angle (A) using formula: A = tan-1( (m1- m2) / (1 + m1 * m2) )
12. Determine age group based on the face angle (A).

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import cv2
import numpy as np
import matplotlib.pyplot as plt

def detect_face_angle(image_path):
try:
# Step 1: Load image and detect face
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')

# Read the input image
image = cv2.imread(image_path)
if image is None:
raise ValueError("Error: Unable to read the input image. Check the file path.")

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(100, 100))

if len(faces) > 1:
raise ValueError("Error: More than one face detected.")
elif len(faces) == 0:
raise ValueError("Error: No face detected.")

# Extract the face region
x, y, w, h = faces[0]
print("x: ", x, " y: ", y)
print("w: ", w, " h: ", h)
face_image = gray[y:y+h, x:x+w]

#plt.subplot(2, 3, 1)
#plt.imshow(face_image, cmap='gray')
#plt.title("Face")

# Step 2: Detect eye region
eyes = eye_cascade.detectMultiScale(face_image)
if len(eyes) < 2:
raise ValueError("Error: Less than two eyes detected in the face region.")

# Step 3: Histogram equalization and binary conversion
face_hist_eq = cv2.equalizeHist(face_image)
#plt.subplot(2, 3, 4)
#plt.imshow(face_hist_eq, cmap='gray')
#plt.title("Equalization (face_hist_eq)")

_, binary_image = cv2.threshold(face_hist_eq, 128, 255, cv2.THRESH_BINARY)

# Step 4: Divide face into UPART and LPART
h, w = binary_image.shape
UPART = binary_image[:h//2, :]
LPART = binary_image[h//2:, :]

#plt.subplot(2, 3, 2)
#plt.imshow(UPART, cmap='gray')
#plt.title("Upper Face (UPART)")

#plt.subplot(2, 3, 3)
#plt.imshow(LPART, cmap='gray')
#plt.title("Lower Face (LPART)")

# Step 5: Divide UPART into REYE and LEYE
REYE = UPART[:, :w//2]
LEYE = UPART[:, w//2:]

#plt.subplot(2, 3, 5)
#plt.imshow(REYE, cmap='gray')
#plt.title("Right Eye (REYE)")

#plt.subplot(2, 3, 6)
#plt.imshow(LEYE, cmap='gray')
#plt.title("Left Eye (LEYE)")

plt.show()

# Step 6: Find R1, C1, and C2
R1 = np.argmin(np.sum(UPART, axis=1)) # Row with minimum row sum in UPART
#print("Row sums in UPART:", np.sum(UPART, axis=1))
print("Selected R1:", R1)
#if np.sum(UPART[R1, :]) == 0:
# raise ValueError("Error: Unable to determine the upper row for eyes.")

#column_sums = np.sum(REYE, axis=0)

# Check if there are any non-numeric values
#assert not (np.isnan(column_sums)).any(), "There are NaNs in the data"

#C1 = np.argmin(np.abs(column_sums)) # Find index of column with minimum sum


C1 = np.argmin(np.sum(REYE, axis=0)) # Column with minimum column sum in REYE
#print("Column sums in REYE:", np.sum(REYE, axis=0))
print("Selected C1:", C1)
#if np.sum(REYE[:, C1]) == 0:
# raise ValueError("Error: Unable to determine the column for the right eye.")

C2 = np.argmin(np.sum(LEYE, axis=0)) + w//2 # Adjust C2 relative to full image
#print("Column sums in LEYE:", np.sum(LEYE, axis=0))
print("Selected C2:", C2)
#if np.sum(LEYE[:, C2 - w//2]) == 0:
# raise ValueError("Error: Unable to determine the column for the left eye.")

# Step 7: Find R2
R2 = np.argmin(np.sum(LPART, axis=1)) + h//2 # Adjust R2 relative to full image4
print("Selected R2:", R2)
#if np.sum(LPART[R2 - h//2, :]) == 0:
# raise ValueError("Error: Unable to determine the row for the mouth.")

# Step 8: Calculate midpoint C3
C3 = (C1 + C2) // 2

# Step 9: Draw triangle

################## image
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

#plt.scatter([x], [y], color='blue', s=50)
#plt.scatter([w], [h], color='yellow', s=50)

plt.scatter([x + C1, x + C2, x + C3], [y + R1, y + R1, y + R2], color='red', s=50)
plt.plot([x + C1, x + C2, x + C3, x + C1], [y + R1, y + R1, y + R2, y + R1], 'g-')
plt.show()

# Step 10: Calculate slopes
m1 = (R2 - R1) / (C3 - C1) if C3 != C1 else np.inf
m2 = (R2 - R1) / (C3 - C2) if C3 != C2 else np.inf

# Step 11: Calculate face angle
A = np.degrees(np.arctan(abs((m1 - m2) / (1 + m1 * m2))))
print("A: ", A)

# Step 12: Determine age group based on the face angle (A)
# Display result
if A < 44: print("< 18")
elif 44 <= A <= 48: print("18 to 25")
elif 49 <= A <= 54: print("26 to 35")
elif 55 <= A <= 60: print("36 to 45")
elif A > 60: print("> 45")
else: print("None") # Handle invalid input if needed)

except Exception as e:
print(f"An error occurred: {e}")

# Usage
detect_face_angle("test.jpg")

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