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Hazel 2025-04-23 17:14:54 +02:00
parent 0e73aff25d
commit 9ae09b1ae5
1 changed files with 0 additions and 80 deletions
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secure_pixelation/detect_humans.py
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@ -255,86 +255,6 @@ def detect_human_parts(human: dict, face_padding: int = 20):
print(clean_points)
"""
for idx in face_indices:
x, y, conf = keypoints[idx]
name = keypoint_names[idx]
if conf > 0.3:
face_points.append((x, y))
point = (int(x), int(y))
name = keypoint_names[idx]
cv2.circle(image, point, 4, (0, 255, 0), -1)
cv2.putText(image, name, (point[0] + 5, point[1] + 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
"""
"""
nose, left_eye, right_eye, left_ear, right_ear = face_points
print(face_points)
# Calculate pairwise distances
nose_to_left_eye = euclidean_distance(nose, left_eye)
nose_to_right_eye = euclidean_distance(nose, right_eye)
eyes_distance = euclidean_distance(left_eye, right_eye)
left_ear_to_nose = euclidean_distance(left_ear, nose)
right_ear_to_nose = euclidean_distance(right_ear, nose)
# Relative distances
eye_to_eye_ratio = eyes_distance / (left_ear_to_nose + right_ear_to_nose) # Eyes vs. nose-to-ears
nose_to_eye_ratio = (nose_to_left_eye + nose_to_right_eye) / (left_ear_to_nose + right_ear_to_nose) # Nose-to-eye vs. ear-to-nose
ear_to_nose_ratio = (left_ear_to_nose + right_ear_to_nose) / 2 # Ear-to-nose proportionality
# Validate using relative distances
if not (EYE_RATIO_THRESHOLD < eye_to_eye_ratio < 0.5): # Arbitrary ratio threshold
print("⚠️ Rejected due to unrealistic eye-to-eye ratio:", eye_to_eye_ratio)
has_valid_face = False
if not (NOSE_EYE_RATIO_THRESHOLD < nose_to_eye_ratio < 0.4): # Arbitrary ratio threshold
print("⚠️ Rejected due to unrealistic nose-to-eye ratio:", nose_to_eye_ratio)
has_valid_face = False
if not (0.5 < ear_to_nose_ratio < EAR_NOSE_RATIO_THRESHOLD):
print("⚠️ Rejected due to unrealistic ear-to-nose ratio:", ear_to_nose_ratio)
has_valid_face = False
# If all checks pass, calculate the bounding box
xs, ys, _ = zip(*face_points)
x_min, x_max = int(min(xs)), int(max(xs))
y_min, y_max = int(min(ys)), int(max(ys))
x_min = max(x_min - face_padding, 0)
y_min = max(y_min - face_padding, 0)
x_max = min(x_max + face_padding, image.shape[1])
y_max = min(y_max + face_padding, image.shape[0])
# Compute box size
box_w = x_max - x_min
box_h = y_max - y_min
if has_valid_face:
cv2.rectangle(image, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)
else:
cv2.rectangle(image, (x_min, y_min), (x_max, y_max), (255, 0, 0), 2)
for i, (x, y, conf) in enumerate(keypoints):
point = (int(x), int(y))
name = keypoint_names[i]
# cv2.circle(image, point, 4, (0, 255, 0), -1)
# cv2.putText(image, name, (point[0] + 5, point[1] - 5),
# cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
# cv2.circle(image, head, 5, (255, 0, 0), -1) # Head in blue
# cv2.circle(image, foot, 5, (0, 0, 255), -1) # Foot in red
"""
cv2.imwrite(detected, image)