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image_processing

This commit is contained in:
livindra 2026-05-31 17:08:58 +07:00
parent 0e9f1202cd
commit 833d8801c9
2 changed files with 56 additions and 25 deletions
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4
app.py
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@ -353,7 +353,7 @@ def api_validate_image():
file.save(temp_filepath)
# Validasi gambar
is_cattle, confidence, reason = validate_cattle_image(temp_filepath, confidence_threshold=0.75)
is_cattle, confidence, reason = validate_cattle_image(temp_filepath, confidence_threshold=0.65)
if is_cattle:
result = {
@ -410,7 +410,7 @@ def predict():
# Validasi ulang di backend supaya request langsung ke /predict tidak bisa bypass.
is_cattle, validation_confidence, validation_reason = validate_cattle_image(
filepath,
confidence_threshold=0.75,
confidence_threshold=0.65,
)
if not is_cattle:
if os.path.exists(filepath):

77
utils/preprocessing.py
View File

@ -3,7 +3,32 @@ import numpy as np
from PIL import Image
def validate_cattle_image(image_path, confidence_threshold=0.75):
def _detect_human_face(gray_image):
"""Return (detected, face_area_ratio) for human face detection."""
try:
cascade_path = cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
face_cascade = cv2.CascadeClassifier(cascade_path)
if face_cascade.empty():
return False, 0.0
faces = face_cascade.detectMultiScale(
gray_image,
scaleFactor=1.1,
minNeighbors=5,
minSize=(50, 50),
)
if len(faces) == 0:
return False, 0.0
image_area = float(gray_image.shape[0] * gray_image.shape[1])
largest_face_area = max((w * h for (_, _, w, h) in faces), default=0)
face_area_ratio = (largest_face_area / image_area) if image_area > 0 else 0.0
return face_area_ratio >= 0.03, face_area_ratio
except Exception:
return False, 0.0
def validate_cattle_image(image_path, confidence_threshold=0.65):
"""
Validasi apakah gambar adalah sapi atau bukan.
Bisa deteksi wajah sapi (dengan mata) atau bagian tubuh sapi lainnya.
@ -26,6 +51,12 @@ def validate_cattle_image(image_path, confidence_threshold=0.75):
h, w = img.shape[:2]
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Tolak foto wajah manusia lebih awal karena sering lolos pada heuristik warna/tekstur.
human_face_detected, face_area_ratio = _detect_human_face(gray)
if human_face_detected:
print(f"[VALIDATE] Wajah manusia terdeteksi | area={face_area_ratio*100:.1f}%")
return False, 0.0, "Gambar tidak menunjukkan sapi"
# ===== LAYER 1: DETEKSI MATA (OPTIONAL - hanya bonus jika ada) =====
circles = cv2.HoughCircles(
gray, cv2.HOUGH_GRADIENT,
@ -76,66 +107,66 @@ def validate_cattle_image(image_path, confidence_threshold=0.75):
cattle_color = brown_mask | red_mask | black_mask | white_mask
color_ratio = np.sum(cattle_color) / (h * w)
# Minimum warna sapi harus 30% (ketat untuk menolak bukan sapi)
if color_ratio < 0.30:
# Minimum warna sapi dibuat lebih longgar supaya foto sapi yang crop/lighting-nya beragam tetap lolos
if color_ratio < 0.18:
return False, 0.0, "Gambar tidak menunjukkan sapi"
# Score color: ketat untuk matching sapi
if color_ratio >= 0.50:
if color_ratio >= 0.45:
color_score = 1.0
elif color_ratio >= 0.40:
color_score = 0.95
elif color_ratio >= 0.30:
color_score = 0.80
color_score = 0.90
elif color_ratio >= 0.18:
color_score = 0.75
else:
color_score = 0.60
color_score = 0.55
print(f"[VALIDATE] Warna sapi: {color_ratio*100:.0f}%")
# ===== LAYER 3: VALIDASI TEKSTUR KULIT =====
laplacian = cv2.Laplacian(gray, cv2.CV_64F)
texture_variance = np.var(laplacian)
# Texture sapi harus dalam range spesifik 80-2200 (ketat)
if texture_variance < 80 or texture_variance > 2200:
# Range texture dibuat lebih fleksibel karena foto asli bisa sangat bervariasi
if texture_variance < 45 or texture_variance > 3500:
return False, 0.0, "Gambar tidak menunjukkan tekstur sapi"
# Score texture: ketat untuk matching sapi texture
if 120 <= texture_variance <= 1800:
if 100 <= texture_variance <= 2200:
texture_score = 1.0
elif 90 <= texture_variance <= 2100:
texture_score = 0.90
elif 70 <= texture_variance <= 3000:
texture_score = 0.85
else:
texture_score = 0.75
texture_score = 0.70
print(f"[VALIDATE] Tekstur variance: {texture_variance:.0f}")
# ===== LAYER 4: EDGE DETECTION (Struktur) =====
edges = cv2.Canny(gray, 50, 150)
edge_density = np.sum(edges > 0) / (h * w)
# Edge density: 2-25% ketat untuk sapi
if edge_density < 0.02 or edge_density > 0.25:
# Edge density dibuat lebih longgar supaya gambar close-up atau background polos tidak langsung gagal
if edge_density < 0.01 or edge_density > 0.35:
return False, 0.0, "Gambar tidak memiliki struktur sapi"
# Score edge: ketat untuk matching
if 0.05 <= edge_density <= 0.15:
if 0.04 <= edge_density <= 0.18:
edge_score = 1.0
elif 0.03 <= edge_density <= 0.22:
edge_score = 0.90
elif 0.02 <= edge_density <= 0.28:
edge_score = 0.85
else:
edge_score = 0.70
edge_score = 0.65
print(f"[VALIDATE] Edge density: {edge_density*100:.1f}%")
# ===== HARD REQUIREMENTS =====
# REQUIREMENT 1: Warna harus minimal decent (tidak boleh terlalu rendah)
if color_score < 0.75:
if color_score < 0.55:
return False, 0.0, "Warna gambar tidak sesuai sapi"
# REQUIREMENT 2: Texture harus dalam range sapi
if texture_score < 0.70:
if texture_score < 0.60:
return False, 0.0, "Tekstur gambar tidak sesuai sapi"
# REQUIREMENT 3: Edge harus terdeteksi
if edge_score < 0.65:
if edge_score < 0.55:
return False, 0.0, "Struktur gambar tidak terlihat jelas"
# ===== FINAL CALCULATION =====