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e56eb98 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | import cv2
import json
import os
def detect_candidate_photo(image_path, board_id):
"""
Detect candidate photo in marksheet image.
Args:
image_path (str): Path to JPG image
board_id (int): 0=Uttarakhand, 1=CBSE, 2=ICSE
Returns:
dict: {"board": str, "photo_detected": int}
"""
board_names = {0: "Uttarakhand", 1: "CBSE", 2: "ICSE"}
board_name = board_names.get(board_id, "Unknown")
# Check if image exists
if not os.path.exists(image_path):
print(f"Error: Image file '{image_path}' not found.")
return {"board": board_name, "photo_detected": 0}
# Load image
img = cv2.imread(image_path)
if img is None:
print(f"Error: Could not load image '{image_path}'.")
return {"board": board_name, "photo_detected": 0}
# Crop to top 30% where photos usually appear
height = img.shape[0]
img_cropped = img[:int(height * 0.3), :]
# Convert to grayscale
gray = cv2.cvtColor(img_cropped, cv2.COLOR_BGR2GRAY)
# Use Haar cascade face detector (reliable and built-in)
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
return {"board": board_name, "photo_detected": 1 if len(faces) > 0 else 0}
def main():
"""Interactive main function"""
print("=== Marksheet Photo Detection System ===\n")
# Get board selection
print("Select Board:")
print("0 → Uttarakhand Board")
print("1 → CBSE Board")
print("2 → ICSE Board")
try:
board_id = int(input("\nEnter board ID (0/1/2): "))
if board_id not in [0, 1, 2]:
print("Error: Invalid board ID. Please enter 0, 1, or 2.")
return
except ValueError:
print("Error: Please enter a valid number.")
return
# Get image path
image_path = input("Enter image path (e.g., datasetnew/10_1.jpg): ").strip()
# Run detection
print(f"\nProcessing image: {image_path}")
print(f"Board: {['Uttarakhand', 'CBSE', 'ICSE'][board_id]}")
print("-" * 40)
result = detect_candidate_photo(image_path, board_id)
# Display result
print("\nResult:")
print(json.dumps(result, indent=2))
# Save result to file
with open("detection_result.json", "w") as f:
json.dump(result, f, indent=2)
print("\nResult saved to: detection_result.json")
if __name__ == "__main__":
main() |