import cv2
import numpy as np
from os import walk
import csv

def detect_corners(gray_image, max_corners=100, quality_level=0.14, min_distance=1):
    corners = cv2.goodFeaturesToTrack(cv2.blur(gray_image, (5,5)), maxCorners=max_corners, qualityLevel=quality_level, minDistance=min_distance)
    if corners is not None:
        corners = np.int0(corners)  
        return len(corners), corners
    else:
        return 0, []

def detect_edges(gray_image):
    edges = cv2.Canny(cv2.blur(gray_image, (5,5)), threshold1=100, threshold2=200)
    edge_count = np.count_nonzero(edges)
    return edge_count, edges



def detect_contours(edge_image):
    contours, _ = cv2.findContours(edge_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    contour_count = len(contours)
    return contour_count, contours


def feature_vector(image_path):
    resultVector = {}
    img = cv2.imread(image_path)
    image_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    corners_count, _ = detect_corners(image_gray)
    edges_count, edges = detect_edges(image_gray)
    contours_count, _ = detect_contours(edges)   

    resultVector["corners"] = corners_count % 100
    resultVector["edges"] = edges_count / 1000
    resultVector["contours"] = contours_count * 0.1

    return resultVector


filenames = next(walk("images"), (None, None, []))[2]  # [] if no file

with open('dict.csv', 'w') as csv_file:  
    writer = csv.DictWriter(csv_file, fieldnames=['corners', 'edges', 'contours'])
    writer.writeheader()
    for filename in filenames: 
        result = feature_vector(f'images/{filename}')
        writer.writerow(result)