edge_detection.py

# Normal library imports
import cv2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os

# importing requisite information from create_data.py
# 1. show_images(): to display the images so it may be verified if Canny Edge detection worked fine
# 2. grey_images: which is a 
from create_data import show_images, gray_images, test_images


def detect_edges(image, low_threshold=50, high_threshold=200):
    # Applies the Canny Edge detection algorithm 
    # Refer: https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_canny/py_canny.html
    # Thresholds are hyper-parameters chosen such that:
    # 1. Shorter than requried lines are not chosen
    # 2. Longer lines such that path etc. are also not chosen and are not mistaken for parking spot
    return cv2.Canny(image, low_threshold, high_threshold)


edge_images = list(map(lambda image: detect_edges(image), gray_images)) # creating a map and applying the Canny edge detection

# Uncomment the line to display the image, for testing purposes
show_images(edge_images)

###############################################################################################################################

def filter_region(image, vertices):
    # The entire image is not required for the training. So we simply remove parts of the image not required/containing paths the network can
    # confuse for some parking spot
    
    mask = np.zeros_like(image) # np.zeros_like() creates a matrix of 0s of the same size as the image. Applying the mask on the image
                                # enables it to multiply the pixel value with 0 (rendering it black)
    
    if len(mask.shape)==2: # No extra channel dimension: 
        cv2.fillPoly(mask, vertices, 255) # Simply fill the image with BLACK
    else:
        cv2.fillPoly(mask, vertices, (255,)*mask.shape[2]) # in case, the input image has a channel dimension
                                                            # Handle the color dimension equally well
    
    return cv2.bitwise_and(image, mask) # Bitwise_and simply does a 0 whenever the mask has a zero value. So the non-required area is chopped off

    
def select_region(image):
    
    # The Region of Interest is defined a polygon of user defined number of vertices
    # FUTURE UPDATE: include a method to let users dynamically define this region of interest
    rows, cols = image.shape[:2]
    pt_1  = [cols*0.05, rows*0.90]
    pt_2 = [cols*0.05, rows*0.70]
    pt_3 = [cols*0.30, rows*0.55]
    pt_4 = [cols*0.6, rows*0.15]
    pt_5 = [cols*0.90, rows*0.15] 
    pt_6 = [cols*0.90, rows*0.90]
    # the vertices are an array of polygons (i.e array of arrays) and the data type must be integer
    vertices = np.array([[pt_1, pt_2, pt_3, pt_4, pt_5, pt_6]], dtype=np.int32)
    return filter_region(image, vertices)


# images showing the region of interest only
roi_images = list(map(select_region, edge_images))

# Uncomment this line to test for the region of interest detection
show_images(roi_images)

#######################################################################################################

def hough_lines(image):
    # This function applies the Hough Line Transform to the Canny Edge detected image (along with ROI implemented)
    return cv2.HoughLinesP(image, rho=0.1, theta=np.pi/10, threshold=15, minLineLength=9, maxLineGap=4)

def draw_lines(image, lines, color=[255, 0, 0], thickness=2, make_copy=True):
    # the lines returned by cv2.HoughLinesP has the shape (-1, 1, 4)
    if make_copy:
        image = np.copy(image) # don't want to modify the original
    cleaned = []
    for line in lines:
        for x1,y1,x2,y2 in line:
            if abs(y2-y1) <=1 and abs(x2-x1) >=25 and abs(x2-x1) <= 55:
                cleaned.append((x1,y1,x2,y2))
                cv2.line(image, (x1, y1), (x2, y2), color, thickness)
    print(" No lines detected: ", len(cleaned))
    return image



list_of_lines = list(map(hough_lines, roi_images))
line_images = []
for image, lines in zip(test_images, list_of_lines):
    line_images.append(draw_lines(image, lines))

show_images(line_images)
#################################
#######################################################################################################

def identify_blocks(image, lines, make_copy=True):
    if make_copy:
        new_image = np.copy(image)
    #Step 1: Create a clean list of lines
    cleaned = []
    for line in lines:
        for x1,y1,x2,y2 in line:
            if abs(y2-y1) <=1 and abs(x2-x1) >=25 and abs(x2-x1) <= 55:
                cleaned.append((x1,y1,x2,y2))
    
    #Step 2: Sort cleaned by x1 position
    import operator
    list1 = sorted(cleaned, key=operator.itemgetter(0, 1))
    
    #Step 3: Find clusters of x1 close together - clust_dist apart
    clusters = {}
    dIndex = 0
    clus_dist = 10

    for i in range(len(list1) - 1):
        distance = abs(list1[i+1][0] - list1[i][0])
    #         print(distance)
        if distance <= clus_dist:
            if not dIndex in clusters.keys(): clusters[dIndex] = []
            clusters[dIndex].append(list1[i])
            clusters[dIndex].append(list1[i + 1])

        else:
            dIndex += 1
    
    #Step 4: Identify coordinates of rectangle around this cluster
    rects = {}
    i = 0
    for key in clusters:
        all_list = clusters[key]
        cleaned = list(set(all_list))
        if len(cleaned) > 5:
            cleaned = sorted(cleaned, key=lambda tup: tup[1])
            avg_y1 = cleaned[0][1]
            avg_y2 = cleaned[-1][1]
    #         print(avg_y1, avg_y2)
            avg_x1 = 0
            avg_x2 = 0
            for tup in cleaned:
                avg_x1 += tup[0]
                avg_x2 += tup[2]
            avg_x1 = avg_x1/len(cleaned)
            avg_x2 = avg_x2/len(cleaned)
            rects[i] = (avg_x1, avg_y1, avg_x2, avg_y2)
            i += 1
    
    print("Num Parking Lanes: ", len(rects))
    #Step 5: Draw the rectangles on the image
    buff = 7
    for key in rects:
        tup_topLeft = (int(rects[key][0] - buff), int(rects[key][1]))
        tup_botRight = (int(rects[key][2] + buff), int(rects[key][3]))
#         print(tup_topLeft, tup_botRight)
        cv2.rectangle(new_image, tup_topLeft,tup_botRight,(0,255,0),3)
    return new_image, rects

# images showing the region of interest only
rect_images = []
rect_coords = []
for image, lines in zip(test_images, list_of_lines):
    new_image, rects = identify_blocks(image, lines)
    rect_images.append(new_image)
    rect_coords.append(rects)
    
show_images(rect_images)

#######################################################################################################

def draw_parking(image, rects, make_copy = True, color=[255, 0, 0], thickness=2, save = True):
    if make_copy:
        new_image = np.copy(image)
    gap = 15.5
    spot_dict = {} # maps each parking ID to its coords
    tot_spots = 0
    adj_y1 = {0: 20, 1:-10, 2:0, 3:-11, 4:28, 5:5, 6:-15, 7:-15, 8:-10, 9:-30, 10:9, 11:-32}
    adj_y2 = {0: 30, 1: 50, 2:15, 3:10, 4:-15, 5:15, 6:15, 7:-20, 8:15, 9:15, 10:0, 11:30}
    
    adj_x1 = {0: -8, 1:-15, 2:-15, 3:-15, 4:-15, 5:-15, 6:-15, 7:-15, 8:-10, 9:-10, 10:-10, 11:0}
    adj_x2 = {0: 0, 1: 15, 2:15, 3:15, 4:15, 5:15, 6:15, 7:15, 8:10, 9:10, 10:10, 11:0}
    for key in rects:
        # Horizontal lines
        tup = rects[key]
        x1 = int(tup[0]+ adj_x1[key])
        x2 = int(tup[2]+ adj_x2[key])
        y1 = int(tup[1] + adj_y1[key])
        y2 = int(tup[3] + adj_y2[key])
        cv2.rectangle(new_image, (x1, y1),(x2,y2),(0,255,0),2)
        num_splits = int(abs(y2-y1)//gap)
        for i in range(0, num_splits+1):
            y = int(y1 + i*gap)
            cv2.line(new_image, (x1, y), (x2, y), color, thickness)
        if key > 0 and key < len(rects) -1 :        
            #draw vertical lines
            x = int((x1 + x2)/2)
            cv2.line(new_image, (x, y1), (x, y2), color, thickness)
        # Add up spots in this lane
        if key == 0 or key == (len(rects) -1):
            tot_spots += num_splits +1
        else:
            tot_spots += 2*(num_splits +1)
            
        # Dictionary of spot positions
        if key == 0 or key == (len(rects) -1):
            for i in range(0, num_splits+1):
                cur_len = len(spot_dict)
                y = int(y1 + i*gap)
                spot_dict[(x1, y, x2, y+gap)] = cur_len +1        
        else:
            for i in range(0, num_splits+1):
                cur_len = len(spot_dict)
                y = int(y1 + i*gap)
                x = int((x1 + x2)/2)
                spot_dict[(x1, y, x, y+gap)] = cur_len +1
                spot_dict[(x, y, x2, y+gap)] = cur_len +2   
    
    print("total parking spaces: ", tot_spots, cur_len)
    if save:
        filename = 'with_parking.jpg'
        cv2.imwrite(filename, new_image)
    return new_image, spot_dict

delineated = []
spot_pos = []
for image, rects in zip(test_images, rect_coords):
    new_image, spot_dict = draw_parking(image, rects)
    delineated.append(new_image)
    spot_pos.append(spot_dict)
    
show_images(delineated)

#######################################################################################################

final_spot_dict = spot_pos[1]
def assign_spots_map(image, spot_dict=final_spot_dict, make_copy = True, color=[255, 0, 0], thickness=2):
    if make_copy:
        new_image = np.copy(image)
    for spot in spot_dict.keys():
        (x1, y1, x2, y2) = spot
        cv2.rectangle(new_image, (int(x1),int(y1)), (int(x2),int(y2)), color, thickness)
    return new_image

marked_spot_images = list(map(assign_spots_map, test_images))
show_images(marked_spot_images)
#######################################################################################################

import pickle

with open('spot_dict.pickle', 'wb') as handle:
    pickle.dump(final_spot_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)

#######################################################################################################

def save_images_for_cnn(image, folder_name, spot_dict = final_spot_dict):
    for spot in spot_dict.keys():
        (x1, y1, x2, y2) = spot
        (x1, y1, x2, y2) = (int(x1), int(y1), int(x2), int(y2))
        #crop this image
#         print(image.shape)
        spot_img = image[y1:y2, x1:x2]
        spot_img = cv2.resize(spot_img, (0,0), fx=2.0, fy=2.0) 
        spot_id = spot_dict[spot]
        
        filename =  'spot' + str(spot_id) +'.jpg'
        print(spot_img.shape, filename, (x1,x2,y1,y2))
        
        cv2.imwrite(os.path.join(folder_name, filename), spot_img)
        
save_images_for_cnn(test_images[0], 'cnn\\train_cnn\\')
save_images_for_cnn(test_images[1], 'cnn\\test_cnn\\')