data_generator

+import os
+import PIL
+from PIL import Image, ImageEnhance, ImageFilter
+import random
+import cv2, argparse
+import numpy as np
+from xml.etree.ElementTree import *
+from pascal_voc_writer import Writer
+
+
+def image_overlay(src, color="#FFFFFF", alpha=0.5):
+    overlay = Image.new('RGBA', src.size, color)
+    bw_src = ImageEnhance.Color(src).enhance(0.0)
+    return Image.blend(bw_src, overlay, alpha)
+
+def insert_black_mask(img):
+    black_mask = Image.new('RGBA',img.size, (0, 0, 0))
+    val = random.randint(100, 150)
+    black_mask.putalpha(val)
+    
+    img.paste(black_mask, (0, 0,img.size[0], img.size[1]), mask = black_mask)
+    return img
+
+
+def random_bright_contrast(img):
+    img = ImageEnhance.Contrast(img)
+    num_contrast = random.uniform(0.7, 1.5)
+    img = img.enhance(num_contrast)
+    num_brightness = random.uniform(0.8, 1.0)
+    img = ImageEnhance.Brightness(img)
+    img = img.enhance(num_brightness)
+    
+    if random.random() < 0.1:
+        img = img.filter(ImageFilter.BLUR)
+
+    return img
+
+
+  
+def image_filtering(img, ang_range=1.2, shear_range=1.5, trans_range=1):
+
+    img = np.array(img)
+    
+    # Rotation
+    ang_rot = np.random.uniform(ang_range) - ang_range / 2
+    rows, cols, ch = img.shape
+    Rot_M = cv2.getRotationMatrix2D((cols / 2, rows / 2), ang_rot, 0.9)
+
+    # Translation
+    tr_x = trans_range * np.random.uniform() - trans_range / 2
+    tr_y = trans_range * np.random.uniform() - trans_range / 2
+    Trans_M = np.float32([[1, 0, tr_x], [0, 1, tr_y]])
+
+    # Shear
+    pts1 = np.float32([[5, 5], [20, 5], [5, 20]])
+
+    pt1 = 5 + shear_range * np.random.uniform() - shear_range / 2
+    pt2 = 20 + shear_range * np.random.uniform() - shear_range / 2
+    pts2 = np.float32([[pt1, 5], [pt2, pt1], [5, pt2]])
+    shear_M = cv2.getAffineTransform(pts1, pts2)
+
+    img = cv2.warpAffine(img, Rot_M, (cols, rows))
+    img = cv2.warpAffine(img, Trans_M, (cols, rows))
+    img = cv2.warpAffine(img, shear_M, (cols, rows))
+    
+    img = Image.fromarray(img)
+
+    return img    
+
+def image_augmentation(img, car_img, car_boxes):
+    bbox = None
+    car_box_1 = car_boxes[0]
+    car_box_2 = car_boxes[1]
+    
+    
+    width_of_plate_holder = car_box_1[2] - car_box_1[0]
+    wpercent = (width_of_plate_holder / float(img.size[0]))
+    hsize = int((float(img.size[1]) * float(wpercent)))
+    img = img.resize((width_of_plate_holder, hsize), PIL.Image.ANTIALIAS)
+        
+    img = np.array(img)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2BGRA)
+    w, h, _ = img.shape
+    pts1 = np.float32([[0, 0], [0, w], [h, 0], [h, w]])
+    
+    
+    h_change = abs(car_box_1[1] - car_box_2[1])
+    if (abs(car_box_1[0] - car_box_2[0])) < (width_of_plate_holder / 2):
+        pts2 = np.float32([[0, h_change], [0, w], [h, 0], [h, w - h_change]])
+    elif (abs(car_box_1[0] - car_box_2[0])) >(width_of_plate_holder / 2):
+        pts2 = np.float32([[0, 0], [0, w - h_change], [h, h_change], [h, w ]])
+    else:
+        pts2 = np.float32([[0, 0], [0, w], [h, 0], [h, w]])
+    
+    M = cv2.getPerspectiveTransform(pts1, pts2)
+    img = cv2.warpPerspective(img, M, (h, w), flags = cv2.INTER_CUBIC, borderMode=cv2.BORDER_CONSTANT, borderValue = [0, 0, 0, 0])
+    img = Image.fromarray(img)
+    
+    width, height = img.size
+    width_prop = car_boxes[0][0]
+    height_prop = car_boxes[0][1]
+    car_img.paste(img, (width_prop, height_prop, width + width_prop, height + height_prop), mask = img)
+    
+    bbox = [width_prop, height_prop, width + width_prop, height + height_prop] 
+    
+    return car_img, bbox
+
+
+class ImageGenerator:
+    def __init__(self, components_path, detection_images_path, 
+                 detection_annotations_path, recognition_data_path, cars_path, background_path):
+        
+        # paths inintialization
+        self.components_path = components_path
+        self.detection_images_path = detection_images_path
+        self.detection_annotations_path = detection_annotations_path
+        self.recognition_data_path = recognition_data_path
+        self.cars_path = cars_path
+        self.background_path = background_path
+        
+        
+        
+        # plates initializtion
+        plate_files_path = 'plates_type_'
+        self.plate_types = ["blank", "1", "2", "3", "4", "5", "6"]
+        # plate type 1
+        self.Plate_1_images, self.Plate_1_names = self.init_component(
+            components_path + plate_files_path + self.plate_types[1] + '/')
+        # plate type 2
+        self.Plate_2_images, self.Plate_2_names = self.init_component(
+            components_path + plate_files_path + self.plate_types[2] + '/')
+        # plate type 3
+        self.Plate_3_images, self.Plate_3_names = self.init_component(
+            components_path + plate_files_path + self.plate_types[3] + '/')
+        # plate type 4
+        self.Plate_4_images, self.Plate_4_names = self.init_component(
+            components_path + plate_files_path + self.plate_types[4] + '/')
+        # plate type 5
+        self.Plate_5_images, self.Plate_5_names = self.init_component(
+            components_path + plate_files_path + self.plate_types[5] + '/')
+        # plate type 6
+        self.Plate_6_images, self.Plate_6_names = self.init_component(
+            components_path + plate_files_path + self.plate_types[6] + '/')
+
+        # components init paths
+        chars_path = 'char_'
+        nums_path = 'num_'
+        regions_path = 'regions_'
+        # folders dictionary
+        chars_dic = {"black":"black", "white":"white"} 
+        nums_dic = {"white":"white", "black":"black"}
+        regions_dic = {"region_black":"black", "region_white":"white", "regions_type_4":"type_4"}
+        
+        # black chars
+        self.black_chars_images, self.black_chars_names = self.init_component(
+            components_path + chars_path + chars_dic["black"] + '/')
+        # white chars
+        self.white_chars_images, self.white_chars_names = self.init_component(
+            components_path + chars_path + chars_dic["white"] + '/')
+        
+        # white numbers
+        self.white_numbers_images, self.white_numbers_names = self.init_component(
+            components_path + nums_path + nums_dic["white"] + '/')
+        # black numbers
+        self.black_numbers_images, self.black_numbers_names = self.init_component(
+            components_path + nums_path + nums_dic["black"] + '/')
+        
+        # white regions
+        self.white_regions_images, self.white_regions_names = self.init_component(
+            components_path + regions_path + regions_dic["region_white"] + '/')
+        # black regions
+        self.black_regions_images, self.black_regions_names = self.init_component(
+            components_path + regions_path + regions_dic["region_black"] + '/')
+        # type 4 regions
+        self.type_4_regions_images, self.type_4_regions_names = self.init_component(
+            components_path + regions_path + regions_dic["regions_type_4"] + '/')
+        
+        
+        self.type_1_components = {"Plate_images":self.Plate_1_images,
+                                  "Plate_resize":(520, 110),
+                                  "Chars":[self.black_chars_images, self.black_chars_names],  
+                                  "Numbers":[self.black_numbers_images, self.black_numbers_names], 
+                                  "Order":[{"type":"Numbers", "place":(50,15), "resize":(50,80)}, 
+                                           {"type":"Numbers", "place":(105,15), "resize":(50,80)}, 
+                                           {"type":"Chars", "place":(160,20), "resize":(50,70)},  
+                                           {"type":"Numbers", "place":(255,15), "resize":(50,80)}, 
+                                           {"type":"Numbers", "place":(310,15), "resize":(50,80)}, 
+                                           {"type":"Numbers", "place":(365,15), "resize":(50,80)}, 
+                                           {"type":"Numbers", "place":(420,15), "resize":(50,80)}]}
+        
+        self.type_2_components = {"Plate_images":self.Plate_2_images,
+                                  "Plate_resize":(335, 155),
+                                  "Chars":[self.black_chars_images, self.black_chars_names],  
+                                  "Numbers":[self.black_numbers_images, self.black_numbers_names], 
+                                  "Order":[{"type":"Numbers", "place":(10,50), "resize":(40,80)}, 
+                                           {"type":"Numbers", "place":(55,50), "resize":(40,80)}, 
+                                           {"type":"Chars", "place":(100,60), "resize":(40,70)},  
+                                           {"type":"Numbers", "place":(150,50), "resize":(40,80)}, 
+                                           {"type":"Numbers", "place":(195,50), "resize":(40,80)}, 
+                                           {"type":"Numbers", "place":(240,50), "resize":(40,80)}, 
+                                           {"type":"Numbers", "place":(285,50), "resize":(40,80)}]}
+        
+        self.type_3_components = {"Plate_images":self.Plate_3_images,
+                                  "Plate_resize":(335, 170),
+                                  "Chars":[self.black_chars_images, self.black_chars_names],  
+                                  "Numbers":[self.black_numbers_images, self.black_numbers_names],
+                                  "Regions":[self.black_regions_images, self.black_regions_names],
+                                  "Order":[{"type":"Regions", "place":(80,10), "resize":(80,50)}, 
+                                           {"type":"Numbers", "place":(180,10), "resize":(30,50)}, 
+                                           {"type":"Numbers", "place":(215,10), "resize":(30,50)},  
+                                           {"type":"Chars", "place":(15,70), "resize":(60,70)}, 
+                                           {"type":"Numbers", "place":(80,70), "resize":(50,90)}, 
+                                           {"type":"Numbers", "place":(140,70), "resize":(50,90)}, 
+                                           {"type":"Numbers", "place":(200,70), "resize":(50,90)}, 
+                                           {"type":"Numbers", "place":(260,70), "resize":(50,90)}]}
+        
+        
+        self.type_4_components = {"Plate_images":self.Plate_4_images,
+                                  "Plate_resize":(520, 110),
+                                  "Chars":[self.black_chars_images, self.black_chars_names],  
+                                  "Numbers":[self.black_numbers_images, self.black_numbers_names],
+                                  "Regions":[ self.type_4_regions_images, self.type_4_regions_names],
+                                  "Order":[{"type":"Regions", "place":(30,15), "resize":(50,80)}, 
+                                           {"type":"Numbers", "place":(90,15), "resize":(50,80)}, 
+                                           {"type":"Numbers", "place":(145,15), "resize":(50,80)},  
+                                           {"type":"Chars", "place":(200,20), "resize":(50,70)}, 
+                                           {"type":"Numbers", "place":(270,15), "resize":(50,80)}, 
+                                           {"type":"Numbers", "place":(325,15), "resize":(50,80)}, 
+                                           {"type":"Numbers", "place":(380,15), "resize":(50,80)}, 
+                                           {"type":"Numbers", "place":(435,15), "resize":(50,80)}]}
+        
+        self.type_5_components = {"Plate_images":self.Plate_5_images,
+                                  "Plate_resize":(440, 220),
+                                  "Chars":[self.white_chars_images, self.white_chars_names],  
+                                  "Numbers":[self.white_numbers_images, self.white_numbers_names],
+                                  "Order":[{"type":"Numbers", "place":(115,15), "resize":(65,55)}, 
+                                           {"type":"Numbers", "place":(185,15), "resize":(65,55)},  
+                                           {"type":"Chars", "place":(255,10), "resize":(65,55)}, 
+                                           {"type":"Numbers", "place":(15,80), "resize":(95,125)}, 
+                                           {"type":"Numbers", "place":(120,80), "resize":(95,125)}, 
+                                           {"type":"Numbers", "place":(225,80), "resize":(95,125)}, 
+                                           {"type":"Numbers", "place":(330,80), "resize":(95,125)}]}
+        
+        self.type_6_components = {"Plate_images":self.Plate_6_images,
+                                  "Plate_resize":(335, 170),
+                                  "Chars":[self.white_chars_images, self.white_chars_names],  
+                                  "Numbers":[self.white_numbers_images, self.white_numbers_names],
+                                  "Regions":[self.white_regions_images, self.white_regions_names],
+                                  "Order":[
+                                           {"type":"Regions", "place":(90,10), "resize":(80,50)}, 
+                                           {"type":"Numbers", "place":(170,10), "resize":(35,50)}, 
+                                           {"type":"Numbers", "place":(210,10), "resize":(35,50)},  
+                                           {"type":"Chars", "place":(15,65), "resize":(60,65)}, 
+                                           {"type":"Numbers", "place":(95,65), "resize":(50,95)}, 
+                                           {"type":"Numbers", "place":(150,65), "resize":(50,95)}, 
+                                           {"type":"Numbers", "place":(205,65), "resize":(50,95)}, 
+                                           {"type":"Numbers", "place":(260,65), "resize":(50,95)}]}
+        
+        
+        
+        self.components_to_types = {"1":self.type_1_components, 
+                                    "2":self.type_2_components, 
+                                    "3":self.type_3_components, 
+                                    "4":self.type_4_components, 
+                                    "5":self.type_5_components,  
+                                    "6":self.type_6_components}
+        
+        self.cars_images, self.cars_list, self.cars_boxes_dic = self.init_car_data(cars_path)
+        self.backgrounds, self.background_list = self.init_component(background_path)
+             
+          
+    def init_component(self, files_dir):
+        # a bit initialization
+        components = list()
+        components_list = list()
+        files_path = os.listdir(files_dir)
+        files_path = [f for f in os.listdir(files_dir) if f.endswith('.jpg') or f.endswith('.png')]
+        
+        for file in files_path:
+            component_path = files_dir + file
+            component = Image.open(component_path)
+            components.append(component)
+            components_list.append(file)
+        return components, components_list
+    
+    def build_data(self, desired_number, desired_types, desired_dataset, count):
+        
+        for iteration in range(desired_number):
+            
+            # generate desired plate
+            plate_type = random.choice(self.plate_types)
+            while plate_type not in desired_types:
+                plate_type = random.choice(self.plate_types)
+            
+            generated_plate, label = self.build_plate(plate_type)
+            if desired_dataset == 'recognition':
+                desired_height = 110
+                
+                if random.random() < 0.9:
+                    generated_plate = image_filtering(generated_plate)
+                self.save_plate(generated_plate, label, desired_height)    
+                try:
+                    
+                    print(str(count) + " / " + str(desired_number))
+                    count += 1
+                
+                except:
+                    print("Fuck! Save error, probably same combination")
+                
+                continue
+            
+               
+            full_img, bbox = self.generate_full_image(desired_dataset, generated_plate)
+            
+            try:
+                self.save_img_annotation(full_img, bbox, label, plate_type, count)
+                print(str(count) + " / " + str(desired_number)) 
+                count += 1
+            except:
+                print('Could not create or save')
+    
+    def build_plate(self, plate_type):
+        generated_plate = None
+        components = self.components_to_types[plate_type]
+        plates = components["Plate_images"]
+        # choose plate
+        Plate_img = random.choice(plates)
+        Plate_img = Plate_img.resize(components["Plate_resize"], PIL.Image.ANTIALIAS)
+        Generated_Plate, Plate_label = self.place_components(Plate_img, components)
+            
+        Generated_Plate = Generated_Plate.convert('RGB')
+        Generated_Plate = random_bright_contrast(Generated_Plate)
+        return Generated_Plate, Plate_label
+    
+    def place_components(self, Plate_img, components):
+        label = str()
+        for component in components["Order"]:
+            index = random.choice(range(len(components[component["type"]][0])))
+            component_img = components[component["type"]][0][index]
+            
+            component_label = components[component["type"]][1][index][:-4]
+            resize_ratio = component["resize"]
+            resized_component_img = component_img.resize(resize_ratio, PIL.Image.ANTIALIAS)
+            xmin,ymin = component["place"]
+            xmax, ymax = xmin + resized_component_img.size[0], ymin + resized_component_img.size[1]
+            place_holder = (xmin, ymin, xmax, ymax)
+            Plate_img.paste(resized_component_img, place_holder, mask = resized_component_img)
+            label += component_label
+        return Plate_img, label
+    
+    def save_plate(self, plate, label, desired_height):
+        wpercent = (desired_height / float(plate.size[1]))
+        hsize = int((float(plate.size[0]) * float(wpercent)))
+        plate = plate.resize((hsize, desired_height), PIL.Image.ANTIALIAS)
+        
+        plate = plate.convert('RGB')
+        plate = plate.convert('L')
+        
+        plate.save(self.recognition_data_path + label + '.jpg')
+        
+    def init_car_data(self, cars_path):
+        cars_xml = [f for f in os.listdir(cars_path) if f.endswith('.xml')]
+        cars_imgs, cars_list = self.init_component(cars_path)
+        car_boxes_dic = {}
+        for car_xml in cars_xml:
+            car_boxes = []
+            car_box1 = []
+            car_box2 = []
+            node = parse(cars_path + car_xml)
+            elems = node.findall('object')
+            
+            for item in (['xmin', 'ymin', 'xmax', 'ymax']):
+                car_box1.append(int(int(elems[0].find('bndbox').find(item).text)))
+                car_box2.append(int(int(elems[1].find('bndbox').find(item).text)))
+            car_boxes.append(car_box1)
+            car_boxes.append(car_box2)
+            car_boxes_dic[car_xml[:-3] + "png"] = car_boxes
+        return cars_imgs, cars_list, car_boxes_dic
+    def generate_full_image(self, desired_dataset, plate):
+         
+        if random.random() < 0.3:
+            plate = insert_black_mask(plate)
+        
+        
+        full_img, bbox = None, None
+        car = random.choice(self.cars_list)
+        print(car)
+        car_img = Image.open(self.cars_path + car)
+        car_boxes = self.cars_boxes_dic[car]
+        background = random.choice(self.background_list)
+        background_img = Image.open(self.background_path + background)
+        
+        car_with_plate_img, bbox = image_augmentation(plate, car_img, car_boxes)
+        full_img, bbox = self.insert_car_to_background(background_img, car_with_plate_img, desired_dataset, bbox)
+        
+        return full_img, bbox
+        
+    def insert_car_to_background(self, background_img, car_with_plate_img, desired_dataset, bbox):
+        
+        background_img = background_img.resize((1600, 1200), PIL.Image.ANTIALIAS)
+        img_width, img_height = background_img.size
+        
+        background_car_holder_width = None
+        
+        if desired_dataset == 'parking':
+            background_car_holder_width = random.randint(int(img_width * 0.7), int(img_width * 0.8))
+            width_prop = random.randint(int(img_width * 0.05), int(img_width * 0.4))
+            height_prop = random.randint(int(img_height * 0.1), int(img_height * 0.3))
+        elif desired_dataset == 'cctv':
+            width_prop = random.randint(int(img_width * 0.1), int(img_width * 0.4))
+            height_prop = random.randint(int(img_height * 0.1), int(img_height * 0.25))
+            background_car_holder_width = random.randint(int(img_width * 0.3 * height_prop * 4/1200), 
+                                                         int(img_width * 0.45 * height_prop * 4/1200))
+        else:
+            print('Error!')
+            return
+        
+        wpercent = (background_car_holder_width / float(car_with_plate_img.size[0]))
+        hsize = int((float(car_with_plate_img.size[1]) * float(wpercent)))
+        resized_car_with_plate_img = car_with_plate_img.resize((background_car_holder_width, hsize), PIL.Image.ANTIALIAS)
+        
+        # take bbox
+        bbox[0] = int(bbox[0] * resized_car_with_plate_img.size[0] / car_with_plate_img.size[0])
+        bbox[1] = int(bbox[1] * resized_car_with_plate_img.size[1] / car_with_plate_img.size[1])
+        bbox[2] = int(bbox[2] * resized_car_with_plate_img.size[0] / car_with_plate_img.size[0])
+        bbox[3] = int(bbox[3] * resized_car_with_plate_img.size[1] / car_with_plate_img.size[1])
+        
+        # insert car in background
+        width, height = resized_car_with_plate_img.size
+        
+
+        background_img.paste(resized_car_with_plate_img, (width_prop, height_prop, width + width_prop, height + height_prop), 
+                             mask = resized_car_with_plate_img)
+        background_img = background_img.convert('L')
+        
+        bbox[0] += width_prop
+        bbox[1] += height_prop
+        bbox[2] += width_prop
+        bbox[3] += height_prop
+        
+        #background_img.save('test.jpg')
+        
+        return background_img, bbox
+        
+    def save_img_annotation(self, full_img, bbox, label, plate_type, count):
+        saved = False
+        
+        
+        #self.detection_images_path = detection_images_path
+        #self.detection_annotations_path = detection_annotations_path
+            
+        
+        try:
+            # annotation file making
+            file_name = str(count) + '_P' + plate_type + '_img.jpg'
+            img_save_path = self.detection_images_path + file_name
+            full_img.save(img_save_path)
+
+
+            writer = Writer(img_save_path, full_img.size[0], full_img.size[1])
+            writer.addObject('P' + plate_type, bbox[0], bbox[1], bbox[2], bbox[3])
+            writer.save(self.detection_annotations_path + file_name[:-3] +'xml')
+            saved = True
+            
+        except:
+            print("Could not save")
+        
+        
+        return saved
+
+components_path = 'components_2/'
+detection_images_path = '../dataset/yolo'
+detection_annotations_path = 'detection_annotations/'
+recognition_data_path = 'recognition_data/'
+
+# choose right folder for desired type
+
+# for parking -> 
+# cars_path = 'cars/'
+# background_path = 'background/'
+
+# for cctv -> 
+# cars_path = 'cctv_cars/'
+# background_path = 'cctv_background/'
+
+
+cars_path = 'cars/'
+background_path = 'background/'
+
+
+desired_number = 10000
+desired_types = ['1', '2', '3', '4', '5', '6']
+# cctv parking recognition provide choose one to generate and store things
+desired_dataset = 'parking'
+count = 1
+
+
+
+generator = ImageGenerator(components_path = components_path, 
+                          detection_images_path = detection_images_path, 
+                          detection_annotations_path = detection_annotations_path, 
+                          recognition_data_path = recognition_data_path, 
+                           cars_path = cars_path, 
+                           background_path = background_path)
+        
+generator.build_data(desired_number = desired_number, 
+                    desired_types = desired_types, 
+                    desired_dataset = desired_dataset, 
+                     count = count) 
\ No newline at end of file

+import os
+import PIL
+from PIL import Image, ImageEnhance, ImageFilter
+import random
+import cv2, argparse
+import pandas as pd
+import numpy as np
+
+# from xml.etree.ElementTree import parse
+# from pascal_voc_writer import Writer
+
+
+def image_overlay(src, color="#FFFFFF", alpha=0.5):
+    overlay = Image.new("RGBA", src.size, color)
+    bw_src = ImageEnhance.Color(src).enhance(0.0)
+    return Image.blend(bw_src, overlay, alpha)
+
+
+def insert_black_mask(img):
+    black_mask = Image.new("RGBA", img.size, (0, 0, 0))
+    val = random.randint(100, 150)
+    black_mask.putalpha(val)
+
+    img.paste(black_mask, (0, 0, img.size[0], img.size[1]), mask=black_mask)
+    return img
+
+
+def random_bright_contrast(img):
+    img = ImageEnhance.Contrast(img)
+    num_contrast = random.uniform(0.7, 1.5)
+    img = img.enhance(num_contrast)
+    num_brightness = random.uniform(0.8, 1.0)
+    img = ImageEnhance.Brightness(img)
+    img = img.enhance(num_brightness)
+
+    if random.random() < 0.1:
+        img = img.filter(ImageFilter.BLUR)
+
+    return img
+
+
+def image_filtering(img, ang_range=1.2, shear_range=1.5, trans_range=1):
+
+    img = np.array(img)
+
+    # Rotation
+    ang_rot = np.random.uniform(ang_range) - ang_range / 2
+    rows, cols, ch = img.shape
+    Rot_M = cv2.getRotationMatrix2D((cols / 2, rows / 2), ang_rot, 0.9)
+
+    # Translation
+    tr_x = trans_range * np.random.uniform() - trans_range / 2
+    tr_y = trans_range * np.random.uniform() - trans_range / 2
+    Trans_M = np.float32([[1, 0, tr_x], [0, 1, tr_y]])
+
+    # Shear
+    pts1 = np.float32([[5, 5], [20, 5], [5, 20]])
+
+    pt1 = 5 + shear_range * np.random.uniform() - shear_range / 2
+    pt2 = 20 + shear_range * np.random.uniform() - shear_range / 2
+    pts2 = np.float32([[pt1, 5], [pt2, pt1], [5, pt2]])
+    shear_M = cv2.getAffineTransform(pts1, pts2)
+
+    img = cv2.warpAffine(img, Rot_M, (cols, rows))
+    img = cv2.warpAffine(img, Trans_M, (cols, rows))
+    img = cv2.warpAffine(img, shear_M, (cols, rows))
+
+    img = Image.fromarray(img)
+
+    return img
+
+
+def image_augmentation(img, car_img, car_boxes):
+    bbox = None
+    car_box_1 = car_boxes[0]
+    car_box_2 = car_boxes[1]
+
+    width_of_plate_holder = car_box_1[2] - car_box_1[0]
+    wpercent = width_of_plate_holder / float(img.size[0])
+    hsize = int((float(img.size[1]) * float(wpercent)))
+    img = img.resize((width_of_plate_holder, hsize), PIL.Image.ANTIALIAS)
+
+    img = np.array(img)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2BGRA)
+    w, h, _ = img.shape
+    pts1 = np.float32([[0, 0], [0, w], [h, 0], [h, w]])
+
+    h_change = abs(car_box_1[1] - car_box_2[1])
+    if (abs(car_box_1[0] - car_box_2[0])) < (width_of_plate_holder / 2):
+        pts2 = np.float32([[0, h_change], [0, w], [h, 0], [h, w - h_change]])
+    elif (abs(car_box_1[0] - car_box_2[0])) > (width_of_plate_holder / 2):
+        pts2 = np.float32([[0, 0], [0, w - h_change], [h, h_change], [h, w]])
+    else:
+        pts2 = np.float32([[0, 0], [0, w], [h, 0], [h, w]])
+
+    M = cv2.getPerspectiveTransform(pts1, pts2)
+    img = cv2.warpPerspective(
+        img,
+        M,
+        (h, w),
+        flags=cv2.INTER_CUBIC,
+        borderMode=cv2.BORDER_CONSTANT,
+        borderValue=[0, 0, 0, 0],
+    )
+    img = Image.fromarray(img)
+
+    width, height = img.size
+    width_prop = car_boxes[0][0]
+    height_prop = car_boxes[0][1]
+    car_img.paste(
+        img,
+        (width_prop, height_prop, width + width_prop, height + height_prop),
+        mask=img,
+    )
+
+    bbox = [width_prop, height_prop, width + width_prop, height + height_prop]
+
+    return car_img, bbox
+
+
+class ImageGenerator:
+    def __init__(
+        self,
+        components_path,
+        plate_images_path,
+        detection_annotations_path,
+        recognition_data_path,
+        cars_path,
+        background_path,
+    ):
+
+        # paths inintialization
+        self.components_path = components_path
+        self.plate_images_path = plate_images_path
+        self.detection_annotations_path = detection_annotations_path
+        self.recognition_data_path = recognition_data_path
+        self.cars_path = cars_path
+        self.background_path = background_path
+
+        # plates initializtion
+        plate_files_path = "plates_type_"
+        self.plate_types = ["blank", "1", "2", "3", "4", "5", "6"]
+        # plate type 1
+        self.Plate_1_images, self.Plate_1_names = self.init_component(
+            components_path + plate_files_path + self.plate_types[1] + "/"
+        )
+        # plate type 2
+        self.Plate_2_images, self.Plate_2_names = self.init_component(
+            components_path + plate_files_path + self.plate_types[2] + "/"
+        )
+        # plate type 3
+        self.Plate_3_images, self.Plate_3_names = self.init_component(
+            components_path + plate_files_path + self.plate_types[3] + "/"
+        )
+        # plate type 4
+        self.Plate_4_images, self.Plate_4_names = self.init_component(
+            components_path + plate_files_path + self.plate_types[4] + "/"
+        )
+        # plate type 5
+        self.Plate_5_images, self.Plate_5_names = self.init_component(
+            components_path + plate_files_path + self.plate_types[5] + "/"
+        )
+        # plate type 6
+        self.Plate_6_images, self.Plate_6_names = self.init_component(
+            components_path + plate_files_path + self.plate_types[6] + "/"
+        )
+
+        # components init paths
+        chars_path = "char_"
+        nums_path = "num_"
+        regions_path = "regions_"
+        # folders dictionary
+        chars_dic = {"black": "black", "white": "white"}
+        nums_dic = {"white": "white", "black": "black"}
+        regions_dic = {
+            "region_black": "black",
+            "region_white": "white",
+            "regions_type_4": "type_4",
+        }
+
+        # black chars
+        self.black_chars_images, self.black_chars_names = self.init_component(
+            components_path + chars_path + chars_dic["black"] + "/"
+        )
+        # white chars
+        self.white_chars_images, self.white_chars_names = self.init_component(
+            components_path + chars_path + chars_dic["white"] + "/"
+        )
+
+        # white numbers
+        self.white_numbers_images, self.white_numbers_names = self.init_component(
+            components_path + nums_path + nums_dic["white"] + "/"
+        )
+        # black numbers
+        self.black_numbers_images, self.black_numbers_names = self.init_component(
+            components_path + nums_path + nums_dic["black"] + "/"
+        )
+
+        # white regions
+        self.white_regions_images, self.white_regions_names = self.init_component(
+            components_path + regions_path + regions_dic["region_white"] + "/"
+        )
+        # black regions
+        self.black_regions_images, self.black_regions_names = self.init_component(
+            components_path + regions_path + regions_dic["region_black"] + "/"
+        )
+        # type 4 regions
+        self.type_4_regions_images, self.type_4_regions_names = self.init_component(
+            components_path + regions_path + regions_dic["regions_type_4"] + "/"
+        )
+
+        self.type_1_components = {
+            "Plate_images": self.Plate_1_images,
+            "Plate_resize": (520, 110),
+            "Chars": [self.black_chars_images, self.black_chars_names],
+            "Numbers": [self.black_numbers_images, self.black_numbers_names],
+            "Order": [
+                {"type": "Numbers", "place": (50, 15), "resize": (50, 80)},
+                {"type": "Numbers", "place": (105, 15), "resize": (50, 80)},
+                {"type": "Chars", "place": (160, 20), "resize": (50, 70)},
+                {"type": "Numbers", "place": (255, 15), "resize": (50, 80)},
+                {"type": "Numbers", "place": (310, 15), "resize": (50, 80)},
+                {"type": "Numbers", "place": (365, 15), "resize": (50, 80)},
+                {"type": "Numbers", "place": (420, 15), "resize": (50, 80)},
+            ],
+        }
+
+        self.type_2_components = {
+            "Plate_images": self.Plate_2_images,
+            "Plate_resize": (335, 155),
+            "Chars": [self.black_chars_images, self.black_chars_names],
+            "Numbers": [self.black_numbers_images, self.black_numbers_names],
+            "Order": [
+                {"type": "Numbers", "place": (10, 50), "resize": (40, 80)},
+                {"type": "Numbers", "place": (55, 50), "resize": (40, 80)},
+                {"type": "Chars", "place": (100, 60), "resize": (40, 70)},
+                {"type": "Numbers", "place": (150, 50), "resize": (40, 80)},
+                {"type": "Numbers", "place": (195, 50), "resize": (40, 80)},
+                {"type": "Numbers", "place": (240, 50), "resize": (40, 80)},
+                {"type": "Numbers", "place": (285, 50), "resize": (40, 80)},
+            ],
+        }
+
+        self.type_3_components = {
+            "Plate_images": self.Plate_3_images,
+            "Plate_resize": (335, 170),
+            "Chars": [self.black_chars_images, self.black_chars_names],
+            "Numbers": [self.black_numbers_images, self.black_numbers_names],
+            "Regions": [self.black_regions_images, self.black_regions_names],
+            "Order": [
+                {"type": "Regions", "place": (80, 10), "resize": (80, 50)},
+                {"type": "Numbers", "place": (180, 10), "resize": (30, 50)},
+                {"type": "Numbers", "place": (215, 10), "resize": (30, 50)},
+                {"type": "Chars", "place": (15, 70), "resize": (60, 70)},
+                {"type": "Numbers", "place": (80, 70), "resize": (50, 90)},
+                {"type": "Numbers", "place": (140, 70), "resize": (50, 90)},
+                {"type": "Numbers", "place": (200, 70), "resize": (50, 90)},
+                {"type": "Numbers", "place": (260, 70), "resize": (50, 90)},
+            ],
+        }
+
+        self.type_4_components = {
+            "Plate_images": self.Plate_4_images,
+            "Plate_resize": (520, 110),
+            "Chars": [self.black_chars_images, self.black_chars_names],
+            "Numbers": [self.black_numbers_images, self.black_numbers_names],
+            "Regions": [self.type_4_regions_images, self.type_4_regions_names],
+            "Order": [
+                {"type": "Regions", "place": (30, 15), "resize": (50, 80)},
+                {"type": "Numbers", "place": (90, 15), "resize": (50, 80)},
+                {"type": "Numbers", "place": (145, 15), "resize": (50, 80)},
+                {"type": "Chars", "place": (200, 20), "resize": (50, 70)},
+                {"type": "Numbers", "place": (270, 15), "resize": (50, 80)},
+                {"type": "Numbers", "place": (325, 15), "resize": (50, 80)},
+                {"type": "Numbers", "place": (380, 15), "resize": (50, 80)},
+                {"type": "Numbers", "place": (435, 15), "resize": (50, 80)},
+            ],
+        }
+
+        self.type_5_components = {
+            "Plate_images": self.Plate_5_images,
+            "Plate_resize": (440, 220),
+            "Chars": [self.white_chars_images, self.white_chars_names],
+            "Numbers": [self.white_numbers_images, self.white_numbers_names],
+            "Order": [
+                {"type": "Numbers", "place": (115, 15), "resize": (65, 55)},
+                {"type": "Numbers", "place": (185, 15), "resize": (65, 55)},
+                {"type": "Chars", "place": (255, 10), "resize": (65, 55)},
+                {"type": "Numbers", "place": (15, 80), "resize": (95, 125)},
+                {"type": "Numbers", "place": (120, 80), "resize": (95, 125)},
+                {"type": "Numbers", "place": (225, 80), "resize": (95, 125)},
+                {"type": "Numbers", "place": (330, 80), "resize": (95, 125)},
+            ],
+        }
+
+        self.type_6_components = {
+            "Plate_images": self.Plate_6_images,
+            "Plate_resize": (335, 170),
+            "Chars": [self.white_chars_images, self.white_chars_names],
+            "Numbers": [self.white_numbers_images, self.white_numbers_names],
+            "Regions": [self.white_regions_images, self.white_regions_names],
+            "Order": [
+                {"type": "Regions", "place": (90, 10), "resize": (80, 50)},
+                {"type": "Numbers", "place": (170, 10), "resize": (35, 50)},
+                {"type": "Numbers", "place": (210, 10), "resize": (35, 50)},
+                {"type": "Chars", "place": (15, 65), "resize": (60, 65)},
+                {"type": "Numbers", "place": (95, 65), "resize": (50, 95)},
+                {"type": "Numbers", "place": (150, 65), "resize": (50, 95)},
+                {"type": "Numbers", "place": (205, 65), "resize": (50, 95)},
+                {"type": "Numbers", "place": (260, 65), "resize": (50, 95)},
+            ],
+        }
+
+        self.components_to_types = {
+            "1": self.type_1_components,
+            "2": self.type_2_components,
+            "3": self.type_3_components,
+            "4": self.type_4_components,
+            "5": self.type_5_components,
+            "6": self.type_6_components,
+        }
+
+        ####################################################################################
+        # self.cars_images, self.cars_list, self.cars_boxes_dic = self.init_car_data(
+        #     cars_path
+        # )
+        # self.backgrounds, self.background_list = self.init_component(background_path)
+
+    def init_component(self, files_dir):
+        # a bit initialization
+        components = list()
+        components_list = list()
+        files_path = os.listdir(files_dir)
+        files_path = [
+            f for f in os.listdir(files_dir) if f.endswith(".jpg") or f.endswith(".png")
+        ]
+
+        for file in files_path:
+            component_path = files_dir + file
+            component = Image.open(component_path)
+            components.append(component)
+            components_list.append(file)
+        return components, components_list
+
+    def build_data(self, desired_number, desired_types, desired_dataset, count):
+        # def build_data(self, desired_number, desired_types, count):
+
+        for iteration in range(desired_number):
+
+            # generate desired plate
+            plate_type = random.choice(self.plate_types)
+            while plate_type not in desired_types:
+                plate_type = random.choice(self.plate_types)
+
+            generated_plate, label = self.build_plate(plate_type)
+
+            # parking??
+            # if desired_dataset == "recognition":
+            #     desired_height = 110
+
+            #     if random.random() < 0.9:
+            #         generated_plate = image_filtering(generated_plate)
+            #     self.save_plate(generated_plate, label, desired_height)
+            #     try:
+
+            #         print(str(count) + " / " + str(desired_number))
+            #         count += 1
+
+            #     except:
+            #         print("Fuck! Save error, probably same combination")
+
+            #     continue
+
+            ################################################################################
+            # full_img, bbox = self.generate_full_image(desired_dataset, generated_plate)
+
+            # try:
+            #     # self.save_img_annotation(full_img, bbox, label, plate_type, count)
+            #     self.save_img_annotation(generated_plate, label, plate_type, count)
+            #     print(str(count) + " / " + str(desired_number))
+            #     count += 1
+
+            # except:
+            #     print("Could not create or save")
+
+            self.save_img_annotation(generated_plate, label, plate_type, count)
+            print(str(count) + " / " + str(desired_number))
+            count += 1
+
+    def build_plate(self, plate_type):
+        generated_plate = None
+        components = self.components_to_types[plate_type]
+        plates = components["Plate_images"]
+        # choose plate
+        Plate_img = random.choice(plates)
+        Plate_img = Plate_img.resize(components["Plate_resize"], PIL.Image.ANTIALIAS)
+        Generated_Plate, Plate_label = self.place_components(Plate_img, components)
+
+        Generated_Plate = Generated_Plate.convert("RGB")
+        Generated_Plate = random_bright_contrast(Generated_Plate)
+        return Generated_Plate, Plate_label
+
+    def place_components(self, Plate_img, components):
+        label = str()
+        for component in components["Order"]:
+            index = random.choice(range(len(components[component["type"]][0])))
+            component_img = components[component["type"]][0][index]
+
+            component_label = components[component["type"]][1][index][:-4]
+            resize_ratio = component["resize"]
+            resized_component_img = component_img.resize(
+                resize_ratio, PIL.Image.ANTIALIAS
+            )
+            xmin, ymin = component["place"]
+            xmax, ymax = (
+                xmin + resized_component_img.size[0],
+                ymin + resized_component_img.size[1],
+            )
+            place_holder = (xmin, ymin, xmax, ymax)
+            Plate_img.paste(
+                resized_component_img, place_holder, mask=resized_component_img
+            )
+            label += component_label
+        return Plate_img, label
+
+    # def save_img_annotation(self, full_img, bbox, label, plate_type, count):  #
+    def save_img_annotation(self, plate_img, label, plate_type, count):  #
+        saved = False
+
+        # print("label param : ", label)
+
+        # self.plate_images_path = plate_images_path
+        # self.detection_annotations_path = detection_annotations_path
+
+        # try:
+        #     # annotation file making
+        #     file_name = str(count) + "_P" + plate_type + "_img.jpg"
+        #     img_save_path = self.plate_images_path + file_name
+        #     plate_img.save(img_save_path)
+
+        #     annotations = []
+        #     vectors = self.components_to_types[plate_type]["Order"]
+        #     for v in vectors:
+        #         x1, y1 = v["place"]
+        #         x2 = v["place"][0] + v["resize"][0]
+        #         y2 = v["place"][1] + v["resize"][1]
+        #         annotations.append([img_save_path, x1, y1, x2, y2])
+        #     saved = True
+
+        #     annotations = np.array(annotations)
+        #     label = label.split("")
+        #     if plate_type in ["4","5","6"]:
+        #         col = [label[0] + label[1]] + label[2:]
+        #     else:
+        #         col = label
+
+        #     print(col)
+        #     np.insert(annotations, -1, col, axis=1)
+
+        #     print(annotations)
+
+        #     pd.DataFrame(
+        #         annotations, columns=["image path", "x1", "y1", "x2", "y2", "label"]
+        #     )
+        #     pd.to_csv("annotations.csv", mode="a", header=False, index=True)
+
+        # except:
+        #     print("Could not save")
+
+        file_name = str(count) + "_P" + plate_type + "_img.jpg"
+        img_save_path = self.plate_images_path + file_name
+        plate_img.save(img_save_path)
+
+        annotations = []
+        vectors = self.components_to_types[plate_type]["Order"]
+        width, height = self.components_to_types[plate_type]["Plate_resize"]
+        for v in vectors:
+            x1, y1 = v["place"]
+            x2 = v["place"][0] + v["resize"][0]
+            y2 = v["place"][1] + v["resize"][1]
+            annotations.append([file_name, width, height, x1, y1, x2, y2])
+        saved = True
+
+        annotations = np.array(annotations)
+        label = list(label)
+        # print("label : ", label)
+
+        if plate_type in ["3", "4", "6"]:
+            col = label[:3] + [label[3] + label[4]] + label[5:]
+        else:
+            col = label[:2] + [label[2] + label[3]] + label[4:]
+
+        # print(col)
+        # np.insert(annotations, 5, col, axis=1)
+
+        # print(annotations)
+
+        df = pd.DataFrame(
+            # annotations, columns=["image path", "x1", "y1", "x2", "y2", "label"]
+            annotations,
+            columns=["image path","width","height", "x1", "y1", "x2", "y2"],
+        )
+        df["label"] = col
+
+        print(df)
+
+        df.to_csv(self.plate_images_path+"annotations.csv", mode="a", header=False, index=False)
+
+        return saved
+
+
+components_path = "components_2/"
+plate_images_path = "../dataset/test/test1/"
+detection_annotations_path = "detection_annotations/"
+recognition_data_path = "recognition_data/"
+
+# choose right folder for desired type
+
+# for parking ->
+# cars_path = 'cars/'
+# background_path = 'background/'
+
+# for cctv ->
+# cars_path = 'cctv_cars/'
+# background_path = 'cctv_background/'
+
+
+cars_path = "cars/"
+background_path = "background/"
+
+
+desired_number = 500
+desired_types = ["1", "2", "3", "4", "5", "6"]
+# cctv parking recognition provide choose one to generate and store things
+desired_dataset = "parking"
+count = 1
+
+
+generator = ImageGenerator(
+    components_path=components_path,
+    plate_images_path=plate_images_path,
+    detection_annotations_path=detection_annotations_path,
+    recognition_data_path=recognition_data_path,
+    cars_path=cars_path,
+    background_path=background_path,
+)
+
+generator.build_data(
+    desired_number=desired_number,
+    desired_types=desired_types,
+    desired_dataset=desired_dataset,
+    count=count,
+)

++Subproject commit 30a272ccb3778bade716d0fb1556df47ef8225f4