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circle_crop.py
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circle_crop.py
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# -*-*- coding utf-8 -*-*-
# to hough circling and get chess image
import cv2
import os
import shutil
import sys
import datetime
import numpy as np
from PIL import Image
import algorithm
import Global_Params
from keras.models import load_model
from keras.preprocessing.image import img_to_array
import filter
import server_one
import time
from load_data import str2int
# img_height = -1
# img_width = -1
CHESS_IMAGE_SHOW = True
SAVE_CROP = True
SHOW_GLOBAL = False
def read_origin_image():
origin_image_path = Global_Params.M_systemCamTest_path
# origin_image_path = "./test_image_process/systemCamTest"
images = os.listdir(origin_image_path)
images = sorted(images,
key=lambda files: os.path.getmtime(os.path.join(origin_image_path, files)),
reverse=False)
count_image = 0
origin_image_list = []
for image in images:
image_full_path = os.path.join(origin_image_path, image)
img = Image.open(image_full_path)
print(image_full_path, " pixel: " + str(img.size[0]) + "*" + str(img.size[1])) # debug
count_image = count_image + 1
origin_image_list.append(image_full_path)
print("image list done! count = ", count_image)
if count_image == 0:
print("ERROR: no image in path: ", origin_image_path)
return -999
return origin_image_list, count_image
def hough_circle(origin_image_list, count_image):
save_dir = Global_Params.M_model_save_path + "/" # the model stored there
folder = Global_Params.M_image_circle_test_path
for filename in os.listdir(folder):
file_path = os.path.join(folder, filename)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
print('Failed to delete %s. Reason: %s' % (file_path, e))
# sort by last modified time
model_lists = os.listdir(save_dir)
model_lists = sorted(model_lists,
key=lambda files: os.path.getmtime(os.path.join(save_dir, files)),
reverse=False)
model_path_vertify = ""
for modelLists in os.listdir(save_dir):
model_path_vertify = os.path.join(save_dir, modelLists)
print(model_path_vertify)
# if model_path_vertify == "": # if the pwd is NULL
# print("No model saved!")
# exit()
#
# model = load_model(model_path_vertify)
# print("model loaded!")
# for index in range(0, count_image):
for index in range(len(origin_image_list)):
origin_image = cv2.imread(origin_image_list[index], cv2.IMREAD_COLOR)
print(origin_image_list[index])
if origin_image is None:
print("ERROR: circle.py line: 36, image loading failed!")
return -888
pil_origin_image = Image.open(origin_image_list[index])
origin_image_height = pil_origin_image.size[1]
origin_image_width = pil_origin_image.size[0]
img_height = origin_image_height
img_width = origin_image_width
gray_origin_image = cv2.cvtColor(origin_image, cv2.COLOR_RGB2GRAY)
gray_origin_image = cv2.medianBlur(gray_origin_image, 5) # 高斯滤波
# gray_origin_image = cv2.resize(gray_origin_image, (img_width, img_height))
# print("grey image show: index = ", index)
window_name = "grey of " + origin_image_list[index]
if CHESS_IMAGE_SHOW or SHOW_GLOBAL:
cv2.imshow(window_name, gray_origin_image)
if SHOW_GLOBAL:
flag = cv2.waitKey(1)
time.sleep(1)
save_path = Global_Params.M_imageProcessTestAns_path + "/grey_" + str(
index) + "_" + datetime.datetime.now().strftime("%Y-%m-%d-%H:%M:%S") + ".jpg"
cv2.imwrite(save_path, gray_origin_image)
print(save_path, " saved")
cv2.destroyWindow(window_name)
else:
flag = cv2.waitKey(0)
if flag == 13: # press enter to save the image
save_path = Global_Params.M_imageProcessTestAns_path + "/grey_" + str(index) + "_" + datetime.datetime.now().strftime("%Y-%m-%d-%H:%M:%S") + ".jpg"
cv2.imwrite(save_path, gray_origin_image)
print(save_path, " saved")
cv2.destroyWindow(window_name)
elif flag == 27:
cv2.destroyWindow(window_name)
else:
print("ERROR: file circle.py, line 56, flag invalid!")
# create mask
mask = []
circles = cv2.HoughCircles(
gray_origin_image, # input image, greyscale
cv2.HOUGH_GRADIENT,
1.0, # dp, the inverse ratio of resolution
round(min(img_height, img_width)/10.5), # Minimum distance between detected centers
param1=325, # Upper threshold for the internal Canny edge detector
param2=30, # Threshold for center detection
minRadius=round(min(img_height, img_width)/40.0), # Minimum radius to be detected, default 0
maxRadius=round(min(img_height, img_width)/15.0) # Maximum radius to be detected, default 0
)
# print(round(min(img_height, img_width)/8.0),
# round(min(img_height, img_width)/100.0),
# round(min(img_height, img_width)/20.0))
count_circle = 0
only_one_x = []
only_one_y = []
only_one_w = []
only_one_h = []
chess_x = []
chess_y = []
temp_origin = cv2.imread(origin_image_list[index], cv2.IMREAD_COLOR)
if circles is not None:
circles = np.uint16(np.around(circles))
for indexCircle in circles[0, :]:
only_one_x.append(indexCircle[0] - indexCircle[2])
only_one_y.append(indexCircle[1] - indexCircle[2])
only_one_w.append(indexCircle[2]*2)
only_one_h.append(indexCircle[2]*2)
# print("count_circle = ", count_circle)
count_circle = count_circle + 1
center = (indexCircle[0], indexCircle[1]) # circle center
chess_x.append(indexCircle[0])
chess_y.append(indexCircle[1])
radius = indexCircle[2] # circle radius
cv2.circle(temp_origin, center, radius, (255, 0, 255), 3)
mask_temp = np.zeros((pil_origin_image.size[1], pil_origin_image.size[0]), np.uint8)
cv2.circle(mask_temp, center, radius, (255, 255, 255), thickness=-1)
mask.append(mask_temp)
if count_circle == 0:
print("count_circle = 0, no circles found!")
print("count_circle = ", count_circle)
# print("hough circle image show: index = ", index)
window_name = "hough circle of " + origin_image_list[index] # or np.array(origin_image_list)[index]
# cv2.imshow(window_name, temp_origin)
# print(str(index) + "_" + datetime.datetime.now().strftime("%Y-%m-%d-%H:%M:%S") + ".jpg")
if CHESS_IMAGE_SHOW or SHOW_GLOBAL:
cv2.imshow(window_name, temp_origin)
if SHOW_GLOBAL:
flag = cv2.waitKey(1)
time.sleep(1)
save_path = Global_Params.M_imageProcessTestAns_path + "/circle_" + str(
index) + "_" + datetime.datetime.now().strftime("%Y-%m-%d-%H:%M:%S") + ".jpg"
cv2.imwrite(save_path, temp_origin)
print(save_path, " saved")
cv2.destroyWindow(window_name)
else:
flag = cv2.waitKey(0)
if flag == 13: # press enter to save the image
save_path = Global_Params.M_imageProcessTestAns_path + "/circle_" + str(index) + "_" + datetime.datetime.now().strftime("%Y-%m-%d-%H:%M:%S") + ".jpg"
cv2.imwrite(save_path, temp_origin)
print(save_path, " saved")
cv2.destroyWindow(window_name)
elif flag == 27:
cv2.destroyWindow(window_name)
else:
print("ERROR: file circle.py, line 88, flag invalid!")
data = []
for index_circle in range(count_circle):
# Copy that image using that mask
# print(origin_image.shape, " ", mask[index_circle].shape)
crop_cv_im = cv2.bitwise_and(origin_image, origin_image, mask=mask[index_circle])
# apply threshold
_, thresh = cv2.threshold(mask[index_circle], 1, 255, cv2.THRESH_BINARY)
# find contour
# contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
crop_cv_im = crop_cv_im[only_one_y[index_circle]:only_one_y[index_circle] + only_one_h[index_circle],
only_one_x[index_circle]:only_one_x[index_circle] + only_one_w[index_circle]]
# cv2.imshow(str(index_circle + 1) + " <crop>", crop_cv_im)
# print(str(index_circle + 1), " \t<crop> ", crop_cv_im.shape)
data_no_use_path = Global_Params.M_image_circle_test_path
if CHESS_IMAGE_SHOW:
cv2.imshow(str(index_circle + 1) + " <crop>", crop_cv_im)
if SAVE_CROP:
flag = cv2.waitKey(1)
time.sleep(0.05)
crop_cv_im = cv2.resize(crop_cv_im, (Global_Params.M_norm_size, Global_Params.M_norm_size))
new_origin_name = datetime.datetime.now().strftime("%Y-%m-%d-%H:%M:%S") + "_crop_" + str(
index) + "_" + str(index_circle) + ".jpg"
cv2.imwrite(os.path.join(data_no_use_path, new_origin_name), crop_cv_im)
cv2.destroyWindow(str(index_circle + 1) + " <crop>")
print("=============== " + os.path.join(data_no_use_path,
new_origin_name) + " ==SAVED===================")
else:
flag = cv2.waitKey(0)
if flag == 27:
cv2.destroyWindow(str(index_circle + 1) + " <crop>")
elif flag == 13:
crop_cv_im = cv2.resize(crop_cv_im, (Global_Params.M_norm_size, Global_Params.M_norm_size))
new_origin_name = datetime.datetime.now().strftime("%Y-%m-%d-%H:%M:%S") + "_crop_" + \
str(index) + "_" + str(index_circle) + ".jpg"
cv2.imwrite(os.path.join(data_no_use_path, new_origin_name), crop_cv_im)
cv2.destroyWindow(str(index_circle + 1) + " <crop>")
print("=============== " + os.path.join(data_no_use_path, new_origin_name) + " ==SAVED===================")
else:
print("generate_data.py, line:24, esc expected")
crop_cv_im = cv2.resize(crop_cv_im, (Global_Params.M_norm_size, Global_Params.M_norm_size))
crop_cv_im = img_to_array(crop_cv_im)
data.append(crop_cv_im)
# data = np.array(data)
# data = filter.RedBlackBoost(data)
# data = data / 255.0
# crop_cv_im = data[0]
# crop_cv_im = np.expand_dims(crop_cv_im, 0) # 扩展至四维
# output = model.predict(crop_cv_im)
# print(str(index_circle + 1) + " <crop> CNN: ", output.argmax())
# chess_int.append(output.argmax())
# data = [] # clear
data = np.array(data)
print("board data shape = ", data.shape, " ===============================")
print("chess_x.size = ", len(chess_x))
print("chess_y.size = ", len(chess_y))
# del model
# return chess_x, chess_y # only one picture!!!
def generate_board_message(count):
print("Chess Detection Count = ", count)
oriImg, cnt = read_origin_image()
hough_circle(oriImg, cnt)
# use for debug
def main():
generate_board_message(1)
# 调用函数
if __name__ == '__main__':
main()