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character_Detection.py
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character_Detection.py
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"""
Character Detection
The goal of this task is to experiment with template matching techniques. Specifically, the task is to find ALL of
the coordinates where a specific character appears using template matching.
There are 3 sub tasks:
1. Detect character 'a'.
2. Detect character 'b'.
3. Detect character 'c'.
"""
import argparse
import json
import os
import utils
from task1 import * # you could modify this line
def parse_args():
parser = argparse.ArgumentParser(description="cse 473/573 project 1.")
parser.add_argument(
"--img_path", type=str, default="./data/characters.jpg",
help="path to the image used for character detection (do not change this arg)")
parser.add_argument(
"--template_path", type=str, default="",
choices=["./data/a.jpg", "./data/b.jpg", "./data/c.jpg"],
help="path to the template image")
parser.add_argument(
"--result_saving_directory", dest="rs_directory", type=str, default="./results/",
help="directory to which results are saved (do not change this arg)")
args = parser.parse_args()
return args
def detect(img, template):
"""Detect a given character, i.e., the character in the template image.
Args:
img: nested list (int), image that contains character to be detected.
template: nested list (int), template image.
Returns:
coordinates: list (tuple), a list whose elements are coordinates where the character appears.
format of the tuple: (x (int), y (int)), x and y are integers.
x: row that the character appears (starts from 0).
y: column that the character appears (starts from 0).
"""
# Thresholding the image to make it cleaner
def thresholding(img,c):
img_threshold = copy.deepcopy(img)
for i in range(len(img_threshold)):
for j in range(len(img_threshold[0])):
if(img[i][j]>c):
img_threshold[i][j] = 255
else:
img_threshold[i][j] = 0
return img_threshold
# resize template
def resize(img, dim):
img = np.array(img).astype('float32')
resized = cv2.resize(img, dim)
return resized
# Normalized cross correlation
def correlation_coef(a, b):
prod = np.mean((a - a.mean()) * (b - b.mean()))
stds = a.std() * b.std()
if stds == 0:
return 0
else:
return prod/stds
def norm_cross_cor(image,template):
image=np.array(image)
l1,b1=np.array(template).shape
l2,b2=np.array(image).shape
ncc=np.zeros((l2-l1+1,b2-b1+1)).tolist()
for i in range(l2-l1+1):
for j in range(b2-b1+1):
img= image[i:i+l1,j:j+b1]
ncc[i][j]=correlation_coef(img,np.array(template))
return ncc
# Drop points for the same character coming more than once
def drop_same_points(m):
store=[]
for idx in m:
p,q=idx[0],idx[1]
if (p-1,q) in store or (p+1,q) in store or (p,q-1) in store or (p,q+1) in store or (p+1,q+1) in store or (p-1,q-1) in store or (p+1,q-1) in store or (p-1,q+1) in store:
pass
else:
store+=([idx])
return store
# Threshold for selection
def find_max(arr,c):
m=[]
(l,b)=arr.shape
for i in range(l):
for j in range(b):
if arr[i][j]>=c:
#m+=[(i,j)]
m+=[(i,j)]
return(m)
def template_match(img,template):
dim=(14, 14)
resized_template = resize(template,dim)
threshold_img = thresholding(resized_template,150)
matches=[]
img_ncc=norm_cross_cor(img,threshold_img)
img_ncc_np=np.array(img_ncc)
matches += find_max(img_ncc_np,0.85)
dim=(11, 11)
resized_template = resize(template,dim)
threshold_img = thresholding(resized_template,170)
img_ncc=norm_cross_cor(img,threshold_img)
img_ncc_np=np.array(img_ncc)
matches += find_max(img_ncc_np,0.67)
if(len(matches)==1):
matches += find_max(img_ncc_np,0.54)
return drop_same_points(matches)
# smoothen the image
gaussian=np.matmul(np.array([[1],[2],[1]]),np.array([[1,2,1]]))
gauss_image=convolve2d(img,gaussian)
coordinates = template_match(gauss_image,template)
print(coordinates)
#raise NotImplementedError
return coordinates
def save_results(coordinates, template, template_name, rs_directory):
results = {}
results["coordinates"] = sorted(coordinates, key=lambda x: x[0])
results["templat_size"] = (len(template), len(template[0]))
with open(os.path.join(rs_directory, template_name), "w") as file:
json.dump(results, file)
def main():
args = parse_args()
img = read_image(args.img_path)
template = read_image(args.template_path)
coordinates = detect(img, template)
template_name = "{}.json".format(os.path.splitext(os.path.split(args.template_path)[1])[0])
save_results(coordinates, template, template_name, args.rs_directory)
if __name__ == "__main__":
main()