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matching.py
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matching.py
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import cv2
import os
def fort_image_matching(url_img_name, fort_img_name, crop_area = 0):
url_img_basename = os.path.basename(url_img_name)
url_img_basename, url_img_ext = os.path.splitext(url_img_basename)
url_img = cv2.imread(url_img_name, 3)
fort_img = cv2.imread(fort_img_name, 3)
if url_img is None or fort_img is None:
return 0.0
height, width, channels = url_img.shape
if url_img_ext == '.jpg':
if width > height:
scale = float(288 / height)
else:
scale = float(288 / width)
height_f, width_f, channels_f = fort_img.shape
scale_fort = width_f / 320
url_img = cv2.resize(url_img, None, fx=scale * scale_fort, fy=scale * scale_fort,
interpolation=cv2.INTER_NEAREST)
crop_y1 = 74
crop_y2 = 246
crop_x1 = 74
crop_x2 = 144
if crop_area == 1:
crop_y1 = 74
crop_y2 = 144
crop_x1 = 134
crop_x2 = 246
crop = fort_img[int(crop_y1 * scale_fort):int(crop_y2 * scale_fort), int(crop_x1 * scale_fort):int(crop_x2 * scale_fort)]
# Calculate vector from center of fort image(x=74, y=74, width=172, height=172) to top left corner of crop of fort_img
fi_center_x = (((246 + 74) / 2) * scale_fort) - (crop_x1 * scale_fort)
fi_center_y = (((246 + 74) / 2) * scale_fort) - (crop_y1 * scale_fort)
if crop.mean() == 255 or crop.mean() == 0:
return 0.0
result = cv2.matchTemplate(url_img, crop, cv2.TM_CCOEFF_NORMED)
min_val3, max_val3, min_loc3, max_loc3 = cv2.minMaxLoc(result)
height, width, channels = url_img.shape
ui_center_x = width / 2 - max_loc3[0]
ui_center_y = height / 2 - max_loc3[1]
dif_x = abs(ui_center_x - fi_center_x)
dif_y = abs(ui_center_y - fi_center_y)
if dif_x > 5 or dif_y > 5:
return 0.0
else: # for png file
scale = float(width / (144 - 74))
height_f, width_f, channels_f = fort_img.shape
scale_fort = width_f / 320
url_img = cv2.resize(url_img, None, fx=scale * scale_fort, fy=scale * scale_fort,
interpolation=cv2.INTER_NEAREST)
crop = fort_img[int(74 * scale_fort):int(246 * scale_fort), int(74 * scale_fort):int(144 * scale_fort)]
if crop.mean() == 255 or crop.mean() == 0:
return 0.0
result = cv2.matchTemplate(url_img, crop, cv2.TM_CCOEFF_NORMED)
min_val3, max_val3, min_loc3, max_loc3 = cv2.minMaxLoc(result)
return max_val3
def fort_image_matching_imshow(url_img_name, fort_img_name, crop_area = 0):
url_img_basename = os.path.basename(url_img_name)
url_img_basename, url_img_ext = os.path.splitext(url_img_basename)
url_img = cv2.imread(url_img_name, 3)
fort_img = cv2.imread(fort_img_name, 3)
if url_img is None or fort_img is None:
return 0.0
height, width, channels = url_img.shape
if url_img_ext == '.jpg':
if width > height:
scale = float(288 / height)
else:
scale = float(288 / width)
height_f, width_f, channels_f = fort_img.shape
scale_fort = width_f / 320
url_img = cv2.resize(url_img, None, fx=scale * scale_fort, fy=scale * scale_fort,
interpolation=cv2.INTER_NEAREST)
crop_y1 = 74
crop_y2 = 246
crop_x1 = 74
crop_x2 = 144
if crop_area == 1:
crop_y1 = 74
crop_y2 = 174
crop_x1 = 134
crop_x2 = 246
crop = fort_img[int(crop_y1 * scale_fort):int(crop_y2 * scale_fort),
int(crop_x1 * scale_fort):int(crop_x2 * scale_fort)]
# Calculate vector from center of fort image(x=74, y=74, width=172, height=172) to top left corner of crop of fort_img
fi_center_x = (((246 + 74) / 2) * scale_fort) - (crop_x1 * scale_fort)
fi_center_y = (((246 + 74) / 2) * scale_fort) - (crop_y1 * scale_fort)
if crop.mean() == 255 or crop.mean() == 0:
return 0.0
result = cv2.matchTemplate(url_img, crop, cv2.TM_CCOEFF_NORMED)
min_val3, max_val3, min_loc3, max_loc3 = cv2.minMaxLoc(result)
height, width, channels = url_img.shape
ui_center_x = width / 2 - max_loc3[0]
ui_center_y = height / 2 - max_loc3[1]
dif_x = abs(ui_center_x - fi_center_x)
dif_y = abs(ui_center_y - fi_center_y)
print(dif_x, dif_y)
if dif_x > 5 or dif_y > 5:
return 0.0
else: # for png file
scale = float(width / (144 - 74))
height_f, width_f, channels_f = fort_img.shape
scale_fort = width_f / 320
url_img = cv2.resize(url_img, None, fx=scale * scale_fort, fy=scale * scale_fort,
interpolation=cv2.INTER_NEAREST)
crop = fort_img[int(74 * scale_fort):int(246 * scale_fort), int(74 * scale_fort):int(144 * scale_fort)]
if crop.mean() == 255 or crop.mean() == 0:
return 0.0
result = cv2.matchTemplate(url_img, crop, cv2.TM_CCOEFF_NORMED)
min_val3, max_val3, min_loc3, max_loc3 = cv2.minMaxLoc(result)
top_left = max_loc3
height, width, channels = crop.shape
bottom_right = (top_left[0] + width, top_left[1] + height)
cv2.rectangle(url_img, top_left, bottom_right, (0, 255, 0), 2)
cv2.rectangle(fort_img, (int(crop_x1 * scale_fort), int(crop_y1 * scale_fort)),
(int(crop_x2 * scale_fort), int(crop_y2 * scale_fort)), (0, 0, 255), 2)
cv2.imshow('matching result', url_img)
cv2.imshow('fort image', fort_img)
cv2.imshow('crop', crop)
cv2.waitKey(0)
if dif_x > 5 or dif_y > 5:
return 0.0
return max_val3
def pokemon_image_matching(pokemon_image_name, fort_img_name, is_pokemon):
pokemon_image = cv2.imread(pokemon_image_name, cv2.IMREAD_UNCHANGED)
fort_img = cv2.imread(fort_img_name, 3)
if pokemon_image is None or fort_img is None:
return 100000.0
croped = pokemon_image[0:256,0:190]
height_f, width_f, channels_f = fort_img.shape
scale = 147 / 256 * width_f / 133
scaled = cv2.resize(croped, None, fx=scale, fy=scale)
scaled_h, scaled_w, scaled_c = scaled.shape
channels = cv2.split(scaled)
if is_pokemon:
scale_crop_fort = width_f / 156
target_x = (16*scale_crop_fort)
target_y = (28*scale_crop_fort)
fort_img = fort_img[target_x-2:target_x+2+scaled_h, target_y-2:target_y+2+scaled_w]
else:
scale_crop_fort = width_f / 133
target_x = int(12*scale_crop_fort)
target_y = int(24*scale_crop_fort)
fort_img = fort_img[target_x-2:target_x+2+scaled_h, target_y-2:target_y+2+scaled_w]
scaled_no_alpth = cv2.merge([channels[0], channels[1], channels[2]])
transparent_mask = cv2.merge([channels[3], channels[3], channels[3]])
white_pixels = channels[3].sum()/255
result = cv2.matchTemplate(fort_img, scaled_no_alpth, cv2.TM_SQDIFF, mask=transparent_mask)
min_val3, max_val3, min_loc3, max_loc3 = cv2.minMaxLoc(result)
min_val3 = min_val3 / white_pixels
return min_val3