for fname in glob.glob("/home/ggdhines/Databases/old_weather/aligned_images/Bear/1940/*.JPG")[:40]:
fname = "/home/ggdhines/Databases/old_weather/aligned_images/Bear/1940/Bear-AG-29-1940-0009.JPG"
img = active_weather.__extract_region__(fname)
id_ = fname.split("/")[-1][:-4]
print(id_)
# set a baseline for performance with otsu's binarization
mask = active_weather.__create_mask__(img)
horizontal_grid, vertical_grid = active_weather.__cell_boundaries__(img)
pca_image, threshold, inverted = active_weather.__pca__(img, active_weather.__otsu_bin__)
masked_image = active_weather.__mask_lines__(pca_image, mask)
# plt.imshow(masked_image)
# plt.show()
im2, contours, hierarchy = cv2.findContours(masked_image.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for cnt,h in zip(contours,hierarchy[0]):
if h[0] == -1:
continue
leftmost = tuple(cnt[cnt[:, :, 0].argmin()][0])[0]
rightmost = tuple(cnt[cnt[:, :, 0].argmax()][0])[0]
topmost = tuple(cnt[cnt[:, :, 1].argmax()][0])[1]
bottommost = tuple(cnt[cnt[:, :, 1].argmin()][0])[1]
perimeter = cv2.arcLength(cnt, True)
for fname in glob.glob(
"/home/ggdhines/Databases/old_weather/aligned_images/Bear/1940/*.JPG"
)[:40]:
fname = "/home/ggdhines/Databases/old_weather/aligned_images/Bear/1940/Bear-AG-29-1940-0005.JPG"
img = __extract_region__(fname)
id_ = fname.split("/")[-1][:-4]
print(id_)
# set a baseline for performance with otsu's binarization
horizontal_grid, vertical_grid = __cell_boundaries__(img)
thres_alg = __binary_threshold_curry__(60)
mask = __create_mask__(img)
pca_image, threshold, inverted = __pca__(img, thres_alg)
masked_image = __mask_lines__(pca_image, mask)
plt.imshow(masked_image, cmap="gray")
plt.show()
for h_index in range(len(horizontal_grid) - 1):
row = masked_image[horizontal_grid[h_index]:horizontal_grid[h_index +
1], :]
for v_index in range(len(vertical_grid) - 1):
cell = row[:, vertical_grid[v_index]:vertical_grid[v_index + 1]]
if len(np.where(cell < 255)[0]) < 30:
continue
x_pts = np.where(cell < 255)[1]
import numpy as np
for fname in glob.glob("/home/ggdhines/Databases/old_weather/aligned_images/Bear/1940/*.JPG")[:40]:
fname = "/home/ggdhines/Databases/old_weather/aligned_images/Bear/1940/Bear-AG-29-1940-0005.JPG"
img = __extract_region__(fname)
id_ = fname.split("/")[-1][:-4]
print(id_)
# set a baseline for performance with otsu's binarization
horizontal_grid,vertical_grid = __cell_boundaries__(img)
thres_alg = __binary_threshold_curry__(60)
mask = __create_mask__(img)
pca_image,threshold,inverted = __pca__(img,thres_alg)
masked_image = __mask_lines__(pca_image,mask)
plt.imshow(masked_image,cmap="gray")
plt.show()
for h_index in range(len(horizontal_grid)-1):
row = masked_image[horizontal_grid[h_index]:horizontal_grid[h_index+1],:]
for v_index in range(len(vertical_grid)-1):
cell = row[:,vertical_grid[v_index]:vertical_grid[v_index+1]]
if len(np.where(cell<255)[0]) < 30:
continue
x_pts = np.where(cell<255)[1]
# plt.imshow(masked_image[:,vertical_grid[v_index]:vertical_grid[v_index+1]],cmap="gray")
def __run__(table, mask):
masked_image = __mask_lines__(table, mask)
transcriptions = __ocr_image__(masked_image)
return transcriptions
for fname in glob.glob(
"/home/ggdhines/Databases/old_weather/aligned_images/Bear/1940/*.JPG"
)[:40]:
fname = "/home/ggdhines/Databases/old_weather/aligned_images/Bear/1940/Bear-AG-29-1940-0009.JPG"
img = active_weather.__extract_region__(fname)
id_ = fname.split("/")[-1][:-4]
print(id_)
# set a baseline for performance with otsu's binarization
mask = active_weather.__create_mask__(img)
horizontal_grid, vertical_grid = active_weather.__cell_boundaries__(img)
pca_image, threshold, inverted = active_weather.__pca__(
img, active_weather.__otsu_bin__)
masked_image = active_weather.__mask_lines__(pca_image, mask)
# plt.imshow(masked_image)
# plt.show()
im2, contours, hierarchy = cv2.findContours(masked_image.copy(),
cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
for cnt, h in zip(contours, hierarchy[0]):
if h[0] == -1:
continue
leftmost = tuple(cnt[cnt[:, :, 0].argmin()][0])[0]
rightmost = tuple(cnt[cnt[:, :, 0].argmax()][0])[0]
topmost = tuple(cnt[cnt[:, :, 1].argmax()][0])[1]
bottommost = tuple(cnt[cnt[:, :, 1].argmin()][0])[1]