def crop_from_one_img(img, horizontal_splits, overlap_px, scale, show=False, save_crops=True, folder_name='', frame_name=''):
width, height = img.size
if show:
fig, ax = plt.subplots()
plt.imshow(img)
plt.xlim(-1 * (width / 10.0), width + 1 * (width / 10.0))
plt.ylim(-1 * (height / 10.0), height + 1 * (height / 10.0))
plt.gca().invert_yaxis()
column_list, row_list = best_squares_overlap(width,height,horizontal_splits,overlap_px)
crop = column_list[0][1] - column_list[0][0]
w_crops = column_list
h_crops = row_list
#print("after w",w_crops)
#print("after h",h_crops)
N = len(w_crops) * len(h_crops)
crops = []
i = 0
for w_crop in w_crops:
for h_crop in h_crops:
if show:
jitter = random.uniform(0, 1) * 15
ax.add_patch(
patches.Rectangle(
(w_crop[0] + jitter, h_crop[0] + jitter),
scale * crop,
scale * crop, fill=False, linewidth=2.0, color=np.random.rand(3, 1) # color=cmap(i)
)
)
area = (int(w_crop[0]), int(h_crop[0]), int(w_crop[0] + scale * crop), int(h_crop[0] + scale * crop))
cropped_img = img.crop(box=area)
cropped_img = cropped_img.resize((crop, crop), resample=Image.ANTIALIAS)
cropped_img.load()
if save_crops:
file_name = frame_name + str(i).zfill(4) + ".jpg"
if not os.path.exists(folder_name + frame_name + "/"):
os.makedirs(folder_name + frame_name + "/")
cropped_img.save(folder_name + file_name)
crops.append((file_name, area))
else:
crops.append((None,area))
i += 1
if show:
plt.show()
return crops, crop
def get_number_of_crops_from_frame(frame_path, horizontal_splits,overlap_px):
# crop*scale is the size inside input image
# crop is the size of output image
img = Image.open(frame_path)
width, height = img.size
column_list, row_list = best_squares_overlap(width,height,horizontal_splits,overlap_px)
w_crops = column_list
h_crops = row_list
N = len(w_crops) * len(h_crops)
return N
def crop_from_one_frame(frame_path, out_folder, horizontal_splits,overlap_px, show, save_crops=True, save_visualization=True, viz_path=''):
# crop*scale is the size inside input image
# crop is the size of output image
frame_name = os.path.basename(frame_path)
frame_name = frame_name[0:-4]
if save_crops:
if not os.path.exists(out_folder+frame_name+"/"):
os.makedirs(out_folder+frame_name+"/")
img = Image.open(frame_path)
width, height = img.size
if show or save_visualization:
fig, ax = plt.subplots()
plt.imshow(img)
plt.xlim(-1 * (width / 10.0), width + 1 * (width / 10.0))
plt.ylim(-1 * (height / 10.0), height + 1 * (height / 10.0))
plt.gca().invert_yaxis()
#w_crops = get_crops_parameters(width, crop, over, scale)
#h_crops = get_crops_parameters(height, crop, over, scale)
column_list, row_list = best_squares_overlap(width,height,horizontal_splits,overlap_px)
crop = column_list[0][1] - column_list[0][0]
w_crops = column_list
h_crops = row_list
N = len(w_crops) * len(h_crops)
if not save_visualization:
print(str((frame_name))+", ", end='', flush=True)
else:
print(str((frame_name)) + " ("+str(N)+" crops per frame), ", end='', flush=True)
#print ("Number of crops:", N)
crops = []
i = 0
for w_crop in w_crops:
for h_crop in h_crops:
if show or save_visualization:
jitter = random.uniform(0, 1) * 15
ax.add_patch(
patches.Rectangle(
(w_crop[0] + jitter, h_crop[0] + jitter),
crop,
crop, fill=False, linewidth=2.0, color=np.random.rand(3, 1) # color=cmap(i)
)
)
area = (int(w_crop[0]), int(h_crop[0]), int(w_crop[0] + crop), int(h_crop[0] + crop))
cropped_img = img.crop(box=area)
cropped_img = cropped_img.resize((crop, crop), resample=Image.ANTIALIAS)
cropped_img.load()
file_name = frame_name + "/" + str(i).zfill(4) + ".jpg"
if save_crops:
cropped_img.save(out_folder + file_name)
crops.append((file_name, area))
else:
crops.append((None, area))
i += 1
if show:
plt.show()
if save_visualization:
plt.savefig(viz_path+'crops_viz.png')
cropped_img.save(viz_path + '_sample_first_crop.jpg')
return crops, crop
def crop_from_one_frame_WITH_MASK_in_mem(img, mask, frame_path, out_folder, horizontal_splits, overlap_px, mask_over, show, save_crops=True, save_visualization=True, viz_path=''):
# V3 - mask carried in memory
# crop*scale is the size inside input image
# crop is the size of output image
frame_name = os.path.basename(frame_path)
frame_name = frame_name[0:-4]
if save_crops:
if not os.path.exists(out_folder+frame_name+"/"):
os.makedirs(out_folder+frame_name+"/")
if mask.mode is not "L":
mask = mask.convert("L")
width, height = img.size
if show or save_visualization:
fig, ax = plt.subplots()
plt.imshow(img)
plt.imshow(mask,alpha=0.4)
plt.xlim(-1 * (width / 10.0), width + 1 * (width / 10.0))
plt.ylim(-1 * (height / 10.0), height + 1 * (height / 10.0))
plt.gca().invert_yaxis()
#w_crops = get_crops_parameters(width, crop, over, scale)
#h_crops = get_crops_parameters(height, crop, over, scale)
column_list, row_list = best_squares_overlap(width,height,horizontal_splits,overlap_px)
crop = column_list[0][1] - column_list[0][0]
w_crops = column_list
h_crops = row_list
N = len(w_crops) * len(h_crops)
if not save_visualization:
#print(str((frame_name))+", ", end='', flush=True)
sys.stdout.write("\r" + str((frame_name)))
sys.stdout.flush()
else:
#print(str((frame_name)) + " ("+str(N)+" crops per frame), ", end='', flush=True)
sys.stdout.write("\r" + str((frame_name)) + " ("+str(N)+" crops per frame)")
sys.stdout.flush()
crops = []
i = 0
for w_crop in w_crops:
for h_crop in h_crops:
area = (int(w_crop[0]), int(h_crop[0]), int(w_crop[0] + crop), int(h_crop[0] + crop))
cropped_img = img.crop(box=area)
cropped_img = cropped_img.resize((crop, crop), resample=Image.ANTIALIAS)
cropped_img.load()
cropped_mask = mask.crop(box=area)
cropped_mask = cropped_mask.resize((crop, crop), resample=Image.ANTIALIAS)
cropped_mask.load()
# four corners
a = cropped_mask.crop(box=(0, 0, crop * (1 - mask_over), crop * (1 - mask_over)))
b = cropped_mask.crop(box=(0, crop * (mask_over), crop * (1 - mask_over), crop * (1 - mask_over) + crop * (mask_over)))
c = cropped_mask.crop(box=(crop * mask_over, 0, crop * (1 - mask_over) + crop * mask_over, crop * (1 - mask_over)))
d = cropped_mask.crop(box=(crop * mask_over, crop * mask_over, crop * (1 - mask_over) + crop * mask_over, crop * (1 - mask_over) + crop * mask_over))
corner_empty = False
for p in [a,b,c,d]:
p.load()
lum = np.sum(np.sum(p.getextrema(), 0))
#print(p.size, lum)
if lum == 0:
corner_empty = True
break
if corner_empty:
continue
extrema = cropped_mask.getextrema()
extrema_sum = np.sum(extrema,0)
#print("summed extrema", extrema_sum)
if extrema_sum == 0: # and extrema_sum[1] == 0:
continue
if show or save_visualization:
jitter = random.uniform(0, 1) * 15
ax.add_patch(
patches.Rectangle(
(w_crop[0] + jitter, h_crop[0] + jitter),
crop,
crop, fill=False, linewidth=2.0, color=np.random.rand(3, 1)[:,0] # color=cmap(i)
)
)
file_name = frame_name + "/" + str(i).zfill(4) + ".jpg"
if save_crops:
cropped_img.save(out_folder + file_name)
i += 1
crops.append((file_name, area))
if show:
plt.show()
if save_visualization:
plt.savefig(viz_path+'crops_viz.png')
cropped_img.save(viz_path + '_sample_first_crop.jpg')
return crops, crop