def main(filename):
# Initialise images
global img_list, ALL_KP, ALL_BB
img_list, fps = GetImageList(filename, crop=0.6)
L = len(img_list)
ALL_KP = [None] * L
ALL_BB = [None] * L
# Create TEMP folder
if not os.path.isdir(TEMP_FOLDER): os.mkdir(TEMP_FOLDER)
# Analyse images
t = time.time()
img_list_chunks = chunks(list(range(L)), L // N_THREADS)
threads = []
for i, chunk in enumerate(img_list_chunks):
threads.append(Thread(target=Process_Img_List, args=(chunk, i)))
for thread in threads:
thread.start()
for thread in threads:
thread.join()
print("\n[+] Done!")
print("Time taken: {}s".format(time.time() - t))
t = time.time()
# Saving images
print("\n[*] Saving images")
count = 0
for img, kp_list, box_list in zip(img_list, ALL_KP, ALL_BB):
plot_hand.plot_img(img,
kp_list,
box_list,
save=TEMP_FOLDER + r"/{}.png".format(count))
count += 1
print("[*] Saving image {0}/{1} \r".format(count, L), end="")
print("\n[+] Done!")
print("Time taken: {}s".format(time.time() - t))
# Creating GIF
print("[*] Saving to GIF")
images_save = []
for i in range(L):
images_save.append(
Image.fromarray(imageio.imread(TEMP_FOLDER +
r"/{}.png".format(i))))
gif_save = images_save[0]
gif_save.info['duration'] = 1. / fps
gif_save.save('test2.gif',
save_all=True,
append_images=images_save[1:],
loop=0)
shutil.rmtree(TEMP_FOLDER)
print("[+] Done!")
def process():
"""
Prepares data for CNN training.
:return: Outputs csv file of bounding box coordinates for each frame.
"""
import csv
import plot_hand
f = open('hands.csv', 'w', newline='')
csvwriter = csv.writer(f, delimiter=',')
img_path = "C:/Users/alchu/Pictures/Camera Roll/" # CHANGE
imglist = [x for x in os.listdir(img_path) if x.endswith(('.png', '.jpg'))]
#imglist = ['x_Albert.jpg','x_Albert2.jpg','x_Albert3.jpg']
#---------------Don't edit from this point on------------------
for idx, filename in enumerate(imglist):
img = Image.open(img_path + filename)
img = np.array(img)
dims = np.shape(img)
# Get predictions
try:
kp_list, box_list = detector(img)
except TypeError:
print(filename)
continue
if kp_list[0] is None:
print(filename + ", keypoints is None.")
continue
# Get bounding box
x = []
y = []
for kp in kp_list[0]: # first set of keypoints
x.append(kp[0])
y.append(kp[1])
minx = max(min(x) - padding, 0)
miny = max(min(y) - padding, 0)
maxx = max(x) + padding
maxy = max(y) + padding
width = maxx - minx
height = maxy - miny
l = max(width, height) # square bounding box
minx = max(minx - ((l - width) / 2), 0)
maxx = minx + l
assert maxx < dims[1]
miny = max(miny - ((l - height) / 2), 0)
maxy = miny + l
assert maxy < dims[0]
bbox = []
bbox.append([minx, miny])
bbox.append([maxx, miny])
bbox.append([maxx, maxy])
bbox.append([minx, maxy])
bbox = [np.array(bbox), None]
#print(kp_list)
#print(bbox)
#print(np.shape(bbox[0]))
csvwriter.writerow([filename, bbox[0]])
# Plot predictions
try:
plot_hand.plot_img(img, kp_list, bbox, save=None)
except TypeError: # multiple sets of keypoints bug out the plot function
print(filename + " has multiple sets of keypoints.")
continue
f.close()