This film is an upgrade for the screenshots of parts. For the specific effect, please jump to the previous article. This film only highlights the upgraded part.
The previous article was expanded according to a fixed ratio, the upper and lower ratios, and the left and right ratios. Here is an upgrade to the ratio, with a ratio of up, down, left, and right, and a random ratio (don't let the target of each picture be in the middle of the picture).
'''
cut 4 part in [head, body, hand, foot]
generate small pic of some rate extend
and generate corresponding json
Update points from the No.1 generation:the rate of extend of foot & hand is random,
and the target not in center,also random
'''
from copy import deepcopy
import cv2
import json
import os
import random
img_path = "/data/weardata/images"
json_path = "/data/weardata/json"
save_path = "/data/weardata/save"
head_save = os.path.join(save_path, "head")
body_save = os.path.join(save_path, "body")
hand_save = os.path.join(save_path, "hand")
foot_save = os.path.join(save_path, "foot")
# 4部件分类
head_label = ["head", "hat", "workhat", "helmet"]
body_label = ["blueworkclothes", "cloth", "refvest", "apron", "whiteworkclothes"]
hand_label = ["glove", "inglove", "hand"]
foot_label = ["shoes", "inshoes", "noshoes"]
save_path = [head_save, body_save, hand_save, foot_save]
for s_p in save_path:
if not os.path.exists(s_p):
os.mkdir(s_p)
def random_cut():
total_random = random.uniform(1, 3)
top_random = random.uniform(0, total_random)
left_random = random.uniform(0, total_random)
bottom_random = total_random - top_random
right_random = total_random - left_random
return [top_random, bottom_random, left_random, right_random]
def cut_part(json_data, part_label, save_path, img_file, img_h, img_w, l_ran, r_ran, t_ran, b_ran):
i = 0
for shape in json_data["shapes"]:
json_data_1 = deepcopy(json_data)
if shape["label"] in part_label:
img_save = os.path.join(save_path, os.path.splitext(os.path.split(img_file)[-1])[0] + ".jpg")
json_save = save_path + "/" + file
if os.path.exists(json_save):
json_save = save_path + "/" + str(i) + file
img_save = save_path + "/" + str(i) + os.path.splitext(os.path.split(img_file)[-1])[0] + ".jpg"
json_data_1["imagePath"] = str(i) + os.path.splitext(os.path.split(img_file)[-1])[0] + ".jpg"
i += 1
p = shape["points"]
x_l = int(abs(p[1][0] - p[0][0]) * l_ran)
x_r = int(abs(p[1][0] - p[0][0]) * r_ran)
y_t = int(abs(p[1][1] - p[0][1]) * t_ran)
y_b = int(abs(p[1][1] - p[0][1]) * b_ran)
x1 = int(min(p[0][0], p[1][0])) - x_l
y1 = int(min(p[0][1], p[1][1])) - y_t
x2 = int(max(p[0][0], p[1][0])) + x_r
y2 = int(max(p[0][1], p[1][1])) + y_b
if x1 < 0:
x1 = 0
if y1 < 0:
y1 = 0
if x2 > img_w:
x2 = img_w
if y2 > img_h:
y2 = img_h
inpart_label = []
for shape1 in json_data_1["shapes"]:
m_p = shape1["points"]
m_x1 = int(min(m_p[0][0], m_p[1][0]))
m_y1 = int(min(m_p[0][1], m_p[1][1]))
m_x2 = int(max(m_p[0][0], m_p[1][0]))
m_y2 = int(max(m_p[0][1], m_p[1][1]))
m_p[0][0] = m_x1
m_p[0][1] = m_y1
m_p[1][0] = m_x2
m_p[1][1] = m_y2
if shape1["label"] == "other" and ((x1 < m_x1 < x2 or x1 < m_x2 < x2)\
and (y1 < m_y1 < y2 or y1 < m_y2 < y2)):
inpart_label.append(shape1)
if x1 < (m_x1 + m_x2)/2 < x2 and y1 < (m_y1 + m_y2)/2 <y2:
if part_label == hand_label:
if shape1["label"] == "phone" or shape1["label"] in hand_label:
inpart_label.append(shape1)
else:
inpart_label.append(shape1)
else:
continue
# print(m_x1, m_y1, m_x2, m_y2)
img = cv2.imread(img_file)
try:
img = img[y1:y2, x1:x2, :]
except TypeError:
print(img_file, shape["label"])
input()
json_data_1["shapes"] = []
for p_label in inpart_label:
m_p = p_label["points"]
if p_label["shape_type"] == "polygon":
print("-------",p_label)
p_label["shape_type"] = "rectangle"
x_p, y_p= [], []
for p in m_p:
x_p.append(p[0])
y_p.append(p[1])
m_p = []
m_p = [[0,0], [0,0]]
m_p[0][0], m_p[0][1], m_p[1][0], m_p[1][1] = min(x_p), min(y_p), max(x_p), max(y_p)
p_label["points"] = m_p
print(m_p)
print(p_label["points"])
m_p[0][0] = m_p[0][0] - x1
m_p[0][1] = m_p[0][1] - y1
m_p[1][0] = m_p[1][0] - x1
m_p[1][1] = m_p[1][1] - y1
if m_p[0][0] < 0:
m_p[0][0] = 0
if m_p[0][1] < 0:
m_p[0][1] = 0
if m_p[1][0] > x2 - x1:
m_p[1][0] = x2 - x1
if m_p[1][1] > y2 - y1:
m_p[1][1] = y2 - y1
json_data_1["shapes"].append(p_label)
json_data_1["imageHeight"] = y2 - y1
json_data_1["imageWidth"] = x2 -x1
json_data_1['imageData'] = None
json.dump(json_data_1, open(json_save, "w"), ensure_ascii=False, indent=2)
cv2.imwrite(img_save, img)
print(img_file)
files = os.listdir(json_path)
for file in files:
if os.path.splitext(file)[-1] != ".json":
continue
img_file = os.path.join(img_path, file.split(".json")[0] + ".jpg")
if not os.path.exists(img_file):
img_file = os.path.join(img_path, file.split(".json")[0] + ".png")
if not os.path.exists(img_file):
img_file = os.path.join(img_path, file.split(".json")[0] + ".jpeg")
json_file = os.path.join(json_path, file)
json_data = json.load(open(json_file))
img_h = json_data["imageHeight"]
img_w = json_data["imageWidth"]
cut_part(json_data, head_label, head_save, img_file, img_h, img_w, 0.3, 0.3, 0.2, 0.2) # head
cut_part(json_data, body_label, body_save, img_file, img_h, img_w, 0.25, 0.25, 0.05, 0.05) # body
[t_ran, b_ran, l_ran, r_ran] = random_cut()
cut_part(json_data, hand_label, hand_save, img_file, img_h, img_w, l_ran, r_ran, t_ran, b_ran) # hand
[t_ran, b_ran, l_ran, r_ran] = random_cut()
cut_part(json_data, foot_label, foot_save, img_file, img_h, img_w, l_ran, r_ran, t_ran, b_ran) # foot
Here, because the parts in the head and body areas are relatively large, in order not to make them too large, the size of the screenshots of the four parts should not differ too much, so the proportion is still fixed, and the hand and foot areas are small and use random proportions (with upper and lower limits).
Look at the screenshots of several hands and feet:
