def bibi(self):
url = "https://passport.bilibili.com/login"
self.browser.get(url)
xpath = '//*[@id="login-username"]'
self.wait.until(EC.presence_of_element_located(
(By.XPATH, xpath))).send_keys('Python')
xpath = '//*[@id="login-passwd"]'
self.wait.until(EC.presence_of_element_located(
(By.XPATH, xpath))).send_keys('Python')
xpath = '//*[@id="geetest-wrap"]/div/div[5]/a[1]'
self.click(xpath)
xpath = '/html/body/div[2]/div[2]/div[6]/div/div/div[2]/div[1]/div/div[2]/img'
logo = self.wait.until(
EC.presence_of_element_located((By.XPATH, xpath)))
f = logo.get_attribute('src')
if f:
res = requests.get(f)
res = res.content
with open(f"bilbil.jpg", 'wb') as f:
f.write(res)
res = demo.run_click("bilbil.jpg")
plan = demo.to_selenium(res)
X, Y = logo.location['x'], logo.location['y']
print(X, Y)
lan_x = 259 / 334
lan_y = 290 / 384
for p in plan:
x, y = p['place']
ActionChains(self.browser).move_by_offset(
X - 40 + x * lan_x, Y + y * lan_y).click().perform()
ActionChains(self.browser).move_by_offset(
-(X - 40 + x * lan_x),
-(Y + y * lan_y)).perform() # 将鼠标位置恢复到移动前
time.sleep(0.5)
xpath = "/html/body/div[2]/div[2]/div[6]/div/div/div[3]/a/div"
self.click(xpath)
time.sleep(1)
try:
self.click(xpath)
with open("bilbil.jpg", 'rb') as f:
data = f.read()
with open(f"error2/{int(time.time())}.jpg", 'wb') as f:
f.write(data)
except:
self.ture += 1
demo.draw("bilbil.jpg", res)
print(res)
print(plan)
print("".join([i['text'] for i in plan]))
img = img[:, :, ::-1]
inp = tf.convert_to_tensor(img[None], tf.float32)
inp = tf.concat([inp, inp], 0)
outs = model(inp, training=False)
num = outs["valid_detections"].numpy()[1]
boxes = outs["nmsed_boxes"].numpy()[1]
scores = outs["nmsed_scores"].numpy()[1]
classes = outs["nmsed_classes"].numpy()[1]
for i in range(num):
box = boxes[i]
# if scores[i] < 0.5:
# continue
# box = boxes[i] * np.array([height, width, height, width])
c = classes[i] + 1
print(box, c)
img = draw(img, box, c, scores[i], coco_id_mapping,
random_color(int(c)))
cv2.imshow("img", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
tf.saved_model.save(
model.detector,
"/home/bail/Data/data2/pretrained_weights/%s" % torch_weight_name)
model.save_weights("/home/bail/Data/data2/pretrained_weights/%s.h5" %
torch_weight_name)
