test single adapter

app_haircolor_inpaint_adapter_15.py

@@ -3,6 +3,7 @@ import gradio as gr
 import time
 import torch
 import numpy as np
+import cv2
 
 from PIL import Image
 from segment_utils import(
@@ -91,12 +92,13 @@ def image_to_image(
     run_task_time = 0
     time_cost_str = ''
     run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
-    canny_image = canny_detector(input_image, int(generate_size*1), generate_size)
+    # canny_image = canny_detector(input_image, int(generate_size*1), generate_size)
+    canny_image = custom_canny_detector(input_image)
     # lineart_image = lineart_detector(input_image, int(generate_size*1), generate_size)
     # run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
     pidiNet_image = pidiNet_detector(input_image, int(generate_size*1), generate_size)
-    cond_image = [canny_image, pidiNet_image]
-    cond_scale = [cond_scale1, cond_scale2]
+    # cond_image = [canny_image, pidiNet_image]
+    # cond_scale = [cond_scale1, cond_scale2]
     
     cond_image = canny_image
     cond_scale = cond_scale1
@@ -130,6 +132,16 @@ def make_inpaint_condition(image, image_mask):
     image = torch.from_numpy(image)
     return image
 
+def custom_canny_detector(image):
+    image = np.array(image)
+
+    low_threshold = 100
+    high_threshold = 200
+
+    image = cv2.Canny(image, low_threshold, high_threshold)
+    image = Image.fromarray(image)
+    return image
+
 def get_time_cost(run_task_time, time_cost_str):
     now_time = int(time.time()*1000)
     if run_task_time == 0: