Update app.py

app.py

@@ -153,6 +153,34 @@ def convert_from_cv2_to_image(img: np.ndarray) -> Image:
 def convert_from_image_to_cv2(img: Image) -> np.ndarray:
     return cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
 
+def draw_kps(w,h, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,255,0), (255,0,255)]):
+    
+    stickwidth = 8
+    limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])
+
+    out_img = np.zeros([h, w, 3])
+
+    for i in range(len(limbSeq)):
+        index = limbSeq[i]
+        color = color_list[index[0]]
+
+        x = kps[index][:, 0]
+        y = kps[index][:, 1]
+        length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5
+        angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))
+        polygon = cv2.ellipse2Poly((int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
+        out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)
+    out_img = (out_img * 0.6).astype(np.uint8)
+
+    for idx_kp, kp in enumerate(kps):
+        color = color_list[idx_kp]
+        x, y = kp
+        out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1)
+
+    # out_img = out_img.astype(np.uint8)
+    out_img_pil = PIL.Image.fromarray(out_img.astype(np.uint8))
+    return out_img_pil
+
 def resize_img(
     input_image,
     max_side=1280,
@@ -301,10 +329,10 @@ with gr.Blocks(css=css) as demo:
             )
 
             submit = gr.Button("Submit", variant="primary")
-            enable_LCM = gr.Checkbox(
-                label="Enable Fast Inference with LCM", value=enable_lcm_arg,
-                info="LCM speeds up the inference step, the trade-off is the quality of the generated image. It performs better with portrait face images rather than distant faces",
-            )
+            # enable_LCM = gr.Checkbox(
+            #     label="Enable Fast Inference with LCM", value=enable_lcm_arg,
+            #     info="LCM speeds up the inference step, the trade-off is the quality of the generated image. It performs better with portrait face images rather than distant faces",
+            # )
 
             # strength
             controlnet_conditioning_scale = gr.Slider(
@@ -383,12 +411,12 @@ with gr.Blocks(css=css) as demo:
             outputs=[gallery, usage_tips],
         )
 
-        enable_LCM.input(
-            fn=toggle_lcm_ui,
-            inputs=[enable_LCM],
-            outputs=[num_steps, guidance_scale],
-            queue=False,
-        )
+        # enable_LCM.input(
+        #     fn=toggle_lcm_ui,
+        #     inputs=[enable_LCM],
+        #     outputs=[num_steps, guidance_scale],
+        #     queue=False,
+        # )
 
     gr.Examples(
         examples=get_example(),