call api to handle tryon

app.py

@@ -1,8 +1,11 @@
-import gradio as gr
+import os
+
 import cv2
-import numpy as np
+import gradio as gr
 import mediapipe as mp
-import os
+import numpy as np
+from PIL import Image
+from gradio_client import Client, handle_file
 
 example_path = os.path.join(os.path.dirname(__file__), 'example')
 
@@ -55,12 +58,78 @@ def detect_pose(image):
     return image
 
 
-def process_image(human_img):
-    # Convert PIL image to NumPy array
-    human_img = np.array(human_img)
+def align_clothing(body_img, clothing_img):
+    image_rgb = cv2.cvtColor(body_img, cv2.COLOR_BGR2RGB)
+    result = pose.process(image_rgb)
+    output = body_img.copy()
 
-    processed_image = detect_pose(human_img)
-    return processed_image
+    if result.pose_landmarks:
+        h, w, _ = output.shape
+
+        # Extract key points
+        def get_point(landmark_id):
+            lm = result.pose_landmarks.landmark[landmark_id]
+            return int(lm.x * w), int(lm.y * h)
+
+        left_shoulder = get_point(mp_pose_landmark.LEFT_SHOULDER)
+        right_shoulder = get_point(mp_pose_landmark.RIGHT_SHOULDER)
+        left_hip = get_point(mp_pose_landmark.LEFT_HIP)
+        right_hip = get_point(mp_pose_landmark.RIGHT_HIP)
+
+        # Destination box (torso region)
+        dst_pts = np.array([
+            left_shoulder,
+            right_shoulder,
+            right_hip,
+            left_hip
+        ], dtype=np.float32)
+
+        # Source box (clothing image corners)
+        src_h, src_w = clothing_img.shape[:2]
+        src_pts = np.array([
+            [0, 0],
+            [src_w, 0],
+            [src_w, src_h],
+            [0, src_h]
+        ], dtype=np.float32)
+
+        # Compute perspective transform and warp
+        matrix = cv2.getPerspectiveTransform(src_pts, dst_pts)
+        warped_clothing = cv2.warpPerspective(clothing_img, matrix, (w, h), borderMode=cv2.BORDER_TRANSPARENT)
+
+        # Handle transparency
+        if clothing_img.shape[2] == 4:
+            alpha = warped_clothing[:, :, 3] / 255.0
+            for c in range(3):
+                output[:, :, c] = (1 - alpha) * output[:, :, c] + alpha * warped_clothing[:, :, c]
+        else:
+            output = cv2.addWeighted(output, 0.8, warped_clothing, 0.5, 0)
+
+    return output
+
+
+def process_image(human_img_path, garm_img_path):
+    client = Client("franciszzj/Leffa")
+
+    result = client.predict(
+        src_image_path=handle_file(human_img_path),
+        ref_image_path=handle_file(garm_img_path),
+        ref_acceleration=False,
+        step=30,
+        scale=2.5,
+        seed=42,
+        vt_model_type="viton_hd",
+        vt_garment_type="upper_body",
+        vt_repaint=False,
+        api_name="/leffa_predict_vt"
+    )
+
+    print(result)
+    generated_image_path = result[0]
+    print("generated_image_path" + generated_image_path)
+    generated_image = Image.open(generated_image_path)
+
+    return generated_image
 
 
 image_blocks = gr.Blocks().queue()
@@ -69,7 +138,7 @@ with image_blocks as demo:
     gr.HTML("<center><p>Upload an image of a person and an image of a garment ✨</p></center>")
     with gr.Row():
         with gr.Column():
-            human_img = gr.Image(type="pil", label='Human', interactive=True)
+            human_img = gr.Image(type="filepath", label='Human', interactive=True)
             example = gr.Examples(
                 inputs=human_img,
                 examples_per_page=10,
@@ -77,7 +146,7 @@ with image_blocks as demo:
             )
 
         with gr.Column():
-            garm_img = gr.Image(label="Garment", type="pil", interactive=True)
+            garm_img = gr.Image(label="Garment", type="filepath", interactive=True)
             example = gr.Examples(
                 inputs=garm_img,
                 examples_per_page=8,
@@ -89,6 +158,6 @@ with image_blocks as demo:
         try_button = gr.Button(value="Try-on", variant='primary')
 
     # Linking the button to the processing function
-    try_button.click(fn=process_image, inputs=human_img, outputs=image_out)
+    try_button.click(fn=process_image, inputs=[human_img, garm_img], outputs=image_out)
 
-image_blocks.launch()
+image_blocks.launch(show_error=True)

requirements.txt

@@ -3,4 +3,5 @@ numpy==1.26.4
 opencv-contrib-python==4.11.0.86
 opencv-python==4.11.0.86
 gradio==5.23.3
-gradio_client==1.8.0
+gradio_client==1.8.0
+