app deploy with multiple object

app.py

@@ -15,8 +15,8 @@ def drawAxis(img, p_, q_, color, scale):
   hypotenuse = sqrt((p[1] - q[1]) * (p[1] - q[1]) + (p[0] - q[0]) * (p[0] - q[0]))
  
   # Here we lengthen the arrow by a factor of scale
-  q[0] = p[0] - scale * hypotenuse * cos(angle)
-  q[1] = p[1] - scale * hypotenuse * sin(angle)
+  q[0] = p[0] - scale * hypotenuse/3 * cos(angle)
+  q[1] = p[1] - scale * hypotenuse/3 * sin(angle)
   cv2.line(img, (int(p[0]), int(p[1])), (int(q[0]), int(q[1])), color, 3, cv2.LINE_AA)
  
   # create the arrow hooks
@@ -69,6 +69,7 @@ def getOrientation(pts, img):
 file_urls = [
     'https://github.com/lucarei/orientation-detection-robotic-grasping/assets/22428774/cefd9731-c57c-428b-b401-fd54a8bd0a95',
     'https://github.com/lucarei/orientation-detection-robotic-grasping/assets/22428774/acbad76a-33f9-4028-b012-4ece5998c272',
+    'https://github.com/lucarei/orientation-detection-robotic-grasping/assets/22428774/ce8a0fb9-99ea-4952-bcc4-3afa023066d9',
     'https://www.dropbox.com/s/7sjfwncffg8xej2/video_7.mp4?dl=1'
 ]
 
@@ -103,26 +104,26 @@ def show_preds_image(image_path):
     dim=(width,height)
 
     outputs = model.predict(source=img_res_toshow)
-
-    #obtain BW image
-    bw=(outputs[0].masks.masks[0].cpu().numpy() * 255).astype("uint8")
-    #BW image with same dimention of initial image
-    bw=cv2.resize(bw, dim, interpolation = cv2.INTER_AREA)
-    img=img_res_toshow
-    contours, _ = cv2.findContours(bw, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
-    for i, c in enumerate(contours):
-        # Calculate the area of each contour
-        area = cv2.contourArea(c)
-        
-        # Ignore contours that are too small or too large
-        if area < 3700 or 100000 < area:
-            continue
-        
-        # Draw each contour only for visualisation purposes
-        cv2.drawContours(img, contours, i, (0, 0, 255), 2)
-        
-        # Find the orientation of each shape
-        angle_deg = getOrientation(c, img)
+    for object in range(len(outputs[0].masks.masks)):
+        #obtain BW image
+        bw=(outputs[0].masks.masks[object].cpu().numpy() * 255).astype("uint8")
+        #BW image with same dimention of initial image
+        bw=cv2.resize(bw, dim, interpolation = cv2.INTER_AREA)
+        img=img_res_toshow
+        contours, _ = cv2.findContours(bw, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
+        for i, c in enumerate(contours):
+            # Calculate the area of each contour
+            area = cv2.contourArea(c)
+            
+            # Ignore contours that are too small or too large
+            if area < 2500 or 500000 < area:
+                continue
+            
+            # Draw each contour only for visualisation purposes
+            cv2.drawContours(img, contours, i, (0, 0, 255), 2)
+            
+            # Find the orientation of each shape
+            angle_deg = getOrientation(c, img)
 
     results = outputs[0].cpu().numpy()
     for i, det in enumerate(results.boxes.xyxy):