Updated app.py with video file upload and pose detection

app.py

@@ -1,6 +1,7 @@
 import streamlit as st
 import cv2 as cv
 import numpy as np
+import tempfile
 
 # Paths to your files
 model_path = "graph_opt.pb"
@@ -79,16 +80,18 @@ def poseDetector(frame, overlay_img):
 
             length = int(np.sqrt((points[idTo][0] - points[idFrom][0])**2 + (points[idTo][1] - points[idFrom][1])**2))
             reduced_length = int(length * 0.5)
-            overlay_resized = cv.resize(overlay_img, (reduced_length, int(overlay_img.shape[0] * 0.5)))
 
-            M = cv.getRotationMatrix2D((overlay_resized.shape[1] / 2, overlay_resized.shape[0] / 2), adjusted_angle, 1)
-            overlay_rotated = cv.warpAffine(overlay_resized, M, (overlay_resized.shape[1], overlay_resized.shape[0]), flags=cv.INTER_LINEAR, borderMode=cv.BORDER_CONSTANT, borderValue=(0, 0, 0, 0))
+            if reduced_length > 0 and overlay_img.shape[0] * 0.5 > 0:
+                overlay_resized = cv.resize(overlay_img, (reduced_length, int(overlay_img.shape[0] * 0.5)))
 
-            wrist_position = points[idTo]
-            position = (int(wrist_position[0] - overlay_rotated.shape[1] / 2), int(wrist_position[1] - overlay_rotated.shape[0] / 2))
+                M = cv.getRotationMatrix2D((overlay_resized.shape[1] / 2, overlay_resized.shape[0] / 2), adjusted_angle, 1)
+                overlay_rotated = cv.warpAffine(overlay_resized, M, (overlay_resized.shape[1], overlay_resized.shape[0]), flags=cv.INTER_LINEAR, borderMode=cv.BORDER_CONSTANT, borderValue=(0, 0, 0, 0))
 
-            alpha_mask = overlay_rotated[:, :, 3] / 255.0
-            overlay_image_alpha(frame, overlay_rotated[:, :, :3], position, alpha_mask)
+                wrist_position = points[idTo]
+                position = (int(wrist_position[0] - overlay_rotated.shape[1] / 2), int(wrist_position[1] - overlay_rotated.shape[0] / 2))
+
+                alpha_mask = overlay_rotated[:, :, 3] / 255.0
+                overlay_image_alpha(frame, overlay_rotated[:, :, :3], position, alpha_mask)
 
     t, _ = net.getPerfProfile()
     return frame