adding cap on output image res

app.py

@@ -1,12 +1,13 @@
 import gradio as gr
 from models import yolo
-from cv2 import  COLOR_BGR2RGB, imread, cvtColor
+from cv2 import  COLOR_BGR2RGB, imread, cvtColor, resize
 import numpy as np
 import pandas as pd
 
 
 
-
+MAX_H = 1000
+MAX_W = 1000
 with open("eggs.names") as f:
     classes = f.read().split("\n")
 
@@ -51,7 +52,20 @@ def classify(img_path, h_tiles, v_tiles):
         #add the width of this split to the horizontal offset
         h_offset += h_split.shape[0]
     detections = pd.DataFrame(detections, columns = ["x", "y", "h", "w", "confidence", "class", "class id"])
-    return cvtColor(output, COLOR_BGR2RGB), detections
+    
+    output = cvtColor(output, COLOR_BGR2RGB)
+    x,y = output.shape[0], output.shape[1]
+    
+    if x > y and x > MAX_W:
+        new_x = MAX_W/x
+        new_y = (y/x)*new_x
+    elif y > MAX_H:
+        new_y = MAX_H/y
+        new_x = (x/y)*new_y
+    else:
+        new_x = 1
+        new_y = 1
+    return  resize(output, fx = new_x, fy = new_y, dsize = None), detections
         
 description = """
     Demonstration of kelp sporophyte object detection network. 
@@ -67,7 +81,7 @@ description = """
 iface = gr.Interface(fn = classify,
     inputs = [gr.Image(type="filepath"), gr.Slider(minimum=1, maximum=10, value=1, step=1), 
         gr.Slider(minimum=1, maximum=10, value=1, step=1)],
-    outputs = [gr.Image(invert_colors=False), gr.DataFrame()],
+    outputs = [gr.Image(), gr.DataFrame()],
     examples = [["ex1.jpg", 1, 1],
         ["ex2.jpg", 1, 1],
         ["ex3.jpg", 1, 1],