Add application file

app.py

@@ -2,6 +2,7 @@ import argparse
 import requests
 import gradio as gr
 import numpy as np
+import cv2
 import torch
 import torch.nn as nn
 from PIL import Image
@@ -47,6 +48,30 @@ eval_transforms = build_transforms(224)
 '''
 borrow code from here: https://github.com/jacobgil/pytorch-grad-cam/blob/master/pytorch_grad_cam/utils/image.py
 '''
+def show_cam_on_image(img: np.ndarray,
+                      mask: np.ndarray,
+                      use_rgb: bool = False,
+                      colormap: int = cv2.COLORMAP_JET) -> np.ndarray:
+    """ This function overlays the cam mask on the image as an heatmap.
+    By default the heatmap is in BGR format.
+    :param img: The base image in RGB or BGR format.
+    :param mask: The cam mask.
+    :param use_rgb: Whether to use an RGB or BGR heatmap, this should be set to True if 'img' is in RGB format.
+    :param colormap: The OpenCV colormap to be used.
+    :returns: The default image with the cam overlay.
+    """
+    heatmap = cv2.applyColorMap(np.uint8(255 * mask), colormap)
+    if use_rgb:
+        heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)
+    heatmap = np.float32(heatmap) / 255
+
+    if np.max(img) > 1:
+        raise Exception(
+            "The input image should np.float32 in the range [0, 1]")
+
+    cam = 0.7*heatmap + 0.3*img
+    # cam = cam / np.max(cam)
+    return np.uint8(255 * cam)
 
 def recognize_image(image, texts):
     img_t = eval_transforms(image) 

requirements.txt

@@ -0,0 +1,6 @@
+torch==1.10.1
+torchvision==0.11.2
+opencv-python-headless==4.5.3.56
+timm==0.4.12
+numpy
+