Add application file

app.py

@@ -2,7 +2,6 @@ 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
@@ -48,30 +47,6 @@ 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)