Update model_2.py

model_2.py

@@ -1,11 +1,10 @@
+# model_1.py
 import os
 import cv2
 import numpy as np
 import importlib.util
-import gradio as gr
 from PIL import Image
 
-# Load the TensorFlow Lite model
 MODEL_DIR = 'model_2'
 GRAPH_NAME = 'detect.tflite'
 LABELMAP_NAME = 'labelmap.txt'
@@ -13,22 +12,18 @@ LABELMAP_NAME = 'labelmap.txt'
 pkg = importlib.util.find_spec('tflite_runtime')
 if pkg:
     from tflite_runtime.interpreter import Interpreter
-    from tflite_runtime.interpreter import load_delegate
 else:
     from tensorflow.lite.python.interpreter import Interpreter
-    from tensorflow.lite.python.interpreter import load_delegate
 
 PATH_TO_CKPT = os.path.join(MODEL_DIR, GRAPH_NAME)
 PATH_TO_LABELS = os.path.join(MODEL_DIR, LABELMAP_NAME)
 
-# Load the label map
 with open(PATH_TO_LABELS, 'r') as f:
     labels = [line.strip() for line in f.readlines()]
 
 if labels[0] == '???':
     del(labels[0])
 
-# Load the TensorFlow Lite model
 interpreter = Interpreter(model_path=PATH_TO_CKPT)
 interpreter.allocate_tensors()
 
@@ -42,12 +37,12 @@ input_mean = 127.5
 input_std = 127.5
 
 outname = output_details[0]['name']
-if ('StatefulPartitionedCall' in outname):
+if 'StatefulPartitionedCall' in outname:
     boxes_idx, classes_idx, scores_idx = 1, 3, 0
 else:
     boxes_idx, classes_idx, scores_idx = 0, 1, 2
 
-def perform_detection(image, interpreter, labels):
+def perform_detection(image):
     imH, imW, _ = image.shape
     image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     image_resized = cv2.resize(image_rgb, (width, height))
@@ -65,12 +60,12 @@ def perform_detection(image, interpreter, labels):
 
     detections = []
     for i in range(len(scores)):
-        if ((scores[i] > 0.5) and (scores[i] <= 1.0)):
+        if scores[i] > 0.5:
             ymin = int(max(1, (boxes[i][0] * imH)))
             xmin = int(max(1, (boxes[i][1] * imW)))
             ymax = int(min(imH, (boxes[i][2] * imH)))
             xmax = int(min(imW, (boxes[i][3] * imW)))
-            
+
             cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (10, 255, 0), 2)
             object_name = labels[int(classes[i])]
             label = '%s: %d%%' % (object_name, int(scores[i] * 100))
@@ -82,13 +77,9 @@ def perform_detection(image, interpreter, labels):
             detections.append([object_name, scores[i], xmin, ymin, xmax, ymax])
     return image
 
-def resize_image(image, size=640):
-    return cv2.resize(image, (size, size))
-
 def detect_image(input_image):
     image = np.array(input_image)
-    resized_image = resize_image(image, size=640)  # Resize input image
-    result_image = perform_detection(resized_image, interpreter, labels)
+    result_image = perform_detection(image)
     return Image.fromarray(result_image)
 
 def detect_video(input_video):
@@ -100,8 +91,7 @@ def detect_video(input_video):
         if not ret:
             break
 
-        resized_frame = resize_image(frame, size=640)  # Resize each frame
-        result_frame = perform_detection(resized_frame, interpreter, labels)
+        result_frame = perform_detection(frame)
         frames.append(result_frame)
 
     cap.release()
@@ -120,20 +110,3 @@ def detect_video(input_video):
     out.release()
 
     return output_video_path
-
-app = gr.Blocks()
-
-with app:
-    with gr.Tab("Image Detection"):
-        gr.Markdown("Upload an image for object detection")
-        image_input = gr.Image(type="pil", label="Upload an image")
-        image_output = gr.Image(type="pil", label="Detection Result")
-        gr.Button("Submit").click(fn=detect_image, inputs=image_input, outputs=image_output)
-    
-    with gr.Tab("Video Detection"):
-        gr.Markdown("Upload a video for object detection")
-        video_input = gr.Video(label="Upload a video")
-        video_output = gr.Video(label="Detection Result")
-        gr.Button("Submit").click(fn=detect_video, inputs=video_input, outputs=video_output)
-
-app.launch()