Update app.py

app.py

@@ -1,73 +1,73 @@
-import gradio as gr
-from PIL import Image, ImageDraw, ImageFont
-
-
-# Use a pipeline as a high-level helper
-from transformers import pipeline
-
-# model_path = ("../Models/models--facebook--detr-resnet-50/snapshots"
-#               "/1d5f47bd3bdd2c4bbfa585418ffe6da5028b4c0b")
-
-object_detector = pipeline("object-detection",
-                model="facebook/detr-resnet-50")
-
-# object_detector = pipeline("object-detection",
-#                 model=model_path)
-
-
-def draw_bounding_boxes(image, detections, font_path=None, font_size=20):
-    # Make a copy of the image to draw on
-    draw_image = image.copy()
-    draw = ImageDraw.Draw(draw_image)
-
-    # Load custom font or default font if path not provided
-    if font_path:
-        font = ImageFont.truetype(font_path, font_size)
-    else:
-        # When font_path is not provided, load default font but it's size is fixed
-        font = ImageFont.load_default()
-        # Increase font size workaround by using a TTF font file, if needed, can download and specify the path
-
-    for detection in detections:
-        box = detection['box']
-        xmin = box['xmin']
-        ymin = box['ymin']
-        xmax = box['xmax']
-        ymax = box['ymax']
-
-        # Draw the bounding box
-        draw.rectangle([(xmin, ymin), (xmax, ymax)], outline="red", width=3)
-
-        # Optionally, you can also draw the label and score
-        label = detection['label']
-        score = detection['score']
-        text = f"{label} {score:.2f}"
-
-        # Draw text with background rectangle for visibility
-        if font_path:  # Use the custom font with increased size
-            text_size = draw.textbbox((xmin, ymin), text, font=font)
-        else:
-            # Calculate text size using the default font
-            text_size = draw.textbbox((xmin, ymin), text)
-
-        draw.rectangle([(text_size[0], text_size[1]), (text_size[2], text_size[3])], fill="red")
-        draw.text((xmin, ymin), text, fill="white", font=font)
-
-    return draw_image
-
-
-def detect_object(image):
-    raw_image = image
-    lst=[]
-    output = object_detector(raw_image)
-    for i in output:
-      lst.append(i['label'])
-    processed_image = draw_bounding_boxes(raw_image, output)
-    return processed_image,lst
-
-demo = gr.Interface(fn=detect_object,
-                    inputs=[gr.Image(label="Select Image",type="pil")],
-                    outputs=[gr.Image(label="Processed Image", type="pil"),gr.Textbox(label="Objcts", lines=3),],
-                    title="@GenAILearniverse Project 6: Object Detector",
-                    description="THIS APPLICATION WILL BE USED TO DETECT OBJECTS INSIDE THE PROVIDED INPUT IMAGE.")
+import gradio as gr
+from PIL import Image, ImageDraw, ImageFont
+
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+# model_path = ("../Models/models--facebook--detr-resnet-50/snapshots"
+#               "/1d5f47bd3bdd2c4bbfa585418ffe6da5028b4c0b")
+
+object_detector = pipeline("object-detection",
+                model="facebook/detr-resnet-50")
+
+# object_detector = pipeline("object-detection",
+#                 model=model_path)
+
+
+def draw_bounding_boxes(image, detections, font_path=None, font_size=20):
+    # Make a copy of the image to draw on
+    draw_image = image.copy()
+    draw = ImageDraw.Draw(draw_image)
+
+    # Load custom font or default font if path not provided
+    if font_path:
+        font = ImageFont.truetype(font_path, font_size)
+    else:
+        # When font_path is not provided, load default font but it's size is fixed
+        font = ImageFont.load_default()
+        # Increase font size workaround by using a TTF font file, if needed, can download and specify the path
+
+    for detection in detections:
+        box = detection['box']
+        xmin = box['xmin']
+        ymin = box['ymin']
+        xmax = box['xmax']
+        ymax = box['ymax']
+
+        # Draw the bounding box
+        draw.rectangle([(xmin, ymin), (xmax, ymax)], outline="red", width=3)
+
+        # Optionally, you can also draw the label and score
+        label = detection['label']
+        score = detection['score']
+        text = f"{label} {score:.2f}"
+
+        # Draw text with background rectangle for visibility
+        if font_path:  # Use the custom font with increased size
+            text_size = draw.textbbox((xmin, ymin), text, font=font)
+        else:
+            # Calculate text size using the default font
+            text_size = draw.textbbox((xmin, ymin), text)
+
+        draw.rectangle([(text_size[0], text_size[1]), (text_size[2], text_size[3])], fill="red")
+        draw.text((xmin, ymin), text, fill="white", font=font)
+
+    return draw_image
+
+
+def detect_object(image):
+    raw_image = image
+    lst=[]
+    output = object_detector(raw_image)
+    for i in output:
+      lst.append(i['label'])
+    processed_image = draw_bounding_boxes(raw_image, output)
+    return processed_image,lst
+
+demo = gr.Interface(fn=detect_object,
+                    inputs=[gr.Image(label="Select Image",type="pil")],
+                    outputs=[gr.Image(label="Processed Image", type="pil"),gr.Textbox(label="Objcts", lines=3),],
+                    title="Object Detector",
+                    description="THIS APPLICATION WILL BE USED TO DETECT OBJECTS INSIDE THE PROVIDED INPUT IMAGE  / Live FEED .")
 demo.launch()