Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+test1.jpg filter=lfs diff=lfs merge=lfs -text
+test3.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1,6 @@
 ---
-title: Receptacle Detection
-emoji: 🐢
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 3.47.1
+title: receptacle_detection
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 3.42.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,338 @@
+
+import gradio as gr
+import cv2
+import requests
+import os
+import numpy as np
+from sahi.utils.yolov5 import (
+    download_yolov5s6_model,
+)
+
+
+# import required functions, classes
+from sahi import AutoDetectionModel
+from sahi.utils.cv import read_image
+from sahi.utils.file import download_from_url
+from sahi.predict import get_prediction, get_sliced_prediction, predict, visualize_object_predictions
+from IPython.display import Image
+
+from ultralytics import YOLO
+import gradio as gr
+import cv2
+import requests
+import os
+
+from ultralytics import YOLO
+
+
+yolov5_model_path = 'best.pt'
+download_yolov5s6_model(destination_path=yolov5_model_path)
+detection_model = AutoDetectionModel.from_pretrained(
+    model_type='yolov5',
+    model_path=yolov5_model_path,
+    confidence_threshold=0.01,
+    device="cpu", # or 'cuda:0'
+)
+
+#model = YOLO('/home/ubuntu/Receptacle_Detection_Demo/best.pt')
+
+
+
+demo = gr.Blocks()
+
+EXAMPLES = [
+    [ "test1.jpg"],
+    ["test2.jpg"],
+    ["test3.jpg"],
+    ["test4.jpg"],
+]
+
+
+def with_labels(image_path):
+    result = get_sliced_prediction(
+    image_path,
+    detection_model,
+    slice_height = 512,
+    slice_width = 512,
+    overlap_height_ratio = 0.12,
+    overlap_width_ratio = 0.12)
+    #result.export_visuals(export_dir="/home/ubuntu/Receptacle_Detection_Demo/")
+    #image = cv2.imread("/home/ubuntu/Receptacle_Detection_Demo/prediction_visual.png")
+    #img = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
+
+    count = -1
+    new_list=[]
+    for i in result.object_prediction_list:
+        count += 1
+        print(i)
+        score = i.score
+        value = score.value
+        category = i.category
+        category_name = category.name
+        if value > confidence_scores[category_name]:
+            print(value)
+            print(confidence_scores[category_name])
+            new_list.append(result.object_prediction_list[count])
+            
+    img_converted = cv2.cvtColor(image_path, cv2.COLOR_BGR2RGB)
+    numpydata = np.asarray(img_converted)
+    visualize_object_predictions(
+    numpydata, 
+    object_prediction_list = new_list,
+    text_size=1,
+    text_th=1,
+    hide_labels = 0, 
+    rect_th=3,
+    output_dir='/home/ubuntu/Receptacle_Detection_Demo/',
+    file_name = 'result',
+    export_format = 'png')
+    image2 = cv2.imread("/home/ubuntu/Receptacle_Detection_Demo/result.png")
+    img_rgb = cv2.cvtColor(image2, cv2.COLOR_BGR2RGB)
+
+ 
+    class_counts = {}
+
+    
+    predictions = new_list
+    for i in predictions:
+        category = i.category
+        category_name = category.name
+        if category_name not in class_counts:
+            class_counts[category_name] = 1
+        else:
+            class_counts[category_name] += 1
+
+ 
+    legend_text = 'Symbols Counted:'
+    for class_name, count in class_counts.items():
+        legend_text += f' {class_name}: {count} |'
+    
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    font_scale = 1
+    font_color = (255, 255, 255)  
+    font_thickness = 2
+    legend_bg_color = (131, 79, 0) 
+    legend_padding = 10
+
+    legend_size, _ = cv2.getTextSize(legend_text, font, font_scale, font_thickness)
+    legend_bg_height = legend_size[1] + 2 * legend_padding
+    legend_bg_width = legend_size[0] + 2 * legend_padding
+
+    legend_bg = np.zeros((legend_bg_height, legend_bg_width, 3), dtype=np.uint8)
+    legend_bg[:] = legend_bg_color
+    cv2.putText(legend_bg, legend_text, (legend_padding, legend_padding + legend_size[1]), font,
+                font_scale, font_color, font_thickness)
+    
+    img_height, img_width, _ = img_rgb.shape
+    legend_x = img_width - legend_bg_width
+    legend_y = img_height - legend_bg_height
+
+    img_rgb[legend_y:, legend_x:, :] = legend_bg
+
+    result_image_path = '/home/ubuntu/Receptacle_Detection_Demo/result_with_legend.png'
+    cv2.imwrite(result_image_path, img_rgb)
+
+    return cv2.cvtColor(cv2.imread(result_image_path), cv2.COLOR_BGR2RGB)
+
+def without_labels(image_path):
+    result = get_sliced_prediction(
+    image_path,
+    detection_model,
+    slice_height = 512,
+    slice_width = 512,
+    overlap_height_ratio = 0.12,
+    overlap_width_ratio = 0.12)
+    #result.export_visuals(export_dir="/home/ubuntu/Receptacle_Detection_Demo/")
+    #image = cv2.imread("/home/ubuntu/Receptacle_Detection_Demo/prediction_visual.png")
+    #img = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
+
+    count = -1
+    new_list=[]
+    for i in result.object_prediction_list:
+        count += 1
+        print(i)
+        score = i.score
+        value = score.value
+        category = i.category
+        category_name = category.name
+        if value > confidence_scores[category_name]:
+            print(value)
+            print(confidence_scores[category_name])
+            new_list.append(result.object_prediction_list[count])
+            
+    img_converted = cv2.cvtColor(image_path, cv2.COLOR_BGR2RGB)
+    numpydata = np.asarray(img_converted)
+    visualize_object_predictions(
+    numpydata, 
+    object_prediction_list = new_list,
+    hide_labels = 1, 
+    rect_th=3,
+    output_dir='/home/ubuntu/Receptacle_Detection_Demo/',
+    file_name = 'result',
+    export_format = 'png')
+    image2 = cv2.imread("/home/ubuntu/Receptacle_Detection_Demo/result.png")
+    img_rgb = cv2.cvtColor(image2, cv2.COLOR_BGR2RGB)
+
+ 
+    class_counts = {}
+
+    
+    predictions = new_list
+    for i in predictions:
+        category = i.category
+        category_name = category.name
+        if category_name not in class_counts:
+            class_counts[category_name] = 1
+        else:
+            class_counts[category_name] += 1
+
+ 
+    legend_text = 'Symbols Counted:'
+    for class_name, count in class_counts.items():
+        legend_text += f' {class_name}: {count} |'
+    
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    font_scale = 1.5
+    font_color = (255, 255, 255)  
+    font_thickness = 2
+    legend_bg_color = (131, 79, 0) 
+    legend_padding = 10
+
+    legend_size, _ = cv2.getTextSize(legend_text, font, font_scale, font_thickness)
+    legend_bg_height = legend_size[1] + 2 * legend_padding
+    legend_bg_width = legend_size[0] + 2 * legend_padding
+
+    legend_bg = np.zeros((legend_bg_height, legend_bg_width, 3), dtype=np.uint8)
+    legend_bg[:] = legend_bg_color
+    cv2.putText(legend_bg, legend_text, (legend_padding, legend_padding + legend_size[1]), font,
+                font_scale, font_color, font_thickness)
+    
+    img_height, img_width, _ = img_rgb.shape
+    legend_x = img_width - legend_bg_width
+    legend_y = img_height - legend_bg_height
+
+    img_rgb[legend_y:, legend_x:, :] = legend_bg
+
+    result_image_path = '/home/ubuntu/Receptacle_Detection_Demo/result_with_legend.png'
+    cv2.imwrite(result_image_path, img_rgb)
+
+    return cv2.cvtColor(cv2.imread(result_image_path), cv2.COLOR_BGR2RGB)
+
+def choose_function(choice, input_text):
+    if choice == "With Labels":
+        return with_labels(input_text)
+    else:
+        return without_labels(input_text)
+print("Starting the demo...")
+
+
+
+def update_duplex(val):
+    confidence_scores['Duplex - Standard'] = val
+    return 'updated!'
+def update_single(val):
+    confidence_scores['Singleplex - Standard'] = val
+    return 'updated!'
+
+def update_triplex(val):
+    confidence_scores['Triplex - Standard'] = val
+    return 'updated!'
+
+def update_quadruplex(val):
+    confidence_scores['Quadruplex - Standard'] = val
+    return 'updated!'
+
+def update_gfci(val):
+    confidence_scores['Duplex - GFCI'] = val
+    return 'updated!'
+
+def update_gfciwp(val):
+    confidence_scores['Duplex - Weatherproof-GFCI'] = val
+    return 'updated!'
+
+theme = gr.themes.Soft()
+
+with gr.Blocks(theme=theme) as demo:
+    gr.Markdown(
+        """
+        <h1 align="center">Receptacle Detector for Takeoff Automation</h1>
+        """
+    )
+
+    gr.Markdown("### Step 1: Upload an image")
+    
+    with gr.Row():
+        input_image = gr.Image(
+            label="Upload an image here.", source="upload", interactive=True,
+        )
+        examples = gr.Examples(
+            examples=EXAMPLES, inputs=[input_image], examples_per_page=4, label="Examples to use.",
+        )
+
+    
+    gr.Markdown("### Step 2: Choose either \n With labels: See receptacles detected with type detected/confidence score included\n Without labels: See only bounding boxes")
+    filter_name = gr.Dropdown(
+        choices=["With Labels", "Without Labels"], label="With/Without Labels", interactive=True
+    )
+    gr.Markdown("### Step 3: Choose confidence score levels for each symbol detected (default are optimal scores)")
+    filter_name1 = gr.Slider(
+        minimum = .1,
+        maximum = 1,
+        value = .53,
+        interactive = True,
+        label = 'Singleplex',
+    )
+    filter_name2 = gr.Slider(
+        minimum = .1,
+        maximum = 1,
+        value = .66,
+        interactive = True,
+        label = 'Duplex',
+    )
+    filter_name3 = gr.Slider(
+        minimum = .1,
+        maximum = 1,
+        value = .65,
+        interactive = True,
+        label = 'Triplex',
+    )
+    filter_name4 = gr.Slider(
+        minimum = .1,
+        maximum = 1,
+        value = .63,
+        interactive = True,
+        label = 'Quadruplex',
+    )
+    filter_name5 = gr.Slider(
+        minimum = .1,
+        maximum = 1,
+        value = .31,
+        interactive = True,
+        label = 'GFCI',
+    )
+    filter_name6 = gr.Slider(
+        minimum = .1,
+        maximum = 1,
+        value = .33,
+        interactive = True,
+        label = 'GFCI/WP',
+    )
+    
+    filter_name2.change(fn=update_duplex, inputs=filter_name2)
+    filter_name1.change(fn=update_single, inputs=filter_name1)
+    filter_name3.change(fn=update_triplex, inputs=filter_name3)
+    filter_name4.change(fn=update_quadruplex, inputs=filter_name4)
+    filter_name5.change(fn=update_gfci, inputs=filter_name5)
+    filter_name6.change(fn=update_gfciwp, inputs=filter_name6)
+    confidence_scores = {'Triplex - Standard': filter_name3.value,'Duplex - Standard': filter_name2.value,'Singleplex - Standard': filter_name1.value,'Duplex - GFCI': filter_name5.value,'Duplex - Weatherproof-GFCI':filter_name6.value,'Quadruplex - Standard': filter_name4.value}
+ 
+    gr.Markdown("### Step 4: See results with number of symbols counted in the bottom right corner")
+    results_button = gr.Button("See Results")
+    results_button.click(
+        choose_function,
+        inputs = [filter_name,input_image],
+        outputs = [gr.components.Image(type="numpy", label="Output Image")]
+        
+    )
+   
+demo.launch()

best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:77e155c040caeedc89419dbf90090e26a78a8a6aa49b7cbd5ffabe40b1f79e68
+size 14835609

requirements.txt

@@ -0,0 +1,7 @@
+sahi
+IPython
+yolov5
+ultralytics==8.0.186
+
+# At 8.0.187 ultralytics deprecates ultralytics.yolo, but they have not updated the yolov5 package.  Which breaks on import :/
+