Create app.py

app.py

@@ -0,0 +1,129 @@
+import os
+from io import StringIO
+import pandas as pd
+import requests
+from tabulate import tabulate
+from requests.compat import urljoin as urlj
+import json
+from IPython.display import Image, display
+import cv2
+import ast
+import gradio as gr
+from PIL import Image
+import os
+import requests
+
+
+API_TOKEN_FROM_YOUR_PROFILE = '0953ed57513ab57a7dccbc6859472657383dd56b'
+token = API_TOKEN_FROM_YOUR_PROFILE
+
+def request_data(url):
+    auth_header = {"Authorization": f"Token {token}"}
+
+
+    r = requests.get(url, headers=auth_header)
+    req = json.loads(r.text)
+    return req
+
+projects_url = "https://sweden.trapper-project.org/media_classification/api/projects"
+projects = request_data(projects_url)
+
+p13_df = pd.read_csv('observations_0_13.csv')
+p6_df = pd.read_csv('observations_0_6.csv')
+p2_df = pd.read_csv('observations_0_2.csv')
+
+def display_image(file_path, width=1000, height=None):
+    print(file_path)
+    if os.path.exists(file_path):
+        display(Image(file_path, width=width, height=height))
+    else:
+        print('File not in dir (yet)')
+
+def draw_bboxs(img_path, coordinates, out_path=None, display_now=False):
+    image = cv2.imread(img_path)
+    height, width, channels = image.shape
+    for c in coordinates:
+        start_point = (int(c[0]*width), int(c[1]*height))
+        #end_point = (int(c[2]*width), int(c[3]*height))
+        end_point = (int(c[0]*width)+ int(c[2]*width), int(c[1]*height) + int(c[3]*height))
+        cv2.rectangle(image, start_point, end_point, color=(0,255,0), thickness=2)
+    if not out_path:
+        out_path = img_path.replace('.', '_bboxes.')
+   
+    cv2.imwrite(out_path, image)
+
+    if display_now:
+        display_image(out_path)
+    return out_path
+
+def select_by_species(df, species):
+    return df[df['commonName'] == species]
+
+def show_random_species(df, species):
+    display_now = True
+    sdf = select_by_species(df, species)
+    if sdf.empty:
+        print('Species not in available photo set')
+    row = sdf.sample(n=None)
+    print(row.to_markdown())
+    row = row.squeeze()
+    photo_path = download_image_url(row.filePath, f'{row.fileName}.png', display_now=display_now)
+    row['local_path'] = photo_path
+    row['bboxes'] = ast.literal_eval(row['bboxes'])
+    return row
+
+def download_image_url(url, file_name, file_path='photos', display_now=False):
+    photo_path = os.path.join(file_path, file_name)
+    auth_header = {"Authorization": f"Token {token}"}
+    r = requests.get(url, headers=auth_header)
+
+    if r.status_code == 200:
+        os.makedirs(file_path, exist_ok=True)
+        with open(photo_path, 'wb') as out_file:
+            out_file.write(r.content)
+    else:
+        print(f"ERROR code: {r.status_code} on URL: {url}")
+        return None
+    
+    if display_now:
+        display_image(photo_path)
+    
+    return photo_path
+
+def display_image(photo_path):
+    image = Image.open(photo_path)
+    image.show()
+    return image
+
+def outputImg(df_name, species):
+    print("df_name", df_name)
+    if df_name == "p13_df":
+        df = p13_df
+    elif df_name == "p6_df":
+        df = p6_df
+    elif df_name == "p2_df":
+        df = p2_df
+    else:
+        print("Invalid DataFrame selected")
+        return None
+    image_info = show_random_species(df, species)
+    print(image_info['local_path'], image_info.to_string())
+    details_table = tabulate(pd.DataFrame(image_info).transpose(), headers='keys', tablefmt='pipe')
+    print("details_table",details_table)
+    bbimg = draw_bboxs(image_info['local_path'], image_info.bboxes)#image_info['local_path'], image_info.to_string()
+    return display_image(bbimg), details_table#image_info.to_string()
+
+iface = gr.Interface(
+    fn=outputImg,
+    inputs=[
+        gr.inputs.Dropdown(label="df", choices=["p13_df", "p6_df", "p2_df"]),
+        gr.inputs.Dropdown(label="species", choices=list(pd.concat([p13_df['commonName'], p6_df['commonName'], p2_df['commonName']]).unique())),
+        #gr.inputs.Textbox(label="DataFrame")
+    ],
+    outputs=[
+        gr.outputs.Image(type="pil", label="Random Image"),
+        gr.outputs.Textbox(label="Details", type="text")
+    ]
+)
+
+iface.launch()