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daviddao commited on Jun 8, 2023

Commit

c62379b

1 Parent(s): b65a84e

updatable script

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Files changed (5) hide show

redis2sheets_img.py +8 -2
redisList.py +28 -0
sql2predict.py +96 -0
sql2redis.py +70 -39
uuids.json +1 -0

redis2sheets_img.py CHANGED Viewed

@@ -18,8 +18,14 @@ for i_key in redis_keys:
     image_id = i_key.decode().split(':')[-2]
     raw_image_data_bytes = r.hgetall(f'image:{image_id}'.encode('utf-8'))
     raw_image_data = {k.decode('utf-8'): v.decode('utf-8') for k, v in raw_image_data_bytes.items()}
-    path = f'https://gainforest-transparency-dashboard.s3.amazonaws.com/{raw_image_data["awsCID"]}'
-    dashboard_url = f'https://gainforest.app/observations/{raw_image_data["uuid"]}'
     row = [image_id, i_key.decode().split(':')[-1], image_data['author'], image_data['author_id'], image_data['role'], image_data['timestamp'], image_data['label'], image_data['confidence'], path, dashboard_url,'=IMAGE("{}", 2)'.format(path)]
     rows.append(row)

     image_id = i_key.decode().split(':')[-2]
     raw_image_data_bytes = r.hgetall(f'image:{image_id}'.encode('utf-8'))
     raw_image_data = {k.decode('utf-8'): v.decode('utf-8') for k, v in raw_image_data_bytes.items()}
+    try:
+        path = f'https://gainforest-transparency-dashboard.s3.amazonaws.com/{raw_image_data["awsCID"]}'
+    except:
+        path = f'https://twitter.com/GainForestNow/photo.jpg'
+    try:
+        dashboard_url = f'https://gainforest.app/observations/{raw_image_data["uuid"]}'
+    except:
+        dashboard_url = f'no'
     row = [image_id, i_key.decode().split(':')[-1], image_data['author'], image_data['author_id'], image_data['role'], image_data['timestamp'], image_data['label'], image_data['confidence'], path, dashboard_url,'=IMAGE("{}", 2)'.format(path)]
     rows.append(row)

redisList.py ADDED Viewed

	@@ -0,0 +1,28 @@

+import redis
+import json
+redis_url = 'redis://default:[email protected]:7369'
+r = redis.from_url(redis_url)
+uuids = []
+redis_keys = r.keys('image:*')
+for i_key in redis_keys:
+    image_data_bytes = r.hgetall(i_key)
+    image_data = {k.decode('utf-8'): v.decode('utf-8') for k, v in image_data_bytes.items()}
+    try:
+        uuids.append(image_data['uuid'])
+    except:
+        print(i_key)
+# Save the UUIDs to a file
+with open('uuids.json', 'w') as file:
+    json.dump(uuids, file)
+print('UUIDs saved to uuids.json')
+# # Read the JSON file
+# with open('./uuids.json', 'r') as file:
+#     uuid_list = json.load(file)
+# # Access the list
+# print(uuid_list == uuids)

sql2predict.py ADDED Viewed

	@@ -0,0 +1,96 @@

+import redis
+import psycopg2
+from gradio_client import Client
+import json
+from datetime import datetime
+redis_url = 'redis://default:[email protected]:7369'
+r = redis.from_url(redis_url)
+# run josh's algorithm
+client = Client("https://diverseco-metaformer.hf.space/")
+# Postgres database get the observations
+def connect():
+    conn = None
+    try:
+        # Connecting to your PostgreSQL server
+        print('Connecting to the PostgreSQL database...')
+        conn = psycopg2.connect('postgresql://postgres:[email protected]:7297/railway')
+        # conn = psycopg2.connect('postgresql://postgres:[email protected]:6771/railway') #staging
+        # conn = psycopg2.connect('postgresql://postgres:[email protected]:5772/railway') #development
+    except (Exception, psycopg2.DatabaseError) as error:
+        print(error)
+    print("Connection successful")
+    return conn
+def predict_images():
+    query = """SELECT "id", "awsCID", "name" FROM "Asset" WHERE "projectId" = 37 AND "classification" = 'Camera Traps'"""
+    conn = connect()
+    cur = conn.cursor()
+    try:
+        # Execute a simple SQL command
+        cur.execute(query)
+        # Fetch all the data returned by the database
+        rows = cur.fetchall()
+        for row in rows:
+            image_cnt = generate_id('cnt:predict:image')
+            # Set multiple field-value pairs using HMSET
+            # Convert the date string to a datetime object
+            # date_obj = datetime.strptime(row[2].split('_')[1], "%Y%m%d%H%M%S")
+            # # Format the datetime object to the desired format
+            # formatted_date = date_obj.strftime("%d/%m/%Y %H:%M")
+            # fields_values = {
+            #     'uuid': row[0],
+            #     'awsCID': row[1],
+            #     'name': row[2],
+            #     'sensor': 'M300/RGB',
+            #     'label': '❓',
+            #     'author': '❓',
+            #     'timestamp': formatted_date
+            # }
+            # r.hmset(f'image:{image_cnt}', fields_values)
+            image_url = f'https://gainforest-transparency-dashboard.s3.amazonaws.com/{row[1]}'
+            print(f'predicting {image_url}')
+            result = client.predict(
+                image_url,
+                api_name="/predict"
+            )
+            with open(result, 'r') as file:
+                json_data = json.load(file)
+            # Extract labels and confidences from JSON data
+            labels = [data['label'] for data in json_data['confidences']]
+            confidences = [data['confidence'] for data in json_data['confidences']]
+            # Store labels and confidences in Redis using HMSET
+            for label, confidence in zip(labels, confidences):
+                pred_cnt = generate_id(f'cnt:prediction:{image_cnt}')
+                fields_values = {
+                    'label': label,
+                    'confidence': confidence,
+                }
+                r.hmset(f'prediction:{image_cnt}:{pred_cnt}', fields_values)
+    except (Exception, psycopg2.DatabaseError) as error:
+        print(error)
+    finally:
+        # Close the cursor and connection
+        cur.close()
+        conn.close()
+        r.close()
+# Function to generate auto-incremented IDs
+def generate_id(key):
+    return r.incr(key)
+predict_images()

sql2redis.py CHANGED Viewed

@@ -2,6 +2,13 @@ import redis
 import psycopg2
 from gradio_client import Client
 import json
 redis_url = 'redis://default:[email protected]:7369'
 r = redis.from_url(redis_url)
@@ -25,7 +32,7 @@ def connect():
     return conn
 def get_all_images():
-    query = """SELECT "id", "awsCID" FROM "Asset" WHERE "projectId" = 37 AND "classification" = 'Camera Traps'"""
     conn = connect()
     cur = conn.cursor()
@@ -36,41 +43,65 @@ def get_all_images():
         # Fetch all the data returned by the database
         rows = cur.fetchall()
         for row in rows:
-            image_cnt = generate_id('cnt:image')
             # Set multiple field-value pairs using HMSET
-            fields_values = {
-                'uuid': row[0],
-                'awsCID': row[1],
-                'sensor': 'Rover/RGB',
-                'label': '❓',
-                'author': '❓',
-                'timestamp': '❓'
-            }
-            r.hmset(f'image:{image_cnt}', fields_values)
-            image_url = f'https://gainforest-transparency-dashboard.s3.amazonaws.com/{row[1]}'
-            print(f'predicting {image_url}')
-            result = client.predict(
-                image_url,
-                api_name="/predict"
-            )
-            with open(result, 'r') as file:
-                json_data = json.load(file)
-            # Extract labels and confidences from JSON data
-            labels = [data['label'] for data in json_data['confidences']]
-            confidences = [data['confidence'] for data in json_data['confidences']]
-            # Store labels and confidences in Redis using HMSET
-            for label, confidence in zip(labels, confidences):
-                pred_cnt = generate_id(f'cnt:prediction:{image_cnt}')
-                fields_values = {
-                    'label': label,
-                    'confidence': confidence,
-                }
-                r.hmset(f'prediction:{image_cnt}:{pred_cnt}', fields_values)
     except (Exception, psycopg2.DatabaseError) as error:
@@ -99,11 +130,11 @@ def get_all_sounds():
             fields_values = {
                 'uuid': row[0],
                 'awsCID': row[1],
-                'sensor': 'Speedybee/Acoustic',
                 'label': '❓',
                 'author': '❓',
                 'label_at': '❓',
-                'timestamp': '❓'
             }
             r.hmset(f'sound:{sound_cnt}', fields_values)
@@ -120,6 +151,6 @@ def get_all_sounds():
 def generate_id(key):
     return r.incr(key)
-r.flushdb()
 get_all_images()
-get_all_sounds()

 import psycopg2
 from gradio_client import Client
 import json
+from datetime import datetime
+import json
+# Read the JSON file
+# Read the JSON file
+with open('./uuids.json', 'r') as file:
+    uuid_list = json.load(file)
 redis_url = 'redis://default:[email protected]:7369'
 r = redis.from_url(redis_url)
     return conn
 def get_all_images():
+    query = """SELECT "id", "awsCID", "name" FROM "Asset" WHERE "projectId" = 37 AND "classification" = 'Camera Traps'"""
     conn = connect()
     cur = conn.cursor()
         # Fetch all the data returned by the database
         rows = cur.fetchall()
         for row in rows:
             # Set multiple field-value pairs using HMSET
+            # Convert the date string to a datetime object
+            if row[0] not in uuid_list:
+                print(row[0])
+                image_cnt = generate_id('cnt:image')
+                if row[2].split('_')[0] == 'DJI':
+                    print(f'adding DJI M300: {row[2]}')
+                    date_obj = datetime.strptime(row[2].split('_')[1], "%Y%m%d%H%M%S")
+                    # Format the datetime object to the desired format
+                    formatted_date = date_obj.strftime("%d/%m/%Y %H:%M")
+                    fields_values = {
+                        'uuid': row[0],
+                        'awsCID': row[1],
+                        'name': row[2],
+                        'sensor': 'M300/RGB',
+                        'label': '❓',
+                        'author': '❓',
+                        'timestamp': formatted_date
+                    }
+                    r.hmset(f'image:{image_cnt}', fields_values)
+                else:
+                    print(f'adding Rover: {row[2]}')
+                    # Format the datetime object to the desired format
+                    fields_values = {
+                        'uuid': row[0],
+                        'awsCID': row[1],
+                        'name': row[2],
+                        'sensor': 'Rover/RGB',
+                        'label': '❓',
+                        'author': '❓',
+                        'timestamp': row[2].split('_')[0]
+                    }
+                    r.hmset(f'image:{image_cnt}', fields_values)
+            # image_url = f'https://gainforest-transparency-dashboard.s3.amazonaws.com/{row[1]}'
+            # print(f'predicting {image_url}')
+            # result = client.predict(
+            #     image_url,
+            #     api_name="/predict"
+            # )
+            # with open(result, 'r') as file:
+            #     json_data = json.load(file)
+            # # Extract labels and confidences from JSON data
+            # labels = [data['label'] for data in json_data['confidences']]
+            # confidences = [data['confidence'] for data in json_data['confidences']]
+            # # Store labels and confidences in Redis using HMSET
+            # for label, confidence in zip(labels, confidences):
+            #     pred_cnt = generate_id(f'cnt:prediction:{image_cnt}')
+            #     fields_values = {
+            #         'label': label,
+            #         'confidence': confidence,
+            #     }
+            #     r.hmset(f'prediction:{image_cnt}:{pred_cnt}', fields_values)
     except (Exception, psycopg2.DatabaseError) as error:
             fields_values = {
                 'uuid': row[0],
                 'awsCID': row[1],
+                'sensor': 'Rover/Acoustic',
                 'label': '❓',
                 'author': '❓',
                 'label_at': '❓',
+                'timestamp': '6th June 2023, 6 am'
             }
             r.hmset(f'sound:{sound_cnt}', fields_values)
 def generate_id(key):
     return r.incr(key)
+# r.flushdb()
 get_all_images()
+# get_all_sounds()

uuids.json ADDED Viewed

	@@ -0,0 +1 @@

+ ["aa709960-e359-479b-ab8d-2105666d58ff", "fcc5073d-09bc-4c84-b686-411d80aea2bd", "87add743-271e-4cbc-8334-ca607bb216e3", "934d6ade-0c41-4c91-8591-dfa42205665e", "6ebac702-a0dc-491f-8b36-fd112f1da5ef", "6d6250be-c6ff-4a7a-b7be-8d92c203cab8", "a07914c3-9838-4af2-a2d5-4d25102a8a06", "1e0fe1ae-0d5c-4230-9b95-b7c77e9e69ce", "afef5dcc-e92c-4bc2-8d43-91f8e32a5d74", "fa0063d8-d5e7-4088-9016-59168e6aecc9", "6723af5a-a6f4-4445-b8de-746a4d7e5708", "bd64d819-5669-404b-bbe6-eeee2b99fc20", "ff081add-043a-4df6-baa3-21a36d3bc097", "a6b95e99-abbd-4e73-8ce6-e449cf981753", "f717ef3a-45af-4208-8f75-b28dd5c72855", "150f89b5-09cf-4411-a254-ca7a8255e612", "8fea38b1-5425-4dd1-824b-edead56a58fb", "2209d127-e94c-477f-87b7-f18d170622dc", "956e01f5-2a27-4aa5-8bff-d8b95c25983c", "47060014-bf76-4d6b-bfe7-9cd6a4324e69", "b8fe4567-c309-4fc0-8ada-26c43c4afb6a", "c22ed793-df3b-47f1-94ef-923a7eaf7454", "6b4df3b2-5f7e-4940-9e53-9a0e16f891ea", "7821dd0d-6dd2-473c-ac5a-1fe595f88f50", "5409728a-925b-4bdc-b250-6640ca82b4e9", "7aceb2f3-be7a-4450-99db-f88a01cf9797", "a8eb6198-5640-4f6d-a10f-ec496f4b3df0", "628da910-b568-4b80-ae06-a087240bbb98", "076e5ccf-e7ef-484e-8c96-617733d06f09", "29bc7bb2-0bb0-4d1e-986a-b9b65cb3dc4e", "b3d165c5-4f53-4a1e-9de0-609ec1514a04", "bcc4bb84-a21f-4683-abc8-e43139d184c8", "a61651f9-e01a-407e-a0a1-00b789df588e", "a49df16f-d848-4d82-840f-c1cb5b115c0a", "6e44ab58-d735-4244-b751-6f95687e09d9", "8dbc69b8-6f80-4c27-b99e-147586e3aef7", "686eeeaf-963c-4306-9977-ef6f4825526d", "a6744dbd-9f3e-402f-ba14-d26bf2b1ee9f", "b1482bc1-0b05-414d-af8c-919a786e6e24", "73748a5d-9d32-4648-80ae-d746435c1d25", "4a6a63b9-acfc-4527-b8a1-b6d978da43fd", "fc9deccf-6306-4661-8a75-3be23ff54170", "4be9688b-e756-479b-89f1-754ac02060f2", "7bfc4329-3ac5-4015-8e0c-351360c3de12", "0e1a7e7a-d4c7-4c9d-ba82-7ff12682ee97", "653d8796-7096-4ff0-8a34-ba9da853c3d6", "fabc1b56-30bb-468b-874d-e4bcece0800d", "1dc3d9c8-3899-4f7e-9e21-5306cf2b0785", "82e86018-1e25-482d-9e93-954512b5a65b", "8bc25292-030e-42f2-94e0-7ebe2ede87a1", "51039d31-661c-4e9f-b5bf-c3e674b04dfe", "726c7cae-1980-4cc7-95b1-16a19805a4d2", "9b67e7df-5c9d-4e41-adf2-7c351748b6c5", "95368698-9c17-4960-b386-2ce0f667620d", "8c04c37c-bf42-446c-94a3-b4c0ea1b66b6", "2e6506a5-9175-4cf0-9000-57915e520d8d", "2c1bb8f3-b57b-417b-b7eb-f08dd790bd5b", "c5ba33a5-cbf6-44aa-a8a6-16e21d56a381", "d67ad26c-10fc-410d-b98e-067e137508dc", "880e30ee-f480-43f0-8d78-024bf576a1b4", "8575a0ff-218a-45a1-8c63-62eb6aef146e", "a4cd5139-ccf6-4c4f-9084-78d1f768559e", "845e44a5-0e8a-444e-b78b-ec5e70fdf7a0", "aafe2dd4-b102-41a9-b194-744057939d60", "b56fae77-1a2f-4e7b-8214-fdca11be487d", "6270f852-647e-4728-8b49-2086532f6e1d", "ccb62204-6463-43f3-bb80-b680a5edbd7a", "c29de73f-4b49-4d4d-bcea-8942d36854dc", "19c99e46-a966-4c92-957d-76b71f1bb957", "9e116f4a-8e92-4711-a211-b92d34439d33", "94b5475e-8ee0-4733-892d-5ad38fc1aa4b", "4184339b-6cd0-4a80-9c1e-0289a43d30a0", "4f72b8bb-5d2e-4dbc-8873-f353f6dd9046", "89f6b1ea-514e-4462-9f39-edc6a85e5683", 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