Add files via upload

identified_plantsCalpteAnna.csv

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+imageFileName,gbifIDPlant,speciesPlant,scorePlant,scientificNamePlant,gbifIDBird,decimalLatitude,decimalLongitude,coordinateUncertaintyInMeters,continent,stateProvince,year,month,day,eventDate
+1022800_2838316417_1.jpg,4093688,Catophractes alexandri,17.485999999999997,"Calypte anna (R.Lesson, 1829)",2476674,38.125915,-122.434387,nan,NORTH_AMERICA,United States - California (CA),2010.0,1.0,12.0,2010-01-12
+1024052_2839891443_1.jpg,3129497,Baccharis pilularis,25.172,"Calypte anna (R.Lesson, 1829)",2476674,37.681302,-122.457,nan,NORTH_AMERICA,United States - California (CA),2010.0,1.0,12.0,2010-01-12
+1026162_2839849463_3.jpg,3794637,Euonymus phellomanus,6.413,"Calypte anna (R.Lesson, 1829)",2476674,37.770715,-122.512665,nan,NORTH_AMERICA,United States - California (CA),2010.0,1.0,13.0,2010-01-13
+1026163_2839849463_1.jpg,3794637,Euonymus phellomanus,19.733999999999998,"Calypte anna (R.Lesson, 1829)",2476674,37.770715,-122.512665,nan,NORTH_AMERICA,United States - California (CA),2010.0,1.0,13.0,2010-01-13
+505701_2835402088_1.jpg,2878213,Quercus macrocarpa,19.227,"Calypte anna (R.Lesson, 1829)",2476674,36.793434,-121.018452,nan,NORTH_AMERICA,United States - California (CA),2003.0,4.0,24.0,2003-04-24
+646115_2832104195_1.jpg,2684030,Cupressus sempervirens,7.5249999999999995,"Calypte anna (R.Lesson, 1829)",2476674,36.578041,-121.961975,nan,NORTH_AMERICA,United States - California (CA),2008.0,4.0,4.0,2008-04-04
+646116_2832104195_2.jpg,2684030,Cupressus sempervirens,4.885,"Calypte anna (R.Lesson, 1829)",2476674,36.578041,-121.961975,nan,NORTH_AMERICA,United States - California (CA),2008.0,4.0,4.0,2008-04-04

identified_plantsCalpteAnna2.csv

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+imageFileName,gbifIDPlant,speciesPlant,scorePlant,scientificNameBird,gbifIDBird,decimalLatitude,decimalLongitude,coordinateUncertaintyInMeters,continent,stateProvince,year,month,day,eventDate
+2206258_2832240104_1.jpg,2928783,Nicotiana glauca,76.534,"Calypte anna (R.Lesson, 1829)",2476674,36.438563,-118.905516,10.0,NORTH_AMERICA,United States - California (CA),2007.0,8.0,22.0,2007-08-22
+3632026_2838566912_1.jpg,5384100,Opuntia oricola,32.326,"Calypte anna (R.Lesson, 1829)",2476674,33.69803,-118.037066,100.0,NORTH_AMERICA,United States - California (CA),2012.0,7.0,16.0,2012-07-16
+3632030_2838566912_5.jpg,2928783,Nicotiana glauca,84.845,"Calypte anna (R.Lesson, 1829)",2476674,33.69803,-118.037066,100.0,NORTH_AMERICA,United States - California (CA),2012.0,7.0,16.0,2012-07-16
+3632031_2838566912_2.jpg,2928783,Nicotiana glauca,84.32300000000001,"Calypte anna (R.Lesson, 1829)",2476674,33.69803,-118.037066,100.0,NORTH_AMERICA,United States - California (CA),2012.0,7.0,16.0,2012-07-16
+3799156_2839697081_1.jpg,9520660,Aloe succotrina,6.1530000000000005,"Calypte anna (R.Lesson, 1829)",2476674,37.75245,-122.487869,1000.0,NORTH_AMERICA,United States - California (CA),2012.0,8.0,14.0,2012-08-14
+5312061_2838840965_3.jpg,2926390,Agastache rupestris,83.643,"Calypte anna (R.Lesson, 1829)",2476674,37.87675,-122.462797,100.0,NORTH_AMERICA,United States - California (CA),2013.0,7.0,25.0,2013-07-25
+9707571_2832731761_1.jpg,7298537,Tecoma fulva,21.566,"Calypte anna (R.Lesson, 1829)",2476674,35.156592,-114.571995,25.0,NORTH_AMERICA,United States - Arizona (AZ),2015.0,10.0,6.0,2015-10-06
+9707576_2839316813_1.jpg,7828119,Senegalia erubescens,8.361,"Calypte anna (R.Lesson, 1829)",2476674,32.731664,-117.151731,25.0,NORTH_AMERICA,United States - California (CA),2015.0,10.0,8.0,2015-10-08
+9707582_2839748792_1.jpg,2777724,Aloe vera,69.396,"Calypte anna (R.Lesson, 1829)",2476674,34.943329,-120.436237,25.0,NORTH_AMERICA,United States - California (CA),2015.0,10.0,12.0,2015-10-12
+9707583_2839748792_2.jpg,6411750,Salvia leucantha,99.261,"Calypte anna (R.Lesson, 1829)",2476674,34.943329,-120.436237,25.0,NORTH_AMERICA,United States - California (CA),2015.0,10.0,12.0,2015-10-12

removebirdsIDPlants.py

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+import os
+import requests
+import json
+import cv2
+import numpy as np
+import torch
+import torchvision.transforms as T
+import torchvision
+from PIL import Image
+import pandas as pd
+from pygbif import species
+
+# Pl@ntNet API details
+API_KEY = "2b10SRlISJG0KaNZCAHV6k8hz"  # Replace with your actual API key
+PROJECT = "all"
+api_endpoint = f"https://my-api.plantnet.org/v2/identify/{PROJECT}?api-key={API_KEY}"
+
+# Load the COCO class labels
+COCO_CLASSES = [
+    '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
+    'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
+    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+    'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag',
+    'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite',
+    'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
+    'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana',
+    'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
+    'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table',
+    'toilet', 'TV', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
+    'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock',
+    'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'
+]
+
+# Load a pre-trained segmentation model
+model = torchvision.models.segmentation.deeplabv3_resnet101(pretrained=True).eval()
+
+# Define a transformation to preprocess the input image
+transform = T.Compose([
+    T.ToTensor(),  # Convert image to tensor
+    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # Normalize
+])
+
+# Function to perform segmentation
+def segment_image(image):
+    # Ensure the image is in RGB mode (i.e., it has 3 channels)
+    if image.mode != 'RGB':
+        image = image.convert('RGB')
+    input_tensor = transform(image).unsqueeze(0)  # Add batch dimension
+    with torch.no_grad():
+        output = model(input_tensor)['out'][0]
+    output_predictions = output.argmax(0).byte().cpu().numpy()
+    return output_predictions
+
+# Function to find the "best guess" for a bird if not detected
+def find_best_guess(segmentation_mask, unique_classes):
+    if len(unique_classes) > 1:
+        largest_object_class = None
+        largest_object_size = 0
+
+        for class_id in unique_classes:
+            if class_id == 0:  # Skip background
+                continue
+            # Calculate the size of the current class
+            object_size = np.sum(segmentation_mask == class_id)
+            if object_size > largest_object_size:
+                largest_object_size = object_size
+                largest_object_class = class_id
+
+        return largest_object_class
+    return None
+
+# Function to parse the custom metadata file format
+def parse_metadata_file(metadata_file):
+    metadata = {}
+    with open(metadata_file, 'r') as file:
+        for line in file:
+            if ':' in line:
+                key, value = line.strip().split(':', 1)
+                metadata[key.strip()] = value.strip()
+    return metadata
+
+# Function to get the scientific name from GBIF using the gbifID
+def get_scientific_name_from_gbif(gbif_id):
+    if gbif_id != 'Unknown':
+        try:
+            result = species.name_usage(key=gbif_id)
+            return result.get('scientificName', 'Unknown')
+        except Exception as e:
+            print(f"Error retrieving scientific name for GBIF ID {gbif_id}: {str(e)}")
+            return 'Unknown'
+    return 'Unknown'
+
+# Function to process images, remove birds, and identify plants
+def process_and_identify_images(input_dir, output_dir, results_csv):
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    results_list = []
+
+    for filename in os.listdir(input_dir):
+        if filename.endswith(('.jpg', '.jpeg', '.png')):
+            try:
+                image_path = os.path.join(input_dir, filename)
+                output_image_path = os.path.join(output_dir, filename)
+                metadata_file = image_path.replace('.jpg', '.txt').replace('.jpeg', '.txt').replace('.png', '.txt')
+
+                if not os.path.exists(metadata_file):
+                    print(f"Metadata file not found for {filename}, skipping.")
+                    continue
+
+                # Parse the custom metadata file
+                metadata = parse_metadata_file(metadata_file)
+
+                # Extract necessary fields from metadata
+                scientific_name = metadata.get('scientificName', 'Unknown')
+                species_key = metadata.get('speciesKey', 'Unknown')
+                decimal_latitude = metadata.get('decimalLatitude', 'Unknown')
+                decimal_longitude = metadata.get('decimalLongitude', 'Unknown')
+                coordinate_uncertainty = metadata.get('coordinateUncertaintyInMeters', 'Unknown')
+                continent = metadata.get('continent', 'Unknown')
+                state_province = metadata.get('stateProvince', 'Unknown')
+                year = metadata.get('year', 'Unknown')
+                month = metadata.get('month', 'Unknown')
+                day = metadata.get('day', 'Unknown')
+                event_date = metadata.get('eventDate', 'Unknown')
+
+                # Load the input image
+                image = Image.open(image_path)
+                image_np = np.array(image)
+
+                # Perform segmentation
+                segmentation_mask = segment_image(image)
+
+                # Identify bird in the image
+                unique_classes = np.unique(segmentation_mask)
+                bird_class_index = COCO_CLASSES.index('bird') if 'bird' in COCO_CLASSES else None
+
+                if bird_class_index is not None and bird_class_index in unique_classes:
+                    bird_mask = (segmentation_mask == bird_class_index).astype(np.uint8) * 255
+                    print(f"Bird detected in {filename}.")
+                else:
+                    print(f"No bird detected in {filename}. Making a best guess.")
+                    best_guess_class = find_best_guess(segmentation_mask, unique_classes)
+                    if best_guess_class is not None:
+                        bird_mask = (segmentation_mask == best_guess_class).astype(np.uint8) * 255
+                        print(f"Best guess for bird class in {filename}: {COCO_CLASSES[best_guess_class]}")
+                    else:
+                        print(f"Could not make a guess for {filename}, skipping.")
+                        continue
+
+                # Inpaint the image to remove the bird (or best guess)
+                inpainted_image = cv2.inpaint(image_np, bird_mask, inpaintRadius=3, flags=cv2.INPAINT_TELEA)
+                cv2.imwrite(output_image_path, cv2.cvtColor(inpainted_image, cv2.COLOR_RGB2BGR))
+
+                # Send the inpainted image to Pl@ntNet API for plant identification
+                with open(output_image_path, 'rb') as image_data:
+                    files = [('images', (filename, image_data))]
+                    data = {'organs': ['flower']}  # Adjust organs as necessary
+                    response = requests.post(api_endpoint, files=files, data=data)
+
+                    if response.status_code == 200:
+                        json_result = response.json()
+                        best_match = json_result.get('results', [{}])[0]
+                        species_name = best_match.get('species', {}).get('scientificNameWithoutAuthor', 'Unknown')
+                        gbif_id = best_match.get('gbif', {}).get('id', 'Unknown')
+                        score = best_match.get('score', 0) * 100
+
+                        # Retrieve the scientific name from GBIF if needed
+                        if species_name == 'Unknown' and gbif_id != 'Unknown':
+                            species_name = get_scientific_name_from_gbif(gbif_id)
+                        # Add result to list including metadata and image file name
+                        result = {
+                            'imageFileName': filename,
+                            'gbifIDPlant': gbif_id,
+                            'speciesPlant': species_name,
+                            'scorePlant': score,
+                            'scientificNameBird': scientific_name,
+                            'gbifIDBird': species_key,
+                            'decimalLatitude': decimal_latitude,
+                            'decimalLongitude': decimal_longitude,
+                            'coordinateUncertaintyInMeters': coordinate_uncertainty,
+                            'continent': continent,
+                            'stateProvince': state_province,
+                            'year': year,
+                            'month': month,
+                            'day': day,
+                            'eventDate': event_date
+                        }
+                        results_list.append(result)
+                        print(f"Processed {filename}: {species_name} ({score:.2f}%)")
+            except Exception as e:
+                print(f"Error processing {filename}: {str(e)}")
+
+    # Convert results to a DataFrame and save as CSV
+    results_df = pd.DataFrame(results_list)
+    results_df.to_csv(results_csv, index=False)
+    print(f"Processing completed. Results saved to {results_csv}")
+
+
+# Directory paths
+input_directory = './images/test2/'  # Replace with your input directory containing images
+output_directory = './imagesNoBirds/CalpteAnna/'  # Replace with your output directory to save processed images
+results_csv = './identified_plantsCalpteAnna2.csv'  # Path to save the CSV results
+
+# Process all images in the directory and save results
+process_and_identify_images(input_directory, output_directory, results_csv)