import numpy as np from PIL import Image, ImageDraw import matplotlib.pyplot as plt polygon_inside = ['1624703953738', '1677843764871', '1684906005406', '1613456188258', '1612354256326', '1614852918378', '1620123908548', '1613365388360', '1612505885674', '1706336250255', '1613478767931', '1614838600285', '1613388991566', '1614762710873', '1711771596326', '1698477566058', '1613193157318', '1614854390976', '1612510162348', '1620191668320', '1621423920299', '1620194317180', '1704953689234', '1613718312299', '1708576356873', '1612769371940', '1620905725978', '1614838400946', '1613190872776', '1613626199423', '1614847953023', '1709112826214', '1615198187105', '1614083311003', '1612605617536', '1614083435075', '1620630188476', '1614159405784', '1620304290692', '1614922517659', '1613111802015', '1614227970144', '1620300938140', '1624882679508', '1614313349704', '1675930368286', '1614142920947', '1614763981694', '1614756881264', '1613195278584', '1624253335033', '1613970013375', '1614849073055', '1615026209073', '1612945767997', '1623912622957', '1611824857184', '1613452777862', '1613024264523', '1624705332258', '1620625798021', '1613805095432', '1614583910642', '1620710869368', '1612931099881', '1611831332781', '1621057609262', '1621338899370', '1613472636074', '1612499030535', '1613128765644', '1612506267698', '1611913320519', '1613195436926', '1612508080465', '1675747639165', '1676529494047', '1613733804099', '1620300381828', '1615200483555', '1675054638861', '1614764748811', '1621055714032', '1613020647492', '1613041148421', '1614314146016', '1614947266743', '1620710081832', '1676092185201', '1614151414246', '1674629069526', '1612336946571', '1611812833957', '1613022788592', '1615201234159', '1621317621681', '1613370839768', '1613805627222', '1697515942343', '1613639066060', '1621411835485', '1620710301521', '1615183467273', '1620714424962', '1622271746930', '1612348658985', '1677665562738', '1620975257262', '1620214384400', '1622183153675', '1614581172645', '1623990371841', '1614579162993', '1620631638556', '1674552289467', '1676444867449', '1620125953167', '1612436341586', '1615183710327', '1622272568267', '1711713693259', '1623730462323', '1711798669281', '1706340026114', '1613373661230', '1615007888207', '1620190173834', '1613456020677', '1624508211578', '1624704506421', '1613475339946', '1612847261783', '1614426642009', '1612520317069', '1711603544575', '1613215660690', '1619612491198', '1674631989366', '1615023852152', '1679740521306', '1620385444112', '1612939457530', '1613726334558', '1674794172481', '1612507455247', '1621073481761', '1709615178320', '1624680226822', '1676267296128', '1614065081380', '1613190646052', '1615187655534', '1675481880024', '1612170970924', '1613019543807', '1620715769443', '1613714119760', '1621411250298', '1700808347231', '1620299043950', '1613992002312', '1613106406215', '1612865031834', '1620102006840', '1613363876496', '1613365769097', '1613021697258', '1674457829808', '1675930428752', '1613627711456', '1614341731948', '1620718134073', '1689750218000', '1613647891476', '1614939283152', '1614595796101', '1613817324112', '1622459306802', '1614759123936', '1624882477124', '1613112270229', '1613190260264', '1704954332838', '1676357661070', '1620298602405', '1676455038371', '1704951766925', '1675417503061', '1612065785634', '1624337464217', '1613720738687', '1619757291352', '1614240115068', '1612418897820', '1623840549803', '1612511204847', '1620968051865', '1613804894809', '1623840981583', '1613453794730', '1620194391561', '1621339059251', '1612585894867', '1614237957666', '1614596602887', '1620904440159', '1698477661861', '1614401357927', '1611832512795', '1613024758259', '1613729264412', '1614235375544', '1613813310597', '1674456394378', '1677910583988', '1614148942204', '1619763820132', '1620883572771', '1614162658878', '1678426503365', '1620127620684', '1711079065249', '1612856718499', '1613387155825', '1613970766418', '1677646753707', '1620890353585', '1621484391239', '1613734283619', '1620360901304', '1678169101192', '1622458778691', '1622438937398', '1711686349604', '1613216659312', '1612603872764', '1624000066258', '1614255056341', '1613636243782', '1613114028575', '1621252770806', '1613994043603', '1612171032101', '1620879428334', '1614843448424', '1611981164281', '1676095946695', '1676545888570', '1612933607572', '1620034631531', '1612867436149', '1614922430715', '1615189824672', '1625031627515', '1614754323597', '1612762346027', '1613020408610', '1620300144553', '1614599770867', '1674629223576', '1680327064249', '1614322067010', '1622439947901', '1675481909438', '1624362483924', '1675744640742', '1615013058288', '1619845271568', '1612251685659', '1620105438989', '1620716089113', '1619754207861', '1698477535095', '1613713539444', '1619585241753', '1674639709355', '1621060111039', '1711688973281', '1614592720632', '1612434538962', '1621230092601', '1613815145229', '1706160646733', '1614332941778', '1613456902075', '1614854128599', '1614058730895', '1614660168650', '1621071091914', '1613623021869', '1614942825486', '1675747575351', '1613215268784', '1612176257078', '1624096601707', '1675747545618', '1612869481411', '1612757727811', '1613627614569', '1615026317829', '1614764363180', '1620276782516', '1679892592254', '1621250248474', '1620038422842', '1613452316781', '1619505105941', '1614228858271', '1614753781504', '1622171913459', '1709011515653', '1613623896162', '1706173823085', '1613454391044', '1613799167778', '1612608692503', '1619758801835', '1612333017906', '1615028032974', '1619674737838', '1621072914084', '1620278478704', '1614062013677', '1621236398837', '1712900326332', '1623821893983', '1612352299545', '1612003945068', '1675398835486', '1706350943763', '1621057351332', '1613727316156', '1621063678446', '1613213428385', '1614062868146', '1621058067952', '1620387844569', '1620902007076', '1674557499228', '1620209993385', '1621311336567', '1621319746328', '1614833388362', '1706178710146', '1613543808014', '1710919159704', '1612174105227', '1613974265461', '1621335171763', '1621487396019', '1613535089528', '1615012149530', '1620192010478', '1619518616455', '1615182369141', '1619756550905', '1612948938270', '1613721807245', '1676436015494', '1620646288252', '1676375580938', '1614330354186', '1677652990725', '1679634096255', '1624272836417', '1612506666007', '1613717660662', '1620277294995', '1620388131197', '1614859597565', '1620628286155', '1621494300599', '1674448780928', '1621312959231', '1674625533569', '1612350582836', '1621247455938', '1622520518838', '1620628909986', '1706173722030', '1698477724887', '1614677638233', '1622171241755', '1613375497223', '1611902025290', '1619757968741', '1613111083562', '1612353226144', '1614593761220', '1620642780254', '1676618786988', '1613632895535', '1709621726387', '1675223820647', '1612761477148', '1615010508652', '1621049488295', '1704949932103', '1711713032207', '1619519554786', '1615199805496', '1674630415274', '1678445520558', '1620886061222', '1614746546096', '1675136806918', '1614413340979', '1674457900066', '1611988204935', '1622177416320', '1624081354849', '1613626423666', '1614055891756', '1614748540262', '1674715094700', '1613038094584', '1613023529878', '1621321226822', '1613625780836', '1620369985596', '1614233892281', '1614314470271', '1614928309367', '1612507639389', '1620883080462', '1614160511257', '1675149928792', '1612432391810', '1613713628690', '1613970146312', '1614341195351', '1614833923729', '1709620810441', '1613475325998', '1620365982114', '1615187714773', '1613467987727', '1620731918548', '1676093735499', '1613626090182', '1676611157957', '1613385818744', '1675930142770', '1613971442380', '1675766059453', '1612245003866', '1621492275469', '1706178907317', '1613628163673', '1620364875344', '1674628320425', '1614165606146', '1611836037897', '1710928832721', '1622112943567', '1619765235247', '1623906548938', '1614771649640', 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'1613368017665', '1620188355850', '1621243600649', '1675315469543', '1614226205690', '1620629570296', '1613471944601', '1620716518578', '1620797331279', '1676703743218', '1613994636876', '1620121655998', '1614674850804', '1621248579575', '1612439188478', '1676106157047', '1623838894452', '1615179808744', '1706176773069', '1613110346197', '1675335605692'] text_to_remove = "1613971589362" for item in polygon_inside[:]: if text_to_remove in item: polygon_inside.remove(item) polygon_inside = sorted(polygon_inside) ######################oil_palm_detection########### # Commented out IPython magic to ensure Python compatibility. # Install Ultralytics library # %pip install ultralytics import os import cv2 #from torchvision.models.detection import fasterrcnn_resnet50_fpn from ultralytics import YOLO ROOT_DIR = "satellite_images" # Define the directory where you want to save the images OUTPUT_DIR = f'{ROOT_DIR}/detections/masked_images/' # Path to the trained model file model_file = "Downloads/yolov8_palm_detection_best.pt" TESTING_DIR = f"{ROOT_DIR}/masked/" confidence_thres = 0.15 iou_thres = 0.20 # IoU is a measure of the overlap between two bounding boxes. #It's calculated by dividing the area of intersection between the bounding boxes by the area of their union. #IoU = Area of Intersection / Area of Union #The IoU value ranges from 0 to 1, where 0 indicates no overlap between the bounding boxes, and 1 indicates complete overlap. os.makedirs(OUTPUT_DIR, exist_ok=True) # Load the trained model model = YOLO(model_file) # List all files in the folder #image_files = os.listdir(TESTING_DIR) tree_count = {} # Process each image file for polygon_id in polygon_inside[:10]: image_path = f"{ROOT_DIR}/masked/{polygon_id}.png" # Read the image frame = cv2.imread(image_path) #results = model(frame)[0] results = model(frame, conf=confidence_thres, iou=iou_thres, max_det=1000, save=True, save_txt=True, save_json=True)[0] detect_count = len(results.boxes) polygon_id = image_file.split('.')[0] tree_count[polygon_id] = detect_count for result in results.boxes.data.tolist(): x1, y1, x2, y2, score, class_id = result # Calculate center coordinates of the bounding box center_x = int((x1 + x2) / 2) center_y = int((y1 + y2) / 2) # Draw circle at the center of the bounding box cv2.circle(frame, (center_x, center_y), radius=5, color=(0, 255, 255), thickness=-1) # -1 thickness fills the circle detect_count = str(detect_count) label = f'Palm tree count:{detect_count}' # Save the annotated image output_path = os.path.join(OUTPUT_DIR, f'{os.path.splitext(image_file)[0]}_object_detection.png') cv2.imwrite(output_path, frame) # Release resources #cv2.destroyAllWindows() #####################Read the .json file with polygon_data and calculate tree density######## import json # Step 1: Read the JSON file with open('Downloads/polygon_data_all.json', 'r') as file: data = json.load(file) # Conversion factors sq_m_to_ha = 1 / 10000 # 1 sq m = 0.0001 hectares sq_m_to_acre = 1 / 4046.86 # 1 sq m = 0.000247105 acres # Step 2: Merge with tree_count dictionary for key, value in data.items(): if key in polygon_inside[:10]: print(key) if key in tree_count: value['total_tree_count'] = tree_count[key] area_sq_m = value['field_area'] value['area_hectares'] = round(area_sq_m * sq_m_to_ha,2) value['area_acres'] = round(area_sq_m * sq_m_to_acre,2) value['tree_count_per_hectare'] = round((value['total_tree_count'] / value['area_hectares']),2) value['tree_count_per_acre'] = round((value['total_tree_count'] / value['area_acres']),2) del value['polygon_coordinates'] del value['fieldmaxlat'] del value['fieldminlat'] del value['fieldmaxlong'] del value['fieldminlong'] # Define the file path file_path = 'Downloads/field_data_with_palm_tree_count.json' # Ensure the directory exists os.makedirs(os.path.dirname(file_path), exist_ok=True) # Write data to JSON file with open(file_path, 'w') as file: json.dump(data, file, indent=4) ################################################################################################# ###############write the field details on the image############################################# import cv2 import json # Example list of field_id values polygon_id_list = polygon_inside[:10] polygon_id_list = sorted(polygon_id_list) # Process each field_id for polygon_id in polygon_id_list: # 1. Open the .png image ROOT_DIR = "satellite_images" image_path = f'{ROOT_DIR}/detections/masked_images/{polygon_id}_object_detection.png' img = cv2.imread(image_path) if img is not None: # 2. Read the .json file json_file_path = '/Downloads/field_data_with_palm_tree_count.json' # Replace with your JSON file path with open(json_file_path) as f: data = json.load(f) #print(data) # 3. Extract data based on field_id if polygon_id in data.keys(): #print(polygon_id) tree_count = data[polygon_id]['total_tree_count'] # Example key, adjust as per your JSON structure field_id = data[polygon_id]['fieldID'] farmer_id = str(data[polygon_id]['farmerID']) land_id = data[polygon_id]['landID'] tree_count_hect = data[polygon_id]['tree_count_per_hectare'] tree_count_acre = data[polygon_id]['tree_count_per_acre'] # 4. Write values on the image (example: put text on image) text = f''' FarmerCode: {farmer_id} \nLandID: {land_id} \nTree Count: {tree_count} ''' # Position to start writing text x, y = 50,50 # Calculate text size to determine the rectangle size text_lines = text.strip().split('\n') max_text_width = 0 total_text_height = 0 # Get maximum width and total height of all lines for line in text_lines: text_size, _ = cv2.getTextSize(line, font, font_scale, thickness) text_width, text_height = text_size max_text_width = max(max_text_width, text_width) total_text_height += text_height + 5 # Add some spacing between lines # Create rectangle coordinates rect_x1, rect_y1 = x, y - total_text_height rect_x2, rect_y2 = x + max_text_width + 130, y + 90 # Adjust as necessary # Draw rectangle with semi-transparent white color (opacity 0.7) overlay = img.copy() cv2.rectangle(overlay, (rect_x1, rect_y1), (rect_x2, rect_y2), (255, 255, 255), cv2.FILLED) cv2.addWeighted(overlay, 0.7, img, 0.3, 0, img) # Split the multi-line text into lines and draw each line lines = text.strip().split('\n') for i, line in enumerate(lines): cv2.putText(img, line, (x, y + i*30), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255, 0, 0), 2) # 5. Save the modified image with a new name annotated_image_path = f'{ROOT_DIR}/detections/with_count/{polygon_id}_annotated.png' cv2.imwrite(annotated_image_path, img) ###############################################################################################